Economics of Structural and Technological Change

Economics of structural and technological change

Technology has long been seen as a path to economic growth. However, there is considerable debate about the exact nature of this relationship. Economics of Structural and Technological Change employs a wide range of theoretical and applied approaches to explore the concept of technological change. The book begins with a series of in-depth discussions of the economic analysis of technological change. The second section contains a discussion of theoretical models of technological change, focusing on issues such as time and innovation. The third section brings together a number of applied analyses of technological change and examines the effect of factors such as human resource constraints, patenting and science and technology indicators. Gilberto Antonelli is Professor of Economics at the University of Bologna and Director of IDSE-CNR, Milan. Nicola De Liso is a full time researcher at IDSE-CNR, Milan.

Industrial economic strategies for Europe Series editors: Patrizio Bianchi, Keith Cowling and Roger Sugden Europe is currently at a crucial stage in its economic, social and political development. This series addresses the challenges to European economic policy. It will explore the design of industrial economic strategies enabling European industries and regions to flourish and prosper as we begin the twenty-first century. Competitiveness, Subsidiarity and Industrial Policy Edited by Pat Devine, Yannis Katsoulacos and Roger Sugden Europe's Economic Challenge Analyses of Industrial Strategy and Agenda for the 1990s Edited by Patrizio Bianchi, Keith Cowling and Roger Sugden The Impact of Privatisation Ownership and corporate performance in the UK Stephen Martin and David Parker Economics of Structural and Technological Change Edited by Gilberto Antonelli and Nicola De Lisa Industrial Policies and Economic Integration Learning from European Experiences Patrizio Bianchi

Economies of structural and technological change Edited by Gilberto Antonelli and Nicola De Liso

London and New York

First published 1997 by Routledge 11 New Fetter Lane, London EC4P 4EE This edition published in the Taylor & Francis e-Library, 2005. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” Simultaneously published in the USA and Canada by Routledge 29 West 35th Street, New York, NY 10001 © 1997 Gilberto Antonelli and Nicola De Liso All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalogue record for this book has been requested ISBN 0-203-02072-3 Master e-book ISBN

ISBN 0-203-20443-3 (Adobe eReader Format) ISBN 0-415-16238-6 (Print Edition)

Contents

1

List of figures

vii

List of tables

viii

List of contributors

ix

Foreword

x

Introduction: An appraisal of the economic analysis of technological change: The path to the last decade Gilberto Antonelli and Nicola De Liso

1

Part I Research paths in the economic analysis of technological change 2

Economic evolution and technology strategy John Stanley Metcalfe

49

3

Technical change and labour displacement: Some comments on recent models of technological unemployment Paolo Pini

61

4

Broad production factors and technological systems Gilberto Antonelli

84

5

Different dimensions in the analysis of technological change Giovanni Pegoretti

105

Part II Theoretical models of technological change 6

On diffusion and the process of technological change John Stanley Metcalfe

120

7

A note on the diffusion of innovation Luigi Filippini

142

8

Technological and organizational innovations as problemsolving activities Massimo Egidi

152

9

Time-saving and innovative processes Paolo Mario Piacentini

168

vi

Part III Applied analyses of technological change 10

Black boxes and variety in the evolution of technologies Pier Paolo Saviotti

184

11

Human resources constraints in technological innovation processes: theoretical considerations and empirical evidence Riccardo Leoni

213

12

Firms, uncertainty and innovation policy: Some spatial considerations in an evolutionary framework Mario Agostino Maggioni

229

13

Patenting abroad and international technology transfer Antonio Sassu and Raffaele Paci

258

14

Science and technology indicators: The state of the art and prospects for the future Giorgio Sirilli

282

Index

309

Figures

6.1 The equilibrium niche 131 6.2 Competition between technologies and transition to the equilibrium 134 niche 6.3 The substitution curve 135 7.1 The process of technological substitution 145 9.1 Time and factor savings as movements along and off the cost curve 170 9.2 Optimal lot sizing 172 9.3 Switching costs graph 174 9.4 The ex ante revenue function 176 9.5 The cumulated cost function 176 9.6 Ex ante and ex post revenue functions 177 9.7 The optimal period of investment 178 10.1 A product as two sets of characteristics 187 10.2 A technological population in a bidimensional X space 188 10.3 The change in a technological population between times t1 and t2 in a 190 bidimensional X space 10.4 Pure substitution in terms of a twin characteristics framework 190 10.5 Twin characteristics representation of inter-technology specialization (1) 191 10.6 Twin characteristics representation of inter-technology specialization (2) 191 10.7 Partial substitution in terms of a twin characteristics framework 192 10.8 Twin characteristics representation of the emergence of a completely 192 new product 10.9 Density change in a technological population 209 12.1 The local environment 234 12.2 The network 239 12.3 The economic neural network 243 12.4 The neural cell 244 12.5 Hierarchy vs. network in the nine agents’ case 245 12.6 The evolution of organizations 248 12.7 The science park 252

Tables

10.1 Examples of different elementary mechanisms and their contribution to output variety 11.1 Distribution of establishments according to source of R&D of product innovations, 1982–1987 11.2 Presence of R&D capacity within establishments 11.3 Relationship between R&D capacity within establishments and capacity acquired in the market 11.4 Relationship between R&D capacity within establishment and presence of autonomous research department 11.5 Employees in design, development and research activities 11.6 Establishments engaged in some process of ‘training’, 1984–1987 11.7 Have you limited or delayed product innovation due to shortage of qualified human resources? 11.8 Percentage of ‘technicians’ employed, 1981 and 1987 12.1 Actors, activities and resources of a science park 13.1 Patents granted by publication, and application countries, 1972–1984 13.2 Foreign patents, 1972–1974 and 1982–1984 13.3 Italy: propensity to patent abroad, 1973–1984 13.4 Germany: propensity to patent abroad, 1973–1984 13.5 France: propensity to patent abroad, 1973–1984 13.6 Great Britain: propensity to patent abroad, 1973–1984 13.7 United States: propensity to patent abroad, 1973–1984 13.8 Italy: propensity of multinational firms to patent abroad, 1973–1975 and 1980–1982 13.9 Italian patenting activity in foreign countries 13.10 Italian patenting in Germany 13.11 Italian patenting in France 13.12 Italian patenting in Great Britain 13.13 Italian patenting in the United States

207 217 218 218 220 220 222 225 226 250 263 263 266 267 267 268 270 272 274 276 277 278 278

List of contributors

Gilberto Antonelli, IDSE-CNR, via A.M. Ampère 56, 20131 Milan, Italy, and University of Bologna, Strada Maggiore 45, 40125 Bologna, Italy, e-mail: [email protected]. Nicola De Liso, IDSE-CNR, via A.M. Ampère 56, 20131 Milan, Italy. e-mail: [email protected]. Massimo Egidi, University of Trento, via Inama 1, 38100 Trento, Italy. Luigi Filippini, Catholic University of Milan, via Necchi 5, 20123 Milan, Italy. Riccardo Leoni, University of Bergamo, Piazza Rosate 2, 24100 Bergamo, Italy. Mario A. Maggioni, IDSE-CNR, via A.M. Ampère 56, 20131 Milan, Italy. John Stanley Metcalfe, School of Economic Studies, CRIC and PREST, University of Manchester, Oxford Road, Manchester Ml3 9PL, UK. Raffaele Paci, University of Cagliari, via Santa Margherita 4, 09100 Cagliari, Italy. Giovanni Pegoretti, University of Trento, via Inama 1, 38100 Trento, Italy. Paolo Mario Piacentini, University of Cagliari, via Santa Margherita 4, 09100 Cagliari, Italy. Paolo Pini, University of Udine, via Tomandini 30/a, 35100 Udine, Italy. Alberto Quadrio Curzio, Catholic Univerisity of Milan, via Necchi 5, 20123 Milan, Italy. Antonio Sassu, University of Cagliari, via Santa Margherita 4, 09100 Cagliari, Italy. Pier Paolo Saviotti, INRA-SERD, Université Pierre Mendés-France, F-38040 Grenoble Cedex 9, France Giorgio Sirilli, ISRDS-CNR, via Cesare De Lollis 12, 00185 Rome, Italy.

Foreword Alberto Quadrio Curzio

The evolution of the world economy during the last few decades has made clear that there exists the need for understanding and modelling the economy by means of those analytical tools which explicitly refer to long-run structural economic dynamics. Such an approach seems to be the most appropriate to understand historical as well as current changes, in which economies follow an irregular pattern as sectors tend to move at different speeds as a consequence of technical innovation and resources scarcity. Methodologies related to economic dynamics are particularly useful when policy-making is to be based on theoretical analyses in order to affect structural relationships within the economy, going beyond short-run policies. Applied analyses have sprung out continuously, particularly after the Second World War, trying to tackle and disentangle the determinants of growth. Recently a new wave of studies, which has stimulated the convergence-divergence debate, has emerged. The present state of the studies concerned with economic dynamics, however, is rather fragmented and heterogeneous, the coexisting themes and theories being so different from each other. For instance, the problem of long-run unemployment in the more advanced countries is at present at the centre of many different interpretations, stressing one or more aspects or ‘truths’ such as the ongoing technological revolutions, institutional rigidities, international competition with particular emphasis on the emerging ‘tigers’, the underlying microeconomic processes, and so on. The same statements, to consider another example, hold mutatis mutandis as far as convergence between countries is concerned. New analytical tools and theories, as in the case of national systems of innovation or the renewed emphasis on local production systems—which come together with analyses of globalization—have been developed. Many difficulties are on the way, and the point of view we adopt a priori is, of course, not neutral. The very definition of technical progress can be less clear than it seems at first sight, and it is not a mere coincidence that economists often prefer the use of the term ‘technological change’. When non-produced means of production are used, the order of efficiency as well can be looked at from two

xi

different perspectives, the first physical, the second related to the pricedistribution mechanisms; the two orders, however interrelated, do not coincide. There emerges a variety of theories and themes which, in itself, constitutes a richness which, to become more productive, needs appropriate arenas for debate. The same holds in order to guarantee more effectiveness if policy-making is to be improved from these studies. In the Italian context, the Research Institute on the Dynamics of Economic Systems of the National Research Council (IDSE-CNR)—to which the next lines are devoted—aimed, and aims, to be such a place. The first line of research of IDSE, in fact, has consisted of the promotion of studies concerned with structural economic dynamics, together with its diffusion and possibility of use for policy. Applied and theoretical studies have been carried out. A second line of research concentrates on studies related to structural economic dynamics and institutional change in Italy, both at the national and local level. Networking with other Italian and European institutions, by means of joint research, organization of seminars and exchange of researchers, constitutes a standard approach at IDSE. Finally, IDSE carries out consultancy activities and supplies indications for policy makers. The chapters in this volume reflect part of the activities carried out at IDSE; they were first presented during an international conference organized at the CNR Research Area of Milan, and then re-elaborated according to the discussion that emerged. When the conference was organised the director of IDSE was Professor Carlo Beretta to whom—also on behalf of the contributors of this book and of the present director, Professor Gilberto Antonelli—I wish to acknowledge my gratitude.

1 Introduction: An appraisal of the economic analysis of technological change The path to the last decade Gilberto Antonelli and Nicola De Lisa1

1 TECHNOLOGICAL CHANGE AND POLICY-MAKING 1.1 The relevance of technological change: a background to policy debate Technological change has undoubtedly been one of the most important features of the economic history of the world at least since the occurrence of the first Industrial Revolution in the last quarter of the eighteenth century in England. Since then, technical change has shaped the development of economies through the continuous occurrence of both major and minor changes. Technical change affects many aspects of both economic and social activity. It influences production in qualitative and quantitative ways, and affects employment, international trade, investment and consumption patterns. These widespread effects of technical change are such as to warrant the examination of the interaction of technical change, market forms and overall economic structure. Given the quantitative and qualitative importance of technological change, it is easy to understand why during the last thirty years there has been an escalation of studies concerned with it. Techniques of production now last for shorter periods; changes, however small, follow one another, and the need to capture the essence of the processes connected with technological change is felt more and more urgently. Both theoretical and empirical developments in the 1980s represented a turning point for economic analysis. It seems to us that these comments highlight fairly well the fact that any analysis of technological change is influenced by policy implications, which, as such, induce action shaped by political interests and values. Many questions can arise here. The first we consider, given its social relevance and capability of shedding light on the chains activated by policies, is unemployment. The early 1990s have

2 GILBERTO ANTONELLI AND NICOLA DE LISO

been characterized by a general concern as structural long-term unemployment seems to persist and increase, and when signs of growth are visible we speak of jobless recovery. There thus emerges the question: what is the socially tolerable level of unemployment? Should governments intervene, and, if they should, how? The main alternatives in the policy arena are the following: (a) short-term flexibility as a cure mainly of labour market rigidities; (b) investment demand expansion combined with fiscal policy revitalization; (c) stimulation of new market opportunities and product innovation; (d) international competitiveness and strategic trade; (e) long-term flexibility based on the development of individual, social and technological capabilities. However, each of these solutions has a draw-back, taking also into account the composite nature of unemployment: from the effectiveness of the policy prescription to the need of increasing taxation, the risk of increasing inflation, and so on. These last points make it clear that whenever policy action is undertaken there occurs a series of reactions in the economic system, not all of which are desirable. And economic analysis should be engaged in increasing the degree of awareness of this, showing the likely scenarios in which complementarities and feedbacks share the same importance as clear-cut alternatives. Other relevant questions concern the relationship between market structure and innovation, the existence and validity of economic regularities, such as the Kaldor-Verdoorn’s law, the rate of adoption and diffusion and ways of influencing it, and so on. All of these topics have been characterized by theoretical, empirical and political debates, sometimes leading to opposite results according to the ‘lenses’, i.e. school of thought, used. The aim in bringing the chapters in this volume together is to provide a timely non-sectarian picture of the main contemporary schools of thought and to provide a forum in which the concepts and theories can be compared. The focus will be on the basic elements which shape technological change; that is, rate, direction and diffusion. Such an analysis constitutes a background to policy debate in that its theoretical foundations are here laid. Whilst the main interest of the book is the economic analysis of technological change in the last decade, in this introduction we also try to summarize some of the basic ideas concerning technical change which current economic analysis has inherited from the works of earlier periods. The analysis distinguishes schools of thought and concepts. Of course this short introduction does not have pretensions to completeness; it simply aims to introduce a set of ideas that should be clarified and developed by reading the chapters in this volume.2 Finally a ‘bias’ should be declared from the start. In this introduction, as well as in the selection of chapters collected in the volume, we are led to attach more importance to the approaches conceiving of technological innovation as ‘interacting learning’ than the ones dealing with it as mere ‘information’.

INTRODUCTION 3

1.2 Some considerations on policy The analytical framework recalled above and developed in the text lends itself to policy considerations that we sketch in this section. The first consideration relates to unemployment. Several studies have stressed important changes in the dynamic relationship between the volume of production and the level of employment. If there is much empirical evidence for a strong direct causal link between production and employment growth when a decrease in production occurs, the link does not appear in phases in which production increases. While the regime of economic growth for the 1950s and 1960s was characterized by sustained production growth, associated with high intensity of employment and sustained product innovation, the regime for the 1970s and 1980s seems marked by low product growth with low intensity of employment and sustained process innovation. The employment—unemployment dynamics cannot be explained only by short-run factors or analysed through traditional conceptual schemes of equilibrium, or even partial equilibrium. Similarly, short-run macroeconomic policies cannot alone be a sufficient response to the employment-unemployment problem. Low employment growth and the increase of unemployment in OECD, and in particular in European countries, are the result of long-run structural changes. The following points deserve attention. 1 Co-ordination between short- and long-run policies. Often in official documents we can find discussions based on the following reasoning, more or less explicitly stated. The inadequacy of existing macroeconomic models or a conscious division of labour between macroeconomic and stabilization policies on the one hand, and sectorial analyses and structural policies, on the other, provide the rationale for a clear-cut separation between structural policies and macroeconomic stabilization policies. This division of labour is often correlated to the span of different administrative and political jurisdictions. 2 Competition policy as a crucial component of industrial policy. New authorities monitoring the existence of real competition have been established even in countries with no previous traditions. However, amazingly enough, very little empirical work has been done on the effects of competition policies on the functioning of labour markets.3 The economic profession, as well as policy-makers, should be more concerned with this topic. 3 The evaluation policy. A similar complaint applies to policies for the evaluation of R&D, on the one hand, and of environmental impact, on the other. The interactions between demand side and supply side are often overruled in both cases and the same is true with regard to the impact on the labour market, unemployment and natural and environmental resources.

4 GILBERTO ANTONELLI AND NICOLA DE LISO

4 Variety and diversity. Variety is certainly an important asset which, however, must be channelled through two polar needs, i.e. systemic coherence and regional specificities and autonomies. The economic constitution and the economic structure are containers of this polarization to be reaggregated within a specific institutional set up. 5 Solidarity. Solidarity could also assume a crucial economic role at a supranational level as a means to promote human resources skills, a primary asset, even more so, in modern economies, and to tone down the determinants of both the divergence in labour markets as well as instability in prices, labour costs and institutions. We cannot assume that compensation effects at a national level, on which the largest part of economic discussion has been concentrated, will operate with the same intensity at a supranational level with increasing variety in local systems of production. 6 Human resources and integration. If a strategy is to be found with a potential of integration similar to that implemented by the founders of the European Community in the 1950s then, related to raw materials and energy, we should focus on human resources, considered in their regional environment —and in this respect the Maastricht agreement is severely lacking. By way of conclusion, let us say that the ‘knowledge-based economy’ needs new policy prescriptions rooted in a deep revisions of our theoretical background. 2 BASIC CLASSICAL CONCEPTS 2.1 Smith's contribution Adam Smith published his Inquiry into the Nature and Causes of the Wealth of Nations in 1776 and in it he analysed ‘technical change’ under the heading of the division of labour. This was the first systematic of production and methods of production. According to Smith’s theory, what we today call technical change is an endogenous phenomenon with respect to the economy, and in the long run increasing returns prevail over decreasing returns. Furthermore, we might note that the division of labour is the structural means through which returns to scale can be modified. The principles at work are: (1) the increase in dexterity of every worker; (2) the saving of the time which is commonly lost in passing from one type of work to another; (3) the invention of a great number of machines which facilitate and reduce the amount of labour, and enable one man to do the work of many.

INTRODUCTION 5

1 Dexterity. Today we refer to this principle as learning-by-doing; however it was clearly expressed by Smith two centuries ago. The article by Arrow (1962b) can be considered a formalized improvement of the Smithian principle of dexterity. Smith wrote that: by reducing every man business to some one simple operation, and by making this operation the sole employment of his life, necessarily increases very much the dexterity of the workman. (Smith, [1776] 1976, p. 18) Smith was mainly concerned here with the manufacturing division of labour. However, the same principles apply when the other forms of division of labour occur (division of professions and division of production)4. The principle of learning-by-doing was stated also by Cesare Beccaria in his Elementi di Economia Pubblica (Elements of Public Economy) in 1771 where he wrote that: everybody knows by experience, that by applying one’s hand and mind always to the same kind of works and products, one obtains easier, more abundant and better results than if everyone, on his own, produced all and only those things that he needs. (Beccaria, [1771] 1958, pp. 387–388, our translation)5 2 The saving of time. The development of particular professions and trades generally implies a physical concentration of those activities, which saves time. Moreover, when the separation between different operations occurs inside the work process, there occurs a further saving of time. 3 The invention of machines. Smith considered mechanization as a by-product of the division of labour. To understand this statement we must remember that Smith was writing in the 1770s, so that, given the technology of the period, only simple operations could be mechanized. However, the development of technology at first, and of scientific technology in a second stage, rendered it possible to mechanize complex operations as well as whole phases and work processes. Another Smithian concept that needs to be emphasized is that the division of labour is limited by the extent of the market. To operate, the abovementioned principles need a market of sufficient size. However, the reverse is also true; that is, the extent of the market is limited by the extent of the division of labour. Finally, Smith propounded, well before Keynes, the thesis of proportionality between variations in investment and employment (Sylos Labini, 1969, p. 151). Two concluding remarks: first, Smith’s is a disequilibrium theory, at least when he speaks of the division of labour; second, it involves a theory of endogenous technical change.6

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2.2 The development of classical ideas The development of technology during the 1820s was very fast, and the analysis of the process of mechanization was quickly undertaken. In this context we can mention the works of, and debates between, Ricardo, McCulloch, Sismondi and Malthus. This debate was particularly interesting and intense, with McCulloch and Ricardo starting from opposed positions, and ending in opposed reversed positions.7 In the third edition of his Principles (1821) Ricardo added a new chapter, the 31st, examining the effects on employment of the introduction of new machinery in production processes. He concluded that the introduction of new machinery could actually be detrimental to workers. This has started an endless debate, still going on after 170 years.8 Less emphasized, but definitely central in the Ricardian analysis of technical progress, is the notion of relative scarcity. Technological innovation, through interdependent changes in processes, products and materials, can influence the scarcity of natural resources helping to ease the constraints on natural resource endowment. In Ricardo’s dynamic scheme the shortage of land (the prototype of natural resources) must be regarded as relative, for technical progress is capable to affect it. The natural resources constraint may slow growth down or even bring it to a halt in the absence of technical progress. In the Ricardian model of natural equilibrium, rent depends on the productivity of the land, wages are determined exogenously and profits are a residual share. Rent therefore arises and grows with scarcity, undergoing the effects of the dynamic interaction between this latter and technical progress (Quadrio Curzio and Antonelli, 1988, pp. 1–8). The labour of nature is paid, not because she does much, but because she does little. In proportion as she becomes niggardly in her gifts, she exacts a greater price for her work. Where she is munificently beneficent, she always works gratis. (Ricardo, [1821] 1951, p. 76) Scarcity has a profound influence on the distribution of total product, although it is not its only determinant. The prices of goods produced using natural resources also depend on their scarcity. This is true to the extent that scarcity entails the use of natural resources with gradually decreasing productivity, or the use of progressively more costly production methods using the same resource. Hence the prices of all produced goods are influenced by the dynamic interaction between scarcity and technical progress, given that all such goods directly or indirectly use natural resources as their means of production. The organization of production processes complies with the criterion of efficiency, according to which natural resources should be ordered and employed according to their

INTRODUCTION 7

decreasing levels of quality and productivity. This efficiency criterion depends on technical progress. The interdependence between basic materials, which are relatively intensive in natural resources, and other produced goods entails that the former are a component in productive processes, which acts as a constraint. A constraint, this, which therefore derives from an interdependence where the various means of production interact and display limited substitutability. Human resources play a similar role in this pattern if the subsistence hypothesis is abandoned. Two contributions made during the 1830s are also worthy of being recalled here: the works of Charles Babbage (1832) and Andrew Ure (1835). These two authors had a great influence over most of the students of both economics and technology who came after them. The main thrust of their work was concerned with the development of manufactures and the progresses of mechanization. Babbage, in particular, felt the need to reconsider the Smithian division of labour in the light of new technology. Moreover he pointed out the positive effects of the division of labour, not only in manual but also in mental work, using as an example the calculation of logarithms from 1 to 200,000. Babbage argued that the person charged with this task, a certain Mr Prony, after reading the first chapters of the Wealth of Nations decided to try to apply the principles of the division of labour. That is, Mr Prony decided ‘de mettre ses logarithmes en manufacture comme les épingles’ (Babbage, 1832, p. 193). This huge work was carried out by three different sections: in the first one pure mathematicians developed basic formulae; in the second section some more mathematicians converted these formulae into simpler ones, and produced some examples; in the third section many persons used these formulae to calculate the value of the logarithms. On this last class of mental workers Babbage writes: It is remarkable that nine-tenths of this class had no knowledge of arithmetic beyond the two first rules which they were thus called upon to exercise, and that these persons were usually found more correct in their calculation than those who possessed a more extensive knowledge of the subject. (Babbage, 1832, p. 195) Karl Marx analysed the changes which occurred in the process of production, reconsidering the works of Smith, Ricardo, Ure and Babbage, and gave new emphasis to the study of technology. According to Rosenberg: quite independently of whether Marx was right or wrong in his characterization of the future course of technological change and its social and economic ramifications, his formulation of the problem still deserves to be a starting point for any serious investigation of technology… (Rosenberg, 1982, p. 34)

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In Marx’s Capital the possible negative effects of technology on the labour force are stressed, both in quantitative and qualitative terms. He concentrated particularly on the aspects of production related to large-scale industry and its connected problems. He took as a starting point the labour process. He then considered manufactures as being basically characterized by two fundamental forms: heterogeneous and organic. Marx defined the latter as the perfected form of manufacture, in which articles: ‘go through connected phases of development, go step by step through a series of processes, like the wire in the manufacture of needles’ (Marx, [1867] 1976, p. 463). Then he analysed the introduction and development of machinery in manufactures. Marxian analysis is thus characterized by an extensive study of what we call today technical change, ranging from the organization of initially unmechanized labour processes to mechanized capitalist work processes.9 Moreover, we can even find in his work an early theory of technological diffusion on the supply side, when he studies how the single capitalist firm, in order to maximize individual profit, is induced to compete with the other firms by introducing new machines, while as a whole the increasing organic composition of capital decreases the overall rate of profit. 3 THE NEOCLASSICAL APPROACH 3.1 The transition from classical to neoclassical economics The first book of Marx’s Das Kapital was published in 1867; it was in the classical tradition and drew on all of the previous classical works, from Smith to J.S. Mill. A few years later, however, three works appeared, giving rise to a new approach to economics. These works were Carl Menger’s Grundsätze der Volkswirtschaftslehre, W. Stanley Jevons’s Theory of Political Economy, and Walras’s Eléments d’Économie Politique Pure. These analyses opened a new phase in economic theorizing in which exchange, consumption, optimization, and general equilibrium became the central concepts. At first the then so-called Marginalists were concerned with consumer theory (marginal utility); then, the principles of marginalism were, mutatis mutandis, applied to production10 as well. An attempt to reconcile and synthesize classical with neoclassical thought was made by Alfred Marshall in his Principles11 (1920). He was already aware of some of the problems implicit in the neoclassical theoretical construct. Moreover, he spent a good deal of his time studying the processes occurring in the real economy, which culminated in the publication, in 1919, of his other great work Industry and Trade. This book was conceived of as a study characterized by a special reference to the technical evolution of industry.12

INTRODUCTION 9

Besides including classical and neoclassical themes, Marshall’s works considered aspects which are still actively studied. First of all he brought time into economic analysis, and he tried to tackle problems relating to diminishing and increasing returns,13 monopolistic and competitive markets, inventive activity, industrial organization, and so on. In this respect, it is worth while to mention the concepts of internal and external economies, where the latter depend on ‘the general development of the industry’ and ‘result from the general progress of the industrial environment’ (Marshall, 1920, p. 266 and p. 441) and are therefore one of the ways through which technical progress emerges. We have noted that Marshall still influences contemporary economists, and in particular those dealing with technological innovation in industrial districts and regional development. 3.2 The definition of technical progress The need to tackle the issue of technical progress clearly emerged after a series of studies concerning economic growth which were conducted after the Second World War. Many countries, in fact, were experiencing long-run economic growth that could not be justified on a simple basis of quantitative growth of factors of production. There emerged a number of studies relating to what was then called ‘growth accounting’. These studies, based on production function models, aimed at distinguishing between output growth due to increases in quantities of factors of production and growth due to other unexplained determinants. What could not be explained by increases in the availability of factors of production, the so-called residual, was mainly attributed to technical progress. Many theoretical and statistical difficulties arose in undertaking the measurement of this residual, which boosted the debate on this issue.14 Before dealing with neoclassical forms of technical progress let us recall some basic concepts, often relevant also to other schools of thought. Central to the standard neoclassical theory is the price-taking firm, operating in markets characterized by perfect competition, in both commodity and factors markets. The firm has the task of organizing production on the basis of profit maximization. Maximization, in turn, implies costs minimization and the choice of optimal level output. The firm has a perfect knowledge of all the possible techniques existing in the system, and instantaneous and free diffusion of new knowledge is generally assumed; thus the firm has perfect information concerning inputs and outputs. Firms produce homogeneous products and, finally, all of the relevant markets, i.e. inputs and products, are in equilibrium. The neoclassical theory of production is based on the production function. The function is said to be well-behaved when it is continuous and smooth, so that it has positive first-order and negative second-order partial derivatives; that is, positive and decreasing marginal productivities of the single factor of

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production. By means of this general and abstract production function the neoclassical theory is capable of generating a whole theory of production. Two central concepts are returns to scale and elasticity of substitution. Returns to scale constitute an important aspect of any theory of production, because according to the kind of returns the economy evolves in different ways. The preferred form of production function is that showing constant returns to scale and homogeneity of degree one. The elasticity of substitution tells us how ‘easy’ the substitution between factors is. Solow’s article published in 1957 made the aggregate production function device famous. In this formulation technical change is simply a function of time and is thus considered as an exogenous phenomenon. Once we have specified the production function in this way, technical progress can be described as a shift of the isoquants towards the origin of axes. Shifts occur as time goes by. In this way, innovations considered are only process innovations. By making use of Solow’s tools, when we consider explicit production functions, we can distinguish movements along the function from shifts of the function itself—a kind of distinction that has been heavily criticized. If, under the assumption of constant returns to scale, we graph our function as q(= Q/L) against k(= K/L) (Solow, 1957, p. 313), we can distinguish increases in production due to increased mechanization—a growing K/L along the same function—from increases in production due to technical progress (greater production for a given capital-labour ratio that is not on the same production function). This form of technical progress has been referred to as disembodied, since such a change occurs independently of any change in inputs. It implies an instantaneous diffusion of innovation which, as it does not imply any change in either capital goods or labour, is sometimes referred to as ‘manna from heaven’. Another form of change specified in the neoclassical approach is the embodied technical change, defined in this way because improvements are embodied in new capital goods and (re)trained labour. In this case a change in inputs is assumed, but instantaneous diffusion of new techniques still holds. 3.3 Neutral technical progress In general, technical progress is modelled as an exogenous variable that is included in the production process through different specifications of the production function. There are three main models based on an aggregate production function (Bowman, 1980; Stoneman, 1983). The first model has two variants. (1.1) (1.2)

INTRODUCTION 11

In both variants C, which is defined ‘technical progress’ (Solow, 1957) or ‘advance in knowledge’ (Denison, 1967) is autonomous with respect to K and L. In (1.1) C is a pure residual, not a function of time, and is disembodied, independent of both K and L. This specification is the one employed by a large part of ‘growth accounting’. In (1.2), as already mentioned, technical progress can be described as a shift in the production function systematically associated with time, but is still disembodied. Therefore we can also write the equation (1.2) as: (1.2′) As a second step, we can allow for an interaction between technical progress and production factors to take place. This interaction influences the efficiency of these factors. Technical progress is thus ‘factor augmenting’; that is, the economy behaves as if an increase in factor endowment had taken place. In this case the aggregate production function can be written in general as: If A(t)=1, so that its rate of change is zero, i.e.: and technical progress is purely capital augmenting. Therefore, the second model can be written as: (2) In equation (2) all forms of technical progress are embodied in physical capital. The marginal productivity of homogeneous labour remains unchanged. This equation can describe the vintage model proposed by Solow (1959). Although the term B(t) enters in the equation as an exogenous function of time, it is reabsorbed in a reformulated input of capital services B(t) K. Technical progress acts as if it should increase the endowment of capital. With reference to model (2), we can define Solow neutrality. Let’s consider an economy developing such that the labour-output ratio remains constant: Solow neutrality exists if factor shares are not affected by technical progress. and technical progress is purely On the other side, if labour augmenting, and the third model can be written as: (3) Equation (3) does not necessarily imply that every form of technical progress be embodied in human resources. The marginal productivity of homogeneous capital is left unchanged. Technical progress acts as if it should increase the endowment of labour. Referring to this model we can define Harrod neutrality. Let’s consider an economy developing such that the capital-output ratio remains constant: Harrod neutrality exists if factor shares are not affected by technical progress. Increasing

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the efficiency of labour, this model allows steady state growth even when the rate of increase of the labour force is less than the warranted one. We can now come back to the first model in version (1.2 ). If , technical progress is equally labour and capital augmenting, i.e. it is product augmenting. With reference to this model we can define Hicks neutrality. Let’s consider an economy developing so that the capital-labour ratio remains constant: Hicks neutrality exists if factor shares are not affected by technical progress. The three concepts of neutrality are compatible with the Cobb-Douglas production function (Cobb and Douglas, 1928). More definitions of neutrality have been put forward, and a lot of attention has been devoted to the definition of biased technical progress when the derived criteria fail. 3.4 Criticisms and further developments 3.4.1 Some problems and attempted solutions This way of looking at technological change, particularly the distinction between movements along the function and shifts of the function, and the notion of disembodied technical change, has been subjected to severe criticism. Other controversies have concerned the shape of the production function, the returns to scale, diffusion phenomena, the coexistence of different techniques, and various aspects related to market failure. Two more controversial aspects, implicitly contained in what precedes, concern endogeneity of technical change and dynamic vs. static forms of progress. Both theoretical and empirical problems arise in this area. First of all, the distinction between movements along the production function and shifts of the production function is a statistical artefact, which at first neoclassical authors traded as theory. In fact, what can be observed are just two points in time; we can say that the points lie on two different production functions only after having estimated two different production functions which, in turn, characterize the two different technologies of the two different periods. The following stage is a vertical comparison between the two functions. In this way we can see how production has evolved, and where it would have been had the technology remained ‘the same’. However, when we move along a production function we move towards different capital-labour ratios which necessarily imply a different technology. Here the implausibility of disembodied technical progress emerges, as well as the ambiguity in distinguishing genuine technical progress from mechanization. Furthermore, if the quantity of capital

INTRODUCTION 13

per worker increases, a different accumulation process has occurred and the source of the increase itself should be justified. The neutrality concept, in its Hicksian and Solowian variants, relies completely on the hypothesis of capital being a primary input. However, as soon as we consider—as we should—capital as a produced input, as well as all of the interconnections through produced inputs, neutrality à la Hicks appears as not only unlikely or implausible, but impossible (Steedman, 1985). The introduction of the term ‘dynamics’, as applied to economic science, makes the situation even more complicated because of the different ways of interpreting this term. Whichever aspect of the term we stress, the neoclassical theory has been, until recently, essentially static, making use of comparative statics as the way to compare different equilibrium states. Alfred Marshall was already aware of the existence of dynamic processes that needed to be captured by the theory. Allyn Young (1928), though belonging to mainstream economics, felt the need to go back to Marshall’s and Smith’s works, stressing the relationship between increasing returns and economic progress. When we turn our attention to returns to scale, we have to note immediately that increasing returns to scale can represent a serious problem for the neoclassical theoretical construct. Uniqueness and stability of equilibrium are under threat, on the one hand, and the spectrum of monopoly shows itself on the other. Moreover, the distinction between static-reversible and dynamic-irreversible increasing returns can be drawn. As far as the question of diffusion phenomena and the coexistence between different techniques are concerned, a first answer can be found in Salter (1966). Basically, Sailer’s answer is that new and old techniques can coexist for a while because they compete on unequal terms. In fact, although the new best-practice technique uses a smaller amount of inputs to produce a given output, the introduction of the new technique itself requires a new outlay. Plants making use of the old technique, instead, need to cover only current costs since the working capital is a sunk cost. New and old techniques can thus coexist. Finally, the vehicle of diffusion is gross investment. A dynamic element was certainly introduced in this theory by Arrow (1962b) in his celebrated article on the economic implications of learning-by-doing. By making use of learning curves, cumulative output and cumulative gross investment being the appropriate measures of experience, we conclude that cost curves are not static. This means that we cannot go ‘up and down’ along cost curves themselves, and that they are not reversible in time and are path dependent. Another form of technical change worth considering here is the one that Rosenberg (1982, pp. 120–140) has defined as learning-by-using. This kind of change is classified as disembodied. The expression ‘learning-by-using’ is employed to describe those forms of learning which involve the use of durable capital goods. Rosenberg’s argument can be summarized as follows: producers of capital goods do not necessarily have a clear idea of their products’

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potentialities, so that they usually set ‘strict’ rules concerning maintenance and instructions for use (intensity and/or prolonged use) of goods themselves. However, the users of these capital goods often find the way to fully exploit their potentialities, because users are often the only ones who can fully appreciate some characteristics and qualities. Neoclassical models referring explicitly to technical change, increasing returns and endogenous growth can now be found in relative abundance in the literature (Sala-i-Martin, 1990; Romer, 1990; Helpman, 1992; Barro and Sala-i-Martin, 1995). The starting point is generally an ‘extended’ version of a Cobb-Douglas production function, characterized by more inputs and by certain properties in the parameters. Examples directed to justify increasing returns to scale range from functions characterized by a constant return to capital which implies, in a two factors production function, overall increasing returns to scale, to functions in which human capital or formal R&D activities are explicitly considered. Endogeneity of change is justified in different ways, from learning phenomena, which are intrinsically endogenous, to the intentional effort rewarded by market mechanisms.15 Thus, as far as the source of technological change is concerned, the usual reaction is to think of R&D departments and laboratories. Such thinking was already apparent in Arrow (1962a), who was among the first to discuss the optimal allocation of resources for invention. Arrow’s conclusion was that neither a perfectly competitive market nor a monopolistic one could guarantee an optimal allocation of resources. Moreover, innovation is negatively influenced by the presence prior to the innovation of monopolistic conditions. R&D activity is considered as one of the determinants of long-run endogenous macroeconomic growth in Helpman’s (1992) model. He particularly stresses the role of knowledge capital, which is that stock of knowledge that affects productivity in research laboratories. Taking as a starting point the model of Dixit and Stiglitz, who introduced variety into neoclassical theory through horizontal product differentiation, Helpman comes to the conclusion that: ‘the rate of innovation is larger the larger the economy’s resource base, the higher its productivity in the lab, the lower its subjective rate of time preference, and the higher its degree of monopoly power’ (Helpman, 1992, p. 256). Another promising direction of studies makes extensive use of the theory of games. One can distinguish two alternative points of view concerning the nature of the optimization to be pursued. On the one hand we have a socially optimum allocation, and on the other we have the individual firm’s optimum allocation. Each model is of course characterized by different assumptions concerning, first of all, uncertainty, and, in game-theoretic terminology, symmetric and asymmetric models, as well as co-operative and non-cooperative games, are considered. Moreover, when technological uncertainty is brought into the picture, it is possible to produce stochastic, as well as deterministic, models of

INTRODUCTION 15

technological change. Finally the problems of licensing and of adoption and diffusion of innovation are tackled. Many of these models are reviewed by Reinganum (1989). In particular, she reminds us that in symmetric non-cooperative models the aggregate R&D expenditure is too high compared with the co-operative optimum. That is, each firm invests too much. The contrary is true, with R&D expenditure being too low, in the case of asymmetric models in which a single firm enjoys a large market share characterized by uncertainty concerning innovation. 3.4.2 The ‘structural transformation’ research programme A different approach is followed by the ‘structural transformation’ programme of research, and namely by Kuznets (1974), Chenery (1988), Abramovitz (1989) and Syrquin (1987), who seem to put forward a ‘partially endogenous’ view of economic growth and technological changé. The analytical scheme proposed by Abramovitz in his most recent work distinguishes between a growth potential and the conditions to realize the potential itself. The potential concerns the opportunities of growing productivity. The main sources of growth are in this respect: the relative position in the international arena of the considered nation; the availability of human resources, natural and environmental resources, technology and capital, and the interaction between these production factors; the availability of social capabilities, i.e. the degree to which technical competencies, education, industrial, political and financial institutions are developed, together with openness to international flows. The main conditions for the realization of the potential are: the easiness of knowledge diffusion; the conditions which facilitate, or limit, structural change in the composition of output, in the distribution of the workforce among different occupations and industries, in the localization of population and firms; the macroeconomic management of aggregate demand capable of stimulating investments and product markets. The complex interaction between the growth potential and its realization seems to suggest that even growth is only one of the possible outcomes. The distinction between potential and actual growth is a usual one also in the Harrodian tradition. However, we are led to study the interactions that take place among the two dimensions of economic growth. Actual dynamics tends to induce permanent effects on the potential one. This can bring about several forms of irreversibility, cumulativity and path dependency, e.g. through social capabilities. Human resources are especially crucial in shaping the interaction between potential and actual growth, partly because they are, at the same time, the generators and users of knowledge. A systemic view is therefore pursued.

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4 SCHUMPETER'S ANALYSIS Schumpeter’s contribution is undoubtedly a milestone in the economic analysis of technological change, and its importance cannot fail to be noticed by any student of technological change. Almost all of the contemporary schools of thought make some reference to his contribution, and it is from his books that many of these schools get their substance. Schumpeter’s The Theory of Economic Development (1912, Eng. tr. 1934) represents a ‘new’ form of analysis of technology. In this book he introduces his concept of innovation which is seen to happen as a result of the actions of entrepreneurs. The entrepreneur can be considered as such only when he implements the innovation itself. In the Theory there emerges also the distinction between inventions and innovations, where the latter can occur without the former. In Schumpeter’s works capitalism, technological change and development become intertwined and merge into one concept that gives rise to what he called the ‘perennial gale of creative destruction’. Schumpeter demonstrates that, since the emergence of the commercial and industrial bourgeoisie, there has existed a causal relationship between capitalism and the observed increase in social wealth.16 Once the lever of profit entered society, at first by means of commercial capitalism, the route to continuous changes was opened. Thus, as Schumpeter has pointed out, The essential point to grasp is that in dealing with capitalism we are dealing with an evolutionary process…Capitalism, then, is by nature, a form or method of economic change and not only never is but never can be stationary. (Schumpeter, 1947a, p. 82) According to Schumpeter (1947a, p. 118), the rate and direction of technological change can hardly be foreseen or, to use his words, ‘technological possibilities are an uncharted sea’. Today these words are probably not as valid as they once were, with the continuous growth of both government- and business-financed R&D rendering the realization and diffusion of technological change less volatile. However, unforeseen discoveries and innovations still exist, and there exist also many cases which we could define as ‘hybrids’, like the unintentional carrying out of basic research undertaken by private enterprises.17 What we are mainly interested in here, however, are the links between capitalism and technological progress. Schumpeter often stresses the role played by the search for what he calls entrepreneurial profits. The essence of this search —the term he uses is hunt—is the utilization of new technologies. It is thus wrong to assert that enterprise and technology are two distinct factors in output growth. Enterprise and technological progress are one and the same thing or,

INTRODUCTION 17

better, the former was the propelling force of the latter (Schumpeter, 1947a, p. 110). The dynamics of capitalism engenders self-sustained growth and gives rise to what Schumpeter defines as development.18 Once again, the Schumpeterian concept of development concerns those changes inside the economic system the causes of which must be found inside the system itself: ‘By “development”, therefore, we shall understand only such changes in economic life as are not forced upon it from without but arise by its own initiative, from within’ (Schumpeter, [1912] 1934, p. 63) and this kind of development is endogenously ensured by innovations. The concept of innovation comprises: (a) the introduction of a new good or of a new quality of a good already existing; (b) the introduction of a new method of production which does not necessarily need any new discovery and/or invention; (c) the opening of a new market; (d) the conquest of a new source of supply of raw materials or half-manufactured goods; (e) the introduction of a new organizational form in an industry, meaning by this also the creation or the destruction of a monopoly position. These definitions are provided by Schumpeter ([1912] 1934, p. 66) and are continuously referred to in all his works. Often, today we implicitly, if not explicitly, refer to the Schumpeterian categorization, and we often subsume the points (a), (b) and (d) under the heading of product and process innovation. A recurring theme is the description of economic change as a businessled phenomenon. Schumpeter ([1912] 1934, p. 65, and 1947a, p. 83), in fact, writes that as a rule it is the producer who initiates economic change. Similarly in another essay he writes that it is: enterprise that introduces ‘new’ commodities, enterprise that introduces technological novelties into the production of ‘old’ commodities; enterprise that introduces new commercial combinations such as the opening up of new markets for products or new sources of supply of materials; enterprise that consists in reorganizing an industry, for instance, by making a monopoly out of it. (Schumpeter, 1947b, p. 153) There emerges an asymmetric relationship between the producer and the consumer. It is the producer who ‘teaches’ the consumer to want new things,19 and when the producer is an innovator he is also a monopolist, until the imitators copy his product or method of production. And we come now to a basic question: What is the ideal market structure to stimulate innovation? Schumpeter seems to have moved from one position to another, seeing first monopoly as a temporary result of innovative attitude the occurrence of which is the highest possible in a competitive market, and later monopoly as the better environment in which innovations can occur.

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These positions should not be considered as antithetical, and each of them reflects a relevant part of the analysis of technological change. As Freeman (1982, p. 214) has pointed out: ‘A general long-term tendency towards concentration of innovations in larger firms is quite consistent with the possibility that long-term cyclical upswings are associated with a resurgence of “Model 1” small-firm innovations.’ In Schumpeter’s works, as far as we understand, we can find many elements relating to the different hypotheses that have come to be called technology push, monopoly push, and demand pull. 1 Technology push. This is basically Schumpeter’s position in The Theory of Economic Development. In this book he argued that development was the result of the innovative ability of the entrepreneur and his introduction of new methods of production. However Schumpeter does not explicitly say where these new methods come from. The entrepreneur, it is assumed, simply finds them in the economic system. As far as basic changes are concerned, changes themselves are often engendered by new technologies, the cause of which is an economic one, but nothing more is said about it. Thus, in the Theory technological progress is, in a certain way, exogenous in the sense that only an economic cause for it, and no clear mechanism for its occurrence, is indicated. It is because of this that we can speak of ‘technology push’: Technology, whether generated outside the economic system or in the large R&D laboratories of a monopolistic competitor, is for Schumpeter the leading engine of growth. Therefore the ‘technology push’ hypothesis of the origin of innovations finds a natural place in Schumpeter’s ideas. (Coombs, et al., 1987, p. 95) 2 Monopoly push. Although monopoly push is an unusual expression, it seems to summarize Schumpeter’s position in Capitalism, Socialism, and Democracy, though Schumpeter’s conception of monopoly and monopolistic practices is a peculiar one. First of all, he states that if economists were a little bit more prone to the observation of facts they would wonder whether large firms do in fact create wealth rather than set a limit on its creation (Schumpeter, 1947a, pp. 81–82). Secondly, since the realization of many innovations needs large investments, not only in R&D but also in installing new plants, large firms are likely to be the only ones which can afford it. Thus, there could easily be superior methods of production available to, and only to, the monopolist (Schumpeter, 1947a, p. 101). Thirdly, monopolists often have to act as if they were in a competitive market. This last point is worthy of a brief elaboration. Schumpeter says that monopoly theory does not represent a good approximation to real monopolies because the monopolistic position of a single producer or seller

INTRODUCTION 19

can in general be conquered and perpetuated only on condition that the monopolist does not behave like a monopolist (Schumpeter, 1947a, p. 99). Another important point to grasp concerns price rigidity. Once (new) production is started, whether it is by a monopolist or not, improvements in a product’s characteristics are always occurring. Almost always these improvements are not coupled with price increases, i.e. we have a better product sold at pre-improvement price. Improvements themselves are more likely to occur in monopolistic markets than in competitive markets because large firms have research departments ‘every member of which knows that his bread and butter depends on his success in devising improvements’ (Schumpeter, 1947a, p. 96). Moreover, whichever industry we investigate, we almost always find that in the long run prices do adapt themselves to technological progress (Schumpeter, 1947a, p. 93). 3 Demand pull. Schmookler is always recalled as the main advocate of the demand pull hypothesis. In particular he discussed the issue whether inventions were mainly knowledge- or demand-induced, and he came to the conclusion that demand had heavy influences upon inventive activity but that it was not the only determinant of inventions themselves. Schumpeter (1947b) also referred to this hypothesis in his essay on the creative response in economic history. In this paper he distinguishes adaptive and creative response, where the former is defined as the process whereby the economy adapts itself to a change in its data in the way that traditional theory describes, while the latter is defined as the process whereby the economy or an industry does something that is outside of the range of existing practice (Schumpeter, 1947b, p. 150). The creative response is engendered by ‘a change in data’: we would argue that, without straining Schumpeter’s argument, what he calls creative response can be placed within the demand pull hypothesis, by regarding the changes in data as new forms of demand. This creative response has three fundamental characteristics: (1) it can be fully appreciated only ex post; (2) creative response shapes the whole course of subsequent events and their long-run outcome; (3) creative responses, in terms of frequency, intensity and success are influenced by the quality of brains available both at a social and sector level, and by decisions and behaviour of social actors, either as individuals or as groups (Schumpeter, 1947b, p. 150). 5 MULTISECTORAL MODELS The authors whose work will be referred to in this section have evolved in different ways from the approach of the classical school; indeed, some of them would not be likely to trace their roots to classical theory per se. We can speak of economists concerned with structural economic dynamics.

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Here is not the place to review these works fully. Rather, we shall simply try to give a résumé of some basic ideas. The authors we have in mind are Von Neumann, Sraffa, Leontief, and Pasinetti. Each of them is characterized by having stressed a particular aspect of economic change. However, their common interests lie in the analysis of the structural dynamics of an economy. This, they all agree, is determined, and can be understood, by the analysis of the evolution of the structural components of the economic system, like population, employment, investment, and national income. Technological change is closely related to the dynamics of these components in many ways. First of all, increase in productivity gives rise to the growth of real wages; this growth engenders, in turn, change in the composition of demand (Engel’s Law). Secondly, production coefficients are continually changing through time and they generally evolve at different rates. Thirdly, technical change adds new parts to the economic system, where those parts are new techniques, new processes, and new products. These last aspects have been analysed in many ways, and theorists of growth have tried to ‘endogenize’ technical change in their own works. The justification for the inverted commas is that, in many studies, technical change is still assumed simply to be a function of time. 5.1 The input-output model The first of the approaches which we will examine, whose roots lie in the classical school, is the input-output approach. Leontief pioneered this line in the 1930s with his studies on the structure of American economy. Input-output models treat the economic system as being made up of n sectors, each of which is connected through input requirements and output deliveries with the other sectors and with itself. There thus exists a technical relationship between sectors that can be captured by a structural coefficient matrix. If we add the n +1th sector, i.e. the final demand sector, we have the so-called open model. The basic static model is characterized by the following technological assumptions: (1) constant returns to scale in every sector, (2) each sector produces one homogeneous commodity or service only, and (3) the input ratio is fixed. Given these assumptions, we are capable of examining the structure of the economy of a nation, or indeed of the world economy. Let X be the total outputs vector and D the final-demand vector; if industries are to produce an output such as to satisfy the input requirements of the n industries themselves as well as the final demand, the following relations must hold: (4)

INTRODUCTION 21

where aij is the amount of commodity i necessary to produce one unit of commodity j, so that: represents the total amount of xi, needed as an input for all of the n industries. We can write in matrix notation: Or, after the usual manipulation: (5) This basic model has been developed in three ways. First of all, a matrix of capital stock coefficients B can supplement the one of technical coefficients. However, when this is done many problems of physical capital evaluation occur. Secondly, the model itself can be transformed into a dynamic one by taking into account the evolution of total production, so that we can write: (6) where Dt is the quantity of commodities delivered to the final demand sector by the n industries in period t; Xt and Xt + 1 are output levels in periods t and t+1, A and B are the matrices of current inputs and physical capital requirements, and B (Xt+1 Xt) represents an investment term, based on the assumption that a good added to the capital stock in year t is put to use in the year t+1 (Leontief, 1986 p. 31; Leontief and Duchin, 1986, p. 135). Thirdly, if technological change is to be included, we have to attach a time subscript to the structural matrices A and B, so that we can write: (7) The elements of the structural matrices aij and bij can change according to some rate, for instance, considering the elements of A, according to the expression: (8) where r represents the rate of change. This rate, however, is neither necessarily steady, nor needs be the same for the different industries. In the case of a shift in input structure some coefficients will drop to zero, while others will change from zero to a positive value. The difference between expressions (6) and (7) lies in that in the former total production changes via the investment term only; that is, in this case it is probably more correct to speak of increased mechanization rather than technical progress. The latter is characterized by two sources of change, the investment term, as before, and technical change ‘properly defined’ as the change in input structure, testified by time subscripts in structural matrices A and B. Thus, while in expression (6) what changes is the quantities whilst the technological context is given, in expression (7) change concerns the inner composition of input structure as well.

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A dynamic input-output model has been used by Leontief and Duchin (1986) to estimate the evolution of input coefficients in selected sectors of the American economy. The authors were particularly concerned with the effects of technological change on labour requirements: The dynamic input-output model is used to project, year by year from 1963 to 2000, the sectoral outputs and investment and labor requirements of the U.S. economy under alternative assumptions about its changing technological structure. (Leontief and Duchin, 1986, p. 7) The alternative assumptions lead to what the authors call various ‘scenarios’, tracing different paths that the American economy might follow between 1980 and 2000. These scenarios are characterized by different speeds of adoption of computer-based technologies in the fields considered. Recent contributions have tried to take into account how innovations can flow between sectors, via the use of new and improved goods which diffuse. An explicit attempt is made to understand the innovative flows in terms of R&D content of goods produced in industry i ending up being used in industry j (Marengo and Sterlacchini, 1990; Leoncini et al., 1996). The input-output approach is a powerful analytical tool which can help the understanding of both the structural relationships existing in economies, and the evolution of structure itself. However, it is not free from criticisms. In particular the assumption of a simple relationship linking technical change to time is rather heroic. As far as the process of embodiment of technologies is concerned, the dynamic input-output approach seems to have partly overcome the assumption of complete and instantaneous embodiment. Changes, when we make use of time subscripts, can occur in matrix A and/or in matrix B, thus influencing investment on the one hand and total production on the other. There thus occurs a change in the weights which characterize the evolution of the system. The inclusion of innovative flows between sectors also adds a further dynamic component. Another limitation of this model is that it needs non-investment final demands to be provided from outside the model itself, so that it is difficult not to make arbitrary assumptions on consumption attitudes. Moreover, it is very difficult to foresee the birth of new sectors and new products, and, associated with this, it is difficult to estimate possible consumption patterns.20

INTRODUCTION 23

5.2 Vertically integrated sectoral analysis 5.2.1 Origins of the model The concept of vertical integration is proposed and used by Pasinetti. The notion is, however, not new and was implicitly used not only by the classical economists but also by others, like Walras (Pasinetti, 1973, p. 1; 1981, p. 114). According to Pasinetti, his multisectoral scheme is the most appropriate way to analyse the dynamics of economic systems. He points out that there exist two theoretical schemes capable of generating almost the same results as his own. They are the Leontief’s and Sraffa’s models (Pasinetti, 1981, p. 109). Limiting our discussion to a comparison with Leontief’s input-output analysis, we see that at a given point in time there is no logical difference, mutatis mutandis, between a vertically integrated sector and a Leontievian industry. In fact, both models represent the same thing, looked at in a different way. Basically, the production coefficients of a vertically integrated model turn out to be a combination, at first a simple algebraic manipulation, of the production coefficients of the corresponding input-output model (Pasinetti, 1981, p. 111). The link between the two kinds of analysis consists of the transposed inverse Leontief matrix. However, once we have obtained the vertically integrated sectors from the original data we have created a different tool, particularly suitable for dynamic analysis. In fact, argues Pasinetti, when the process of vertical integration has been developed, each sector has been reduced to one flow-input of labour and to one stock-quantity of capital goods. This makes it much easier to take into account technical change than in the ‘simple’ input-output model, because by resolving all varieties of products into the same constituent elements a flow of labour and a stock of capital goods…the vertically integrated approach leads to relations whose performance over time is independent of specific technical possibilities. (Pasinetti, 1981, p. 116) This means that technological change, within this framework, implies simply a continuous change of the vertically integrated coefficients. 5.2.2 The effects of technological change In Pasinetti’s model technical change gives rise to an uneven evolution of technical coefficients, with each sector having a different rate of change. This process, in turn, causes a change in cost structure which is reflected in changes in

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relative prices. If prices are not allowed to fully adjust to changes in cost, the price system becomes distorted and loses its efficiency.21 As far as employment and demand are concerned, technical change can engender reductions in coefficients such that compensation mechanisms are not enough to fully compensate the ‘detrimental’ effects. The opposite case is also possible; that is, demand grows faster than production so that part of demand itself will be unsatisfied. As an immediate consequence, technological unemployment and labour mobility are intrinsic features of advanced capitalist systems. In this way, because of the different rates of change of techniques characterizing each sector, and because of the different rates at which demand for goods evolves, steady growth is not a natural situation or position that the system can reach. Steady growth (on the one hand) and full employment (on the other) are goals that must be actively pursued. At the firm level, particularly when large production units are involved, the search for steady growth is useful at least from an organizational point of view. At the macro level, besides the change in costs, the dynamics of population and the introduction of new products and sectors contribute to the global process of change of the economic system. The system is thus shaped by forces which lead to dynamic disequilibrium, and by other forces which lead towards equilibrium, but not automatically. 5.3 Global and composite technology analysis The principle of relative scarcity constitutes the foundation of this approach, which focuses on the contribution of the Ricardian theory to the analysis of technical change (Quadrio Curzio, 1990). This principle can be framed in terms of antagonism-coexistence-synergy between the scarcity of natural resources, the scarcity of human resources and the producibility of goods (Quadrio Curzio and Antonelli, 1988). Antagonism means that the effects of scarcity can be counteracted and delayed by the main thrust to productivity growth generated by technical progress. Coexistence means that the forces responsible for scarcity and producibility are constantly in operation, and combine to shape the organization of production processes and the nature of the connections between industries and sectors. Synergy means that scarcity and producibility interact dynamically and reinforce each other, and that, although one of the main determinants of technical progress can be identified with the search for new methods to increase endowment of means of production, this does not bring scarcity to an end, but only constantly modifies it. Ricardo’s underestimation of the potential of technical progress led him to predict the advent of a stationary state. Nevertheless, although his scheme did not make full use of the principle of antagonism-coexistencesynergy between

INTRODUCTION 25

scarcity and producibility, it was substantially based on it and may be used in explaining some of the principal economic events that have followed the shocks of the last quarter of a century. In this framework, two innovative theoretical schemes have been proposed to analyse the interaction between production, changes in activity levels and changes in techniques. The first scheme, useful in uniperiodal situations, is based on techniques ‘jointed’ in a particular manner and originating in the economic system a ‘global’ technology, which is portrayed by splitting coefficients and procedures of aggregation and disaggregation of all the production processes which utilize non-produced means of production (NPMP).22 Two main cases are examined, starting from a situation of labour full employment with a given number of NPMP activated and then increasing the labour force employment. In the first case continuous changes in efficiency of technology take place, the number of processes with activated NPMP does not change, but the global technology undergoes changes in structure (not in its dimension) and in efficiency as the last process with NPMP assumes an increasing weight. Increases in the quantities produced and in employment also take place. In the second case discontinuous changes in efficiency of technology take place and the number of processes with NPMP increases. The structure and dimension of the global technology change, together with its efficiency. Also in this case increases in the quantities produced and in employment take place. The second scheme, useful both in uniperiodal and dynamic situations, is based on ‘composite’ technologies, in which the techniques remain ‘dis-joint’ but nevertheless connected, with each technique utilizing one single NPMP.23 The main conclusions reached by this approach are the following. (1) The analysis of a production system employing NPMP needs a more complex explanation than the one provided by standard theories. (2) The physical efficiency of the production system changes both because of the greater activation of the last NPMP put into operation and because of new NPMP activated. (3) Technical change, including both technical progress and choice of techniques and technologies, depends not only on the internal efficiency of each available technique but also on the compatibility of structures and linkages of the different techniques successively employed, taking into account the constraints imposed by the NPMP. (4) Technical progress cannot always be transformed into an increase of efficiency at a systemic level; this is due to the problems arising from the composite nature of production structures and from the possibility that ‘residuals’, due to the imperfect compatibility among technologies, will reduce or eliminate the effects of technical progress. (5) The order of efficiency between NPMP, established on the basis of price-distribution criteria, might not coincide with the one corresponding to maximum accumulation and growth (Quadrio Curzio, 1990, pp. 7–9). This approach is thus characterized by non-proportional growth and nonsteady dynamics. Moreover, it emphasizes the powerful role that relative scarcity

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can play in a systemic framework as a focusing device: that is, as a means effective in focusing individual and collective concern on specific cores of structural change. It can be maintained that the analytical schemes developed in this approach are extremely useful in the study of ‘technological systems’ and ‘national systems of innovation’. An attempt to pursue this line of thought has been made by Antonelli and Pegoretti (1992, 1995). In this work they focus the attention on some effects of the main forces driving structural as well as institutional change in economies in which the immaterial resources play a relevant role. Such an analysis is particularly important as it refers to some of the crucial sources of growth and determinants of national systems competitiveness in the context of dynamic interdependencies which develop on an international scale. 6 NEO-KEYNESIAN MODELS The neo-Keynesian perspective has generally a macroeconomic flavour, and the problems relating to technological unemployment are, in particular, stressed. Problems relating to economic growth and cyclical fluctuations are also considered. The latter kind of analysis, having as the central object the role of investment in economic cycles, characterized the debate for the two decades following the late 1930s. What follows overlaps, from the theoretical point of view, with the analysis contained in the previous sections devoted to multisectorial models and Schumpeterian theory. 6.1 The debate immediately after Keynes Let us note at the outset that Keynes’s General Theory (1936)was essentially static and short-run oriented; thus technological change could not be taken into account. However, one of the main concerns of Keynes was to offer a theoretical explanation of the existence of involuntary unemployment. To put it another way, he demonstrated that unemployment could be coupled with equilibrium. The Keynesian theory has been subjected to criticisms and has under-gone many developments. Among the less recent contributions let us recall, first of all, Domar’s (1947)24 work. Domar’s analysis concerns expansion and employment. The basic criticism of Keynes results from the fact that in his General Theory he assumed a simple relationship between national income and employment because the analysis was short run. However, we have to take into account the dual effect of investment. On the one hand it generates income through the multiplier, while on the other it increases productive capacity. The right assumption would be, according to Domar, to consider the percentage of labour force employed as a function of the

INTRODUCTION 27

ratio between national income and productive capacity, where by productive capacity he means the total output of the economy at full employment (Domar, 1947, pp. 37–46). Domar also suggests that it would be interesting to introduce time lags in the analysis; in this paper, however, he considers both the multiplier effect and the increase in capacity as taking place simultaneously . Another great economist, Michal Kalecki, was not only aware of the existence of time lags but explicitly made use of the time lags occurring between investment decisions and investment productions (Kalecki, 1936– 7, p. 77).25 To the above we should add the impact of the accelerator as explicitly propounded by Harrod in his trade cycle theory (Harrod, 1936, pp. 53– 64).26 The accelerator (at first called the Relation), in its original form, is the principle according to which, in an upward phase of the trade cycle, activity in trades producing capital goods can increase more rapidly than that in the trades producing consumer goods (Harrod, 1936, p. 53). The value of the accelerator, particularly in its relationship with net investment, is influenced by technical improvements and inventions besides the rate of interest and relative prices. In fact, the importance of technical progress is that it provides a basis of net investment (Harrod, 1936, p. 60). The principle behind the accelerator can be made clearer through the explanation of the interaction of the accelerator and the Keynesian multiplier. As Hansen points out, when for instance an increase in autonomous investment occurs, income will increase by a magnified amount according to the multiplier. ‘This increase in income may, however, induce a further increase in investment’ (Hansen, 1951, p. 173, original emphasis). The principle can be also explained in terms of ‘derived demand’ (Hansen, 1951, p. 179). The most important scholar concerned with technological change, in the Keynesian tradition, is Nicholas Kaldor. He has anticipated with Schumpeter many of the key ideas in the study of endogenous forces driving economic growth (Kaldor, 1954). In this article on economic growth and cyclical fluctuations he stresses the fact that with a few more assumptions Keynes’s model is capable of generating a rhythmical movement around a stationary equilibrium position. The model, though characterized by a complete cycle, is still static. In order to introduce a trend in the model itself, it is possible to take into account, for instance, a linear percentage growth in population over time, or neutral technical progress (Kaldor, 1954, pp. 222–223). These assumptions do not upset the model itself; they consist of ‘a superimposition of a linear trend introduced from the outside on an otherwise trendless model without altering, in any way, its basic character. […] The trend itself is not explained; it is introduced as a datum’ (Kaldor, 1954, p. 224 and p. 226). So, why do societies progress at such different rates? Kaldor’s answer is manysided, ranging from human attitudes to risk-taking and money-making to cultural reasons.27 The most important role is attached to entrepreneurial behaviour, to ‘Schumpeter’s hero’ (Kaldor, 1954, p. 232).

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In our perspective, it can be argued (Pugno, 1992) that it is possible to distinguish a first Kaldor (1957–66) mainly concerned with the ‘growth models’, and a second Kaldor (1966–86) mainly concerned with the ‘growth laws’. The second Kaldor questions the very relevance of the notion of equilibrium, and its main argument is the dominance of increasing returns. However, the ‘cumulative causation model’ develops in steps. In the first step (Kaldor, 1940), the economic dynamics is conceived as a sequential and cumulative interaction between demand pulls and production changes on the supply side. In the second step (Kaldor, 1972), the Adam Smith and Young lessons led him to conceive the increase in productivity as the effect of the sequential and cumulative interaction between demand, supply specialization, creation of new production stages and of new demand: the ‘Kaldor-Verdoorn’s law’. A different perspective with respect to the original Keynesian theory was offered, almost immediately after the publication of the General Theory, by Hicks (1937). In his article on Keynes and the classics he outlined the IS/ LM model, which was subsequently developed by other economists, and has become the most popular macroeconomic model until the early 1970s. Technical progress, however, has never been a central issue of this line of analysis. 6.2 Recent developments Recent developments, besides trying to substantiate the theory by building its microeconomic foundation, have led towards the embodiment of the aspects relating to technological change on the one hand and to market forms on the other. A great effort to take into account both technical progress and market forms has been made by Sylos Labini (1969, 1984, 1990). His works constitute the trait d’union between old and recent contributions, both chronologically and scientifically: the first Italian edition of Oligopolio e Progressa Tecnico, in fact, dates back to 1956. However, we will raise only a few important points. Proportionality between variations in investment and employment can be valid only on the assumption that wages, technique of production, and price of capital goods do not change. These seem to be reasonable assumptions if they refer to the short run; however: the replacement of machines gradually wearing out affords continual opportunities for introducing quite important changes in the technical coefficients…. This kind of replacement is a gradual process which forms part of a normal business management; the assumption of technological change, therefore, can logically be applied also to the short period. (Sylos Labini, 1969, p. 153) The same concept can be expressed in a different way. If we do not take into account the effects of investment on productivity and productive capacity

INTRODUCTION 29

because they will affect the economy in the future, we must, however, consider the effects of past investment on today’s productive system. Thus while current investment tends to increase employment, past investment tends to reduce employment itself. This kind of analysis can lead to an explanation of the determinants of the adoption and diffusion of innovations, particularly the labour-saving ones, but is not capable of explaining the phases of R&D which precede their adoption (Pini, 1992, p. 127). Looking at the interaction between market structure, technical progress and price determination, Sylos Labini suggests that the process of industrial concentration, typical of the twentieth-century economies, creates technological discontinuities, so that only large firms can use certain techniques. Price determination will be influenced by the market size, demand elasticity, and technologies. In such a situation competitive mechanisms, though not the traditional ones, still exist. In particular, price-leading firms will tend to keep the price at a level such as to prevent the entry of new firms; this means that, with respect to the consumer, the oligopolistic firm probably cannot fully exploit its power. Several authors have progressed this work in different directions.28 In the work by Boyer, Coriat and Petit29 the theoretical basis is the Kaldorian cumulative growth model. This model has been generalized in order to study growth regimes characterized by unstable dynamic equilibria. The inclusion of features concerning institutional factors and technological systems in the economic analysis of growth allows these authors to develop the so-called théorie de la régulation. The neo-Keynesian macroeconomic debate has also been enriched by the confrontation with the other schools of thought which emerged particularly during the 1970s and 1980s. In this respect we might mention the laying of microeconomic foundations to macroeconomics and the role of expectations. In order to emphasize the ‘opposition’ to New Classical Macroeconomics, some economists coined the expression New Keynesian Economics to describe some of the works belonging to the Keynesian tradition.30 The main themes of New Keynesian analyses concern pricing behaviour, wage setting, real and nominal rigidities. What characterized the New Keynesian school in the past decade has been, according to Gordon (1990, p. 1137), ‘the search for rigorous and convincing models of wage and/or price stickiness based on maximizing behavior and rational expectations. …Most new-Keynesian models combine rational expectations with maximizing behaviour at the level of the individual agent.’ Though the New-Keynesian literature seems to have provided many explanations of wage and price rigidities, we can maintain that it can be summarized as a model in which monopolistically competitive firms set their prices accepting the level of real sales as a constraint (Gordon, 1990, p. 1116).

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As we can see, the language has sharply changed. Technical change is not explicitly considered, and the key role played by investment is hardly mentioned. Input-output tables with the related technological environment are, however, referred to (Gordon, 1990, p. 1151). 7 PRODUCTION AND TIME 7.1 Hicks's neo-Austrian model A synthesis of what a modern or neo-Austrian model is can be found in Hicks (1973, 1985) and Amendola and Gaffard (1988). Hicks (1985, p. 149) asks himself the following questions on the nature of the traverse, i.e. on the transition from one steady state to another: ‘How do we know that, after a new technique is introduced, there will be convergence to a new equilibrium? And what will happen on the way?.’ Before trying to provide his ‘Austrian’ answer to these questions we need to discuss his modelling a little. When we speak of the Austrian process of production, we speak of a technique characterized by a profile of unit processes which are defined as streams of inputs which are converted into streams of outputs, with both streams extending over time. Since homogeneous labour is, in the simplest version of the model, the only input, we can speak of flows of labour. Such a process of production can be divided into two conceptual phases: a first one in which intermediate products, like capital goods, are produced, and a second one in which more labour is performed on these intermediate products to give rise to final output. However, as Hicks points out, ‘all that is inside the process. It is just the stream of labour inputs and the stream of final product outputs which define the process’ (Hicks, 1985, p. 145). Machines are produced but not ‘shown’, and emphasis is laid not on physical capital goods but on the making of the machines. The profile of a unit process can be indicated by means of a vector of dated inputs, at, and outputs, bt (a0, a1,…,an; b1, bn) (9) An assumption of constant returns to scale is made so that the scale of production can be changed by multiplying each element by a common factor (i.e. the number of starts in Hicksian terms). Let w be the wage rate and r the rate of interest. The viable processes will be those for which the following relation holds: where , (i.e. the surplus, or deficit, in period t).

INTRODUCTION 31

Expression (10) represents the discounted value of the streams of qs to the start of the process; that is, the present value of the process itself. Not all of the constituents of this expression are positive but the summation must be nonnegative. Moreover k0 diminishes as w increases (and r remains unchanged), while it rises as r falls.31 Finally, the market rate of interest associated with the wage rate must be such that k0=0. Let us now consider an economy fully adjusted to a technique. In period 0 a new and more efficient one becomes available; the traverse analysis tries to explicate the phases through which the economic system passes to reach the new equilibrium. Hicks, in particular, was interested in the early phase of the traverse itself, in which the old and the new techniques coexist, and in phenomena connected with further induced inventions. The presence of a new technique implies a higher rate of profit whose counterpart is a given wage rate. In this way all the new starts will make use of the new technique. Moreover, in the early phase of the traverse, processes using the old technique coexist with the one using the new. The economic life of these processes, however, is shortened. It can be demonstrated (Hicks, 1973, 1985) that by following a fixwage path, i.e. a path along which the wage rate remains unchanged, an increment of employment could occur in period one, though the opposite effect, i.e. unemployment, cannot be excluded. If a full employment path must be followed, a fall in the real wage rate is necessary, but this fall can be overcome in the long run. A third case, that can lead to induced inventions, can be found in a situation in which the economy is following a full employment path with restricted supply of labour. Such a situation implies that at a certain point in time an increase in the real wage rate will occur. The new situation may render more profitable the use of techniques that would not have been profitable to adopt in the case of the pre-labour-forcerestricted wage rate. If such a shift of technique occurs, and in particular when it comes about in response to changes in wages, it must be classified as an induced invention. The analysis of the effects of innovations on employment and wages has been explicitly, if briefly, discussed. The reader may well be reminded of this by Ricardo’s chapter XXXI ‘On Machinery’, added to the third edition of the Principles (1821), as was Hicks: ‘So where we come to, on this Austrian route, is close to Ricardo’ (1985, p. 156).32 That is close to Ricardo’s postulation as to the negative effects on employment due to the introduction of machines. The Hicksian—Austrian analysis stresses the fact that such an effect can occur if the innovation is characterized by an increase in the input coefficients, i.e. labour, needed during the (first) phase of machine building. In this case, in fact, the short-run production of consumption goods will fall and, if productive capacity is to be maintained at least unaltered (Hicks, 1973), the system as a whole would experience unemployment.

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7.2 Further developments Criticisms of this approach are basically related to the assumptions of the existence of only one kind of input, homogeneous labour, and to the existence of the traverse from one equilibrium path to another. However, the biggest problem is that the source of innovations is never indicated. The new technique simply becomes available—although the partial exception of induced innovations must be acknowledged. Given the above indicated assumptions; that is, ‘given the parameters of the model, the “traverse” becomes a predetermined sequence of decisions of a quantitative kind which can be fully traced out ex-ante and where expectations play no role’ (Amendola and Gaffard, 1988, p. 32). Moreover there is no real choice concerning investment since a superior technique automatically implies its choice. To put it another way, if we violate a single assumption, we cannot fully understand ex ante how the productive capacity will evolve. The process of change can be limited by the existence of the processes already existing, and a sequence of innovations can alter the path(s). Thus the idea of the traverse as the process of transition from one equilibrium path to another becomes a deceptive one. The point of arrival cannot be predicted. The ‘natural’ development of the neo-Austrian approach is found in Amendola (1983) and Amendola and Gaffard (1988). In these works the process of innovation is analysed as a sequential process articulated in real irreversible time. Thus the evolution of the productive capacity is influenced by the environment in which the capacity is located. In this way not all of the technical possibilities theoretically available to the system can be pursued. Structural constraints play an important role in shaping technical change. As far as the formal models are concerned, in this development of the neoAustrian theory, a vector is substituted for the scalar in defining the existing resources, and a matrix is substituted for the vector in defining the labour inputs. By means of the latter substitution different kinds of labour can be considered; moreover, there emerges a change of perspective with respect to labour itself. It is now considered as a human resource endowed with purposeful will. The immediate consequence of this statement is that there exists a bi-directional relationship between technology and human resources. The creation of new technologies becomes identified with the enrichment and specialization of the human resource and can thus be brought inside the model (Amendola and Gaffard, 1988, pp. 33–34). A similar explicit attention to the sequential nature of technology and to the length of use of material and human inputs, coupled with the analysis of the interaction among the fund factors available—but not necessarily in use —within the firm, can be found in the model proposed by Georgescu-Roegen (1970, 1976, 1986). This seminal contribution has paved the way to several studies.33 The

INTRODUCTION 33

production process can be described as a network of tasks rather than as a combination of inputs (Scazzieri, 1993, p. 242). The main features of technological change in this analytical framework are the following (Scazzieri, 1993, p. 253): (a) it is conceived of as a process of transformation of current practices rather than of input substitution; (b) it can be based on a reorganization of existing tasks rather than on the invention of new tasks; (c) the institutional framework is a crucial determinant of its speed of diffusion; (d) changes in technical practices may be analysed at different institutional levels (firms, production subsystems or systems). 8 THE EVOLUTIONARY APPROACH 8.1 Precursors and concepts: from Schumpeter to national systems of innovation Though the links between different schools of thought and Schumpeter are often apparent, these links are even more apparent in the case of the evolutionary theory. This can be seen in the following quotation taken from Nelson and Winter’s book (1982): The influence of Joseph Schumpeter is so pervasive in our work that it requires particular mention here. Indeed, the term ‘neo-Schumpeterian’ would be as appropriate a designation for our entire approach as ‘evolutionary.’ More precisely, it could be reasonably said that we are evolutionary theorists for the sake of being neo-Schumpeterians’ (Nelson and Winter, 1982, p. 39, original emphasis). The evolutionary approach has now reached a high degree of development, and a good deal of attention has been devoted to it. Ideas characterizing this approach spread during the 1970s, and have been subject to further diffusion during the 1980s and early 1990s (Magnusson, 1994). However, some precursors must be indicated, the first being Alfred Marshall, whose passage: ‘the Mecca of the economist lies in economic biology rather than in economic mechanics’ is often quoted.34 The two other authors always referred to are Veblen (1898) and Alchian (1950). These two contributions, however, are very different in kind. Veblen’s article is concerned with the question: Why is economics not an evolutionary science? His question, we want to emphasize, came historically in a period in which Darwinism (on the one side) and

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Positivism (on the other) constituted the leading scientific and philosophical paradigms. Veblen’s problem was thus one of scientific identity. Alchian’s analysis, instead, is based purely on economic ground, and the terminology is, of course, the contemporary one. His work constitutes an attempt to incorporate in economic analysis incomplete information and uncertainty, and he makes use of an evolutionary approach. As he himself wrote, where foresight is uncertain profit maximization is meaningless as a guide to specifiable action (Alchian, 1950, p. 211). What matters is the pursuit of profit as the relevant objective, whose fulfilment is rewarded with survival. Firms actively try to adapt to the environment around them; moreover, while survivors may appear to be those having adapted themselves to the environment, it might well be that it was the environment that adopted them. Schumpeter explicitly used the term mutation, and it is no simple coincidence, we believe, that he used this word in the chapter devoted to the process of creative destruction. Moreover, after a few lines, he refers to the economic system as to an organic process (Schumpeter, 1947a, p. 83). Other authors gave hints towards an alternative direction with respect to orthodox theory; among these Metcalfe (1989, pp. 59–64) recalls Steindl and Downie.35 Whilst none of the above-quoted precursors developed a systematic evolutionary theory, such work has been undertaken by some economists since the 1980s.36 The use by economics of concepts related to evolution developed in the biological sciences has not been merely passive. However, some concepts such as population thinking, as distinct from typological thinking, can be borrowed directly. An evolutionary theory will make use of the former concept, because it implies the existence of a population composed of many individuals who share many characteristics, but who are also characterized by individuality. To put it another way: in population thinking, species are described in terms of a distribution of characteristics and, whereas in typological thinking variation is a nuisance, in population thinking…it is the variety in the system which drives the evolutionary process. (Metcalfe, 1989, p. 56) As far as evolution and economics are concerned, this involves a shift of perspective from static orthodox equilibrium to dynamic economic change.37 Other aspects characterizing the evolutionary approach are the following. First of all, for a theory to be defined as evolutionary it must contain a mechanism that introduces novelties to the system (Nelson, 1987, p. 12). Novelties are innovations. Secondly, this mechanism must be understandable, even though it is characterized by random or stochastic elements, and must operate on entities present in the system (Nelson and Winter, 1982, p. 15).38

INTRODUCTION 35

The models that this kind of theory generates are the result of the application of the components of the theory itself; namely, decision rules, search, and selection, which also bridge the micro and macroeconomic level of the analysis. These components can be made clear alongside the explanation of the different kinds of routines. The most recent contributions of many evolutionary economists (Free-man, 1988; Nelson, 1993; Hodgson and Screpanti, 1991; Lundvall, 1992) have converged towards a new way of analysing the evolution of particular economies by pointing at the notion of ‘national systems of innovation’—a notion anticipated by Babbage (1830). The definition aims at synthesizing the common features of a nation, and in particular the set of institutions which, individually and jointly, contribute to the definition of a national development path (Metcalfe, 1995). In this context it is easier to understand why the convergence hypothesis predicted by the neoclassical theory does not hold. Cumulative causation processes, specific directions of specialization, lock-in phenomena and path dependence can find reasonable explanations. 8.2 Features of the model As with almost all other models concerned with change and growth, this one also considers technical progress as the most important lever of growth. In this model the relationship between technical progress and R&D activity, which is itself the result of a decision process, is particularly emphasized. Decision rules can be considered as a subset of a routine, where routine is a term used to indicate all regular and predictable behavioural patterns of firms (Nelson and Winter, 1982, p. 14 and p. 209). Predictability, however, acquires a meaning different from the one it has in orthodox theory. Three classes of routines can be distinguished: (a) routines which characterize a particular plant during a particular period in terms of its capital stock (fixed) and factors of production in general—short-run routines are also defined as standard operating procedures; (b) routines which determine the period-byperiod variation of the firm’s capital stock and factors of production, considered as fixed in the short run; (c) routines which, when for instance profits are no longer considered adequate, lead to an improvement in the firm’s performances. Basically this kind of routine involves deliberate action on the part of the firm through search activity (Nelson, 1987, p. 22). In this decision-making process R&D activity is conceived of as an organizational response to adversity, rather than a continuing policy commitment. In other words, if firms believe that they are sufficiently profitable, they do not carry out R&D activity (Nelson and Winter, 1982, p. 211). This conceptual construction engenders Markov processes because state variables at time t are influenced by the situation existing at time t –1. Thus,

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when we speak of predictability in evolutionary models, predictability itself always concerns a probability distribution. At this stage the role of selection has to be investigated. Different decisions will engender different development paths of plants and industries because of different information, knowledge, know-how, and R&D performance. If, for the sake of simplicity, each firm produces a single product or service by means of its own technology, market demand will operate as a selective force by selecting products as well as the technologies used to produce them. Thus, there are many possible techniques of production producing similar goods with similar performances. Saviotti (Chapter 10, this book) clearly distinguishes between the technical characteristics and service characteristics of a good. What is directly demanded by users is a service. However, a service can be offered in many ways. We can think of mechanical and electronic watches, which offer the same service by means of two quite different technologies. This is nothing but variety in technological and organizational forms, and it gives rise to economic change. Different forms of organization can engender different levels of profitability, and these latter imply different rates and directions of technological change. As a particular case we can think in terms of the share reached by the different technologies experiencing selection forces in a given technology set. Once we have defined the average technology in terms of costs and quality adjusted price, we see that the relative economic weight of a product, and thus of a technology, changes according to its distance from average performance within the technology set (Metcalfe, 1989). Another feature worth stressing is that by means of these models one can build up macro-aggregates taking microeconomic data as a starting point. Nelson and Winter have addressed the same problem as Solow did; that is, the increase in output in industry over a forty-year period in the United States economy, considering private non-farm economic activity. However, where Solow used ex post statistical data to evaluate his technical change index A(t), Nelson and Winter obtain plausible (p. 218) aggregative time-series by means of a model capable of generating these data ex ante, setting only the initial conditions in such a way as to correspond to the conditions revealed in Solow’s data. To conclude, in these models variety is accepted, bounded rationality is the normal situation, maximizing behaviour is not necessary, nor is the concept of the production function; neither equilibrium nor optimality in the Paretian sense are implied. As a further remark we might note that the term ‘evolutionism’ has been used without any Ideological implication. Evolution in this sense does not mean progress in any obvious sense, it simply involves change (Metcalfe, Chapter 2, this book). Not surprisingly a similar concept was expressed by Schumpeter in Capitalism, Socialism, and Democracy (1947a, p. 74) where he said that most methods of social selection do not guarantee the performance of the selected individual.

INTRODUCTION 37

Such a situation is described by Arthur’s analysis (1989), characterized by two competing technologies, A and B, and two kinds of adopters, R and S. R agents prefer A to B, while the contrary applies to S agents. Arthur demonstrates that when increasing returns to adoption exist, if by chance adoption pushes technology A far enough ahead of B, S-type agents, preferring technology B, will shift towards the ‘less’ preferred one. Such a process is illustrated by means of a random walk with absorbing barriers, which means that the system becomes ‘locked-in’. This kind of process does not imply path efficiency. In the case of two technologies the share must eventually become 100 per cent and 0 per cent. The choice of the surviving technology means only that it was ‘superior’ at a certain point in time. Over a longer period the excluded technology could have been more efficient. 9 SYSTEMIC ANALYSES Recent as well as less recent economic literature39 in many cases connected with the evolutionary approach, in some others more linked to classical principles has pointed at the need of a systemic view of technology and at the determinants of its change. Technological change occurs in a context in which economic, institutional as well as ‘pure’ technological forces play an active role. The picture is further complicated by the fact that when we deal with technology we have to analyse at least its three following dimensions; that is, technology as knowledge, technology as skills and technology as artefacts (Layton, 1974; De Liso and Metcalfe, 1996). The interaction between natural and environmental resources, on the one side, and technology, on the other, makes even more complex the task of the economic analyst (Antonelli, Chapter 4, this book). In such a context we have to deal with technology creativity on the one hand, and with effectiveness and efficiency in the use of the technology on the other. We do recognize the existence of an agenda which has to do with incentives and the resources allocated to technological development. In conditions of modern capitalism the development of technology is primarily carried out in firms, but these firms operate in a wider institutional matrix in which we observe the existence of a technological community and a set of institutions and mechanisms that define a particular technology support system. Different institutions generate different kinds of knowledge which, in turn, imply different accumulation mechanisms. Co-ordination through the market is not sufficient for our purpose. For a variety of reasons market mechanisms will fail to develop technology in an ideal fashion, and for this reason other kinds of coordination between institutions are of paramount importance. The technology support system notion has thus been recently proposed, which means the existence of an organized institutional structure, which of course includes firms. The existence of a technology support system implies that there are some general technological principles accepted by a specific technological community. It also

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implies that there exist some generic elements of a technology capable of aggregating the intentional action of both economic and political agents, where the latter play a regulatory and normative role. The central point thus becomes that there occurs an alignment of objectives which makes it possible to focus on specific directions, strengthening each member of the system, as well as the structure as a whole. There develop a series of links, which, once started evolve and can become different from what they were at the beginning. Also, once the system is started, its history matters, i.e. it biases the technological direction undertaken (De Liso and Metcalfe, 1996). We have explicitly mentioned national systems of innovation in the previous section, and we have just referred to technological systems in their broader form in the present one. The notion of technology support systems, however, makes it clear that systems can be focused on specific technologies and techniques, or even on sectors. There also exists another systemic dimension that must thus be taken into account: the ‘local’ dimension; and its relevance has been stressed for a long time, although with different emphases, by several authors (Brusco, 1989; Becattini and Rullani, 1993; Storper and Harrison, 1991; Nuti, 1991). There are different ways of setting the boundaries of a local system, but generally subdivisions occur by referring to some sort of homogeneity. Such homogeneity can be found at different levels. Two relevant ones concern the development of a specific single technology or the development of a series of connected technologies, examples being the Silicon Valley and the Italian industrial districts, respectively. Of great importance is the connection of these local, technological, production and innovation systems with the human resources and skills endogenously developed and the creation of local open labour markets. Intellect co-operates in the creation of economic values by means of a continuous transformation of context-specific knowledge in general codified knowledge, which is then conveyed in other specific economic contexts. In this view, the aim of each economic agent, which is ultimately conditioned by path dependency, is to achieve commensurability among economic projects, also in order to select from different technological paths and to create the new ones. This tends to shape the profile of the accumulation cycle of knowledge, leaving an important role to the theory of ‘embedded action’. In order to derive from a specific context the opportunity of new variants of goods, services and processes, information is necessary but not sufficient. Experience, or the capability to extract and decodify the relevant elements from the context is what is needed most. When the context is understandable in a relatively static and simple framework, routines and algorithms40 are adequate to support economic actions. But, until the complexities of a context are not fully understood so that they can be standardized, an economic action of translation is crucial. Translation is based, apart from information, on knowledge and skills.

INTRODUCTION 39

10 PLAN OF THE VOLUME The volume is divided into three parts. The first part is composed of four chapters which complete and deepen some of the topics analysed in the previous assessment. The four chapters of which the second part of the volume is composed tackle specific theoretical aspects of technical change. Part III, though dealing with further aspects of the theory of innovation, is characterized by more applied works. Metcalfe, in Chapter 2, reviews some recent developments in the evolutionary theory of technological change, dealing with theoretical concepts. After having illustrated some analogies between economic and biological evolutionary concepts he reminds us that intentional behaviour is at work with all its subsequent implications. Pini provides a critical updated review of recent analyses concerned with technological unemployment; concentrating on reabsorption mechanisms. In particular he identifies four approaches: the neoclassical, the classical-Keynesian, the multisectorial, and the ‘transitional’, stressing their strengths as well as their weaknesses. Antonelli emphasizes how in the contemporary world the old categorization used by economic theory, making use of the simple factors of production, land, labour and capital, is no longer appropriate. More powerful categories are outlined by the ‘broad factors of production’; namely, natural and environmental resources, labour and human resources, physical capital and technology, combined in a systemic view. Pegoretti reminds us that there must be a closer link between the microeconomic and the macroeconomic analysis on the one hand, and that reference to economic reality is important on the other. His analysis considers the different dimensions in the process of technical change, taking into account aspects such as the financing of innovations and the systemic interactions which develop around the firm. Metcalfe’s essay on diffusion and the process of technological change marks the transition to the second part of the volume. After recalling the distinction between adoption and diffusion, the author focuses on the processes of diffusion and substitution between technologies. He analyses these processes by means of a neat formal model. Some policy implications conclude the work. The note by Filippini on the diffusion of innovation constitutes an extension of Metcalfe’s diffusion model, taking into account the effects on diffusion itself engendered by increasing returns to scale. In particular he demonstrates that most of the results emerging from the original model survive the introduction of increasing returns themselves. Egidi’s main theme is that decision-making occurs in a situation characterized by incomplete knowledge and uncertainty. Such a situation, however, is not due to the human subjective way of dealing with the world, but can be a feature of

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formalized reasoning. Given these statements, first-best maximizing behaviour is no longer applicable because no one knows what the first-best is. Satisficing behaviour becomes the consistent operative criterion. Piacentini focuses his attention on three types of temporal length of which the analyses concerning innovation make use. The three categories considered and explained are cycle time, switching time and lead time. The author emphasizes how within each category there exist possible sources of cost reduction. Though microeconomic in kind, the analysis could be extended to a sectorial or aggregate level. The analysis carried out by Saviotti constitutes an explicit attempt to open up and explore the black box of technology, taking into account the knowledge and artefacts dimensions of technology itself. The author’s emphasis thus concentrates on the revealed technological performance implicit in firms’ products. Such a perspective allows the author to analyse technological variety and its role in economic development. The chapter by Leoni examines the relationship between technical progress and human resources development. In particular he points out that while economists have generally concentrated on the effects of the former over the latter, the opposite relationship also deserves attention. He thus supplies empirical evidence from a sample of 200 firms. Maggioni’s work constitutes an attempt to include spatial considerations in the analysis of technological change in an evolutionary framework. He considers innovation policies in this light whilst pointing out the necessity for an integration between the network and the environment. The chapter is concluded by an analogy between neural networks and science parks. Sassu and Paci tackle the issues related to the internationalization of technological activity by means of an analysis concerned with firms’ patenting strategies. In particular, they consider patenting propensities and international patents flows. An econometric investigation of Italian firms’ strategies is offered. Sirilli’s chapter is devoted to the models and indicators for science and technology analysis. After a brief review of the early attempts to measure R&D activity, he concentrates on the most recent attempts to measure it. Problems relating to sources of information, as well as to methodology and harmonization needs are emphasized. Efforts to take into account the social impact of science and technology are also recalled. NOTES 1 The authors share equal responsibility for sections 1.1, 2.2, 6, 7.2, 9 and 10 while G. Antonelli is mainly responsible for sections 1.2, 3.3, 3.4.2, 5.3 and N. De Liso for sections 2.1, 3.1, 3.2, 3.4.1, 4, 5.1, 5.2.1, 5.2.2, 7.1 and 8. We wish to thank

INTRODUCTION 41

2 3 4 5

6 7 8 9

10 11 12 13

14 15

16 17 18

19 20 21 22 23 24

25

26 27

Riccardo Leoncini for his comments and Anna Alexandroff Syrquin for her editorial assistance. For more complete surveys concerned with the economics of technical change see Stoneman (1995), Freeman (1994) and Nelson (1995). This is particularly true with respect to Europe. See also Schmoller ([1898] 1904, p. 540). Actually, we should go back even further to 1638, when Galileo published his Dialoghi delle Nuove Scienze. For this suggestion we are indebted to Professor Carlo Poni. See Eltis (1975). See Sraffa (1951, pp. Iviii-lix). Pini (1992) and Chapter 3, this book. See Capital’s chapters 7, 14, and 15 which constitute an interesting analysis of the process of technological change that occurred in the eighteenth and nineteenth centuries. See Pasinetti (1981). Of which eight editions were published between 1890 and 1920. See the first lines of the Preface (p. v) of Industry and Trade. The ‘Appendix H’ on the ‘Limitations of the use of statical assumptions in regard to increasing return’ (pp. 805–812) has become one of the most studied parts of the Principles. See Abramovitz (1989), Solow (1957), Jorgenson and Griliches (1967). Romer’s second premise, for instance, is that ‘technological change arises in large part because of intentional actions taken by people who respond to market incentives. Thus the model is one of endogenous rather than exogenous technological change’ (Romer, 1990, p. 72). See Schumpeter (1947a, pp. 72–74). See Rosenberg (1990). In the first German edition of the Theory, i.e. in the Théorie der wirtschaftlichen Entwicklung, Schumpeter defined this endogenous process as the dynamics of economic systems. In later editions, to avoid conceptual confusion, he used the word development. Could this expression represent a revised version of Say’s Law? For a review and an updated analysis see Pini (1992, Chapter IV). Adjustment can be reached by means of different sectorial rates of inflation. This scheme was first developed in Quadrio Curzio (1967). This scheme was first developed in Quadrio Curzio (1975). This article should not be confused with the more famous one published in 1946 on ‘Capital Expansion, Rate of Growth’, and Employment, Econometrica, pp. 137– 147. There is no room in this introduction to reopen the debate over whether Kalecki or Keynes should be given priority on the theoretical construction leading to the concept of effective demand, as well as to the role played by investment. The reader could consult the book by Sawyer (1985, pp. 178–208). It is not our intention to reconstruct the history of the accelerator, which would likely go back to Marx. Kaldor recalls the Protestant ethic and the rise of capitalism.

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28 See Sylos Labini (1984, 1990), Freeman and Soete (1987), Kregel et al. (1988), Gordon (1990), Mankiw (1990), Hargreaves Heap (1992), Pini (1992). 29 See Boyer and Petit (1982; 1988), Boyer and Coriat (1986). 30 Gordon (1990, p. 1115, note). 31 The fact that k0 must be rised by a fall in r is not so obvious (Hicks, 1985, pp. 147– 148, and 1973, pp. 17–21). 32 See also Hicks (1973, section 8.6). 33 Among them we can mention: Morroni (1992); Piacentini, Chapter 9, this book; Scazzieri (1993). In the last work a thorough survey of this line of research can be found. 34 See, for instance, Metcalfe (1989, p. 54), Nelson and Winter (1982, p. 44). 35 For further indications see Nelson and Winter (1982, pp. 33–38), which concern ‘allies and antecedents of evolutionary theory’, and Saviotti and Metcalfe (1991). 36 See the bibliographies of Nelson and Winter (1982), Hodgson (1993), Metcalfe (1995) and Chapter 2, this book, Saviotti (1996) and Chapter 10,this book. 37 For analyses and comparisons see Metcalfe (1995). 38 See also Nelson (1987, p. 12). 39 Among the recent studies see, for instance, Carlsson’ (1995), Antonelli and De Liso (1995), De Liso and Metcalfe (1996). As for the less recent see Gille, Sahal (1981). See also the notion of ‘technological convergence’ by Rosenberg (1976) and the analytical framework proposed by Lange (1965), both relevant in this respect, not to mention the work in the classical track by List (1841). 40 Which often can be easily incorporated in machines.

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INTRODUCTION 43

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Kregel, J.A.Matzner, E. and Roncaglia, A. (eds) (1988), Barriers to Full Employment, London, Macmillan. Kuznets, S. (1974), Population, Capital and Growth, London, Heinemann. Lange, O. (1965), Wholes and Parts. A General Theory of System Behaviour, Oxford, Pergamon Press. Layton, E.T. (1974), ‘Technology as Knowledge’, Technology and Culture, Vol. 15, No. 1, January, pp. 31–41. Leoncini, R., Maggioni, M.A. and Montresor, S. (1996), ‘Intersectoral Innovation Flows and National Technological Systems: Network Analysis for Comparing Italy and Germany’, Research Policy, Vol. 25, No. 3, pp. 415–430. Leontief, W. (1986), Input-Output Economics (2nd edn), Oxford, Oxford University Press. Leontief, W. and Duchin, F. (1986), The Future Impact of Automation on Workers, New York, Oxford University Press. List, F. (1841), Das nationale System der politischen Oekonomie [Engl. tr. (1904) The National System of Political Economy, London, Longmans Green & Co.]. Lundvall, B.-A. (ed.) (1992), National Systems of Innovation, London, Pinter. Magnusson, L. (ed.) (1994), Evolutionary and Schumpeterian Approaches to Economics, Boston, Kluwer. Mankiw, N.G. (1990), ‘A Quick Refresher Course in Macroeconomics’, Journal of Economic Literature, Vol. XXVIII, No. 4, December, pp. 1645–1660. Marengo, L. and Sterlacchini, A. (1990), ‘Intersectoral Technology Flows. Methodological Aspects and Empirical Applications’, Metroeconomica, Vol. 41, No. 1, pp. 19–39. Marshall, A. (1920), Principles of Economics, London, Macmillan [repr. 1936, first edn. 1890]. Marx, K. ([1867], Engl. tr. 1976), Capital, Volume I, Harmondsworth, Penguin Books Metcalfe, J.S. (1989), ‘Evolution and Economic Change’, in A.Silberston (ed.) Technology and Economic Progress, London, Macmillan, pp. 54–85. Metcalfe, J.S. (1995), ‘The Economic Foundations of Technology Policy: Equilibrium and Evolutionary Perspectives’, in P.Stoneman (ed.) Handbook of the Economics of Inovation and Technological change, Oxford, Blackwell, pp. 409– 512. Morroni, M. (1992), Production Process and Technical Change: A Study of Industrial Technique and Production Unit Organization, Cambridge, Cambridge University Press. Nelson, R.R. (1987), Understanding Technological Change as an Evolutionary Process, Amsterdam, North-Holland. Nelson, R.R. (ed.) (1993), National Innovation Systems. A Comparative Analysis, New York, Oxford University Press. Nelson, R.R. (1995), ‘Recent Evolutionary Theorizing about Economic Change’, Journal of Economic Literature, Vol. 33, pp. 48–90. Nelson, R.R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., Harvard University Press. Nuti, F. (1991), I Distretti dell’Industria manifatturiera in Italia, Milano, Franco Angeli. Pasinetti, L.L. (1973), ‘The Notion of Vertical Integration in Economic Analysis’, Metroeconomica, Vol. 25, No. 1, Jan.–Apr., pp. 1–29. Pasinetti, L.L. (1981), Structural Change and Economic Growth, Cambridge, Cambridge University Press.

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Pini, P. (1992), Cambiamento Tecnologico e Occupazione. Analisi di Recenti Modelli di Disoccupazione Tecnologica, Bologna, II Mulino. Pugno, M. (1992), Roy F. Harrod: dall’Equilibria Dinamico all’Instabilità Ciclica, Bologna, II Mulino. Quadrio Curzio, A. (1967), Rendita e Distribuzione in un Modello Economico Plurisettoriale, Milan, Giuffrè. Quadrio Curzio, A. (1975), Accumulazione del Capitale e Rendita, Bologna, II Mulino. Quadrio Curzio, A. (1990), ‘Rent, Distribution and Economic Structure. A Collection of Essays’, Dynamis-Quaderni IDSE, No. 1. Quadrio Curzio, A. and Antonelli, G. (1988), ‘The Development of an AgroTechnological System between Market Pulls and Structural Pushes’, in G. Antonelli and A.Quadrio Curzio (eds) The Agro-Technological System Towards 2000. A European Perspective, Amsterdam, Elsevier. Reinganum, J.F. (1989), ‘The Timing of Innovation: Research, Development, and Diffusion’, in R.Schmalensee and R.Willig (eds) Handbook of Industrial Organization, Amsterdam, North-Holland, pp. 849–908. Ricardo, D. ([1821] 1951), ‘On the Principles of Political Economy and Taxation’, in The Works and Correspondence of David Ricardo, edited by P. Sraffa, Vol. I, Cambridge, Cambridge University Press. Romer, P.M. (1990), ‘Endogenous Technological Change’, Journal of Political Economy, Vol. 98, No. 5, Part 2, October, pp. S71–102. Rose, N.L. and Joskow, P.L. (1990), ‘The Diffusion of New Technologies’, RAND Journal of Economics, Vol. 21, No. 3, Autumn. Rosenberg, N. (1976), Perspectives on Technology, Cambridge, Cambridge University Press. Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, Cambridge, Cambridge University Press. Rosenberg, N. (1990), ‘Why do Firms do Basic Research (with their own money)?’, Research Policy, Vol. 19, No. 2, April, pp. 165–174. Rosenberg, N. (1994), Exploring the Black Box. Technology, Economics, and History, Cambridge, Cambridge University Press. Sahal, D. (1981), Patterns of Technological Innovation, Reading, Wesley Publishing Company. Sala-i-Martin, X. (1990), ‘Lecture Notes on Economic Growth: Introduction to the Literature and Neoclassical Models’, Cambridge, Mass., NBER Working Paper no. 3563, December. Salter, W.E.G. (1966), Productivity and Technical Change (2nd edn), with an addendum by W.B.Reddaway, Cambridge, Cambridge University Press. Samuelson, P.A. (1939), ‘Interactions Between the Multiplier Analysis and the Principle of Acceleration’, Review of Economic Statistics, Vol. XXI, No. 2, May, pp. 75–78. Sato, R. (1981), Theory of Technical Change and Economic Invariance, New York, Academic Press. Saviotti, P.P. (1996), Technological Evolution, Variety and the Economy, Cheltenham, Elgar. Saviotti, P.P. and Metcalfe, J.S. (1991), Evolutionary Theories of Economic and Technological Change, Chur, Harwood. Sawyer, M.C. (1985), The Economics of Michal Kalecki, London, Macmillan. Scazzieri, R. (1993), A Theory of Production, Oxford, Oxford University Press.

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Schmoller, G. ([1898], It. tr. 1904), Lineamenti di Economia Nazionale, Turin, UTET. Schmookler, J. (1966), Invention and Economic Growth, Cambridge, Mass., Harvard University Press. Schumpeter, J.A. ([1912], Engl. tr. 1934), The Theory of Economic Development, Cambridge, Mass., Harvard University Press. Schumpeter, J.A. (1935), ‘The Analysis of Economic Change’, Review of Economic Statistics, Vol. XVII, No. 4, May, pp. 2–10. Schumpeter, J.A. (1947a), Capitalism, Socialism, and Democracy (2nd edn), London, Allen Unwin. Schumpeter, J.A. (1947b), ‘The Creative Response in Economic History’, Journal of Economic History, Vol. 7, No. 2, November, pp. 149–159. Smith, A. ([1776] 1976), An Inquiry into the Nature and Causes of the Wealth of Nations, edited by R.H.Campbell, A.S.Skinner and W.B.Todd, Oxford, Clarendon Press. Solow, R.M. (1957), ‘Technical Change and the Aggregate Production Function’, Review of Economics and Statistics, Vol. 39, pp. 312–320. Solow, R.M. (1959), ‘Investment and Technical Progress’, in K.Arrow, S.Karlin and P.Suppes (eds) Mathematical Methods in the Social Sciences, Stanford, Stanford University Press. Solow, R.M. (1988), ‘Growth Theory and After’, American Economic Review, Vol. 78, No. 3, June, pp. 307–317. Sraffa, P. (1951), ‘Introduction’ to The Works and Correspondence of David Ricardo, Vol. I, Cambridge, Cambridge University Press. Steedman, I. (1985), ‘On the Impossibility of Hicks-Neutral Technical Change’, Economic Journal, Vol. 95, No. 379, September, pp. 746–758. Stoneman, P. (1983), The Economic Analysis of Technological Change, Oxford, Oxford University Press. Stoneman, P. (ed.) (1995), Handbook of the Economics of Innovation and Technological Change, Oxford, Blackwell. Storper, M. and Harrison, B. (1991), ‘Flexibility, Hierarchy and Regional Development: The Changing Structure of Industrial Production Systems and their Form of Governance in the 1990s’, Research Policy, Vol. 20, No. 5, October, pp. 407–422. Sylos Labini, P. (1969), Oligopoly and Technical Progress (2nd edn), Cambridge, Mass., Harvard University Press. Sylos Labini, P. (1984), The Forces of Economic Growth and Decline, Cambridge, Mass., MIT Press. Sylos Labini, P. (1990), ‘Technical Progress, Unemployment, and Economic Dynamics’, Structural Change and Economic Dynamics, Vol. 1. No. 1, June, pp. 41–55. Syrquin, M. (1987), ‘Growth Accounting with Intermediate Inputs and the Transmission of Technical Change’, Journal of Development Economics, Vol. 26, pp. 17–23. Ure, A. (1835), The Philosophy of Manufactures, London. Veblen, T. (1898), ‘Why is Economics Not an Evolutionary Science?’, Quarterly Journal of Economics, Vol. 12, July, pp. 373–397. Young, A.A. (1928), ‘Increasing Returns and Economic Progress’, Economic Journal, Vol. 38, No. 152, December, pp. 527–542.

Part I Research paths in the economic analysis of technological change

2 Economic evolution and technology John Stanley Metcalfe

1 INTRODUCTION In this contribution I propose to review some recent developments in the evolutionary theory of technological change. While I shall deal almost entirely in terms of theoretical concepts it must be clear at the outset that advancing this approach to economic change will require detailed, and often historical, studies of firms and technologies. The distinguishing feature of the evolutionary approach is its emphasis on the open-ended nature of economic and technological development, by which I mean that there are no determinate positions of long run equilibrium to define positions of rest for an industry or economy. Evolution is not predictable. But this does not mean that it is chaotic or involves non-law-like development. Quite the contrary, there are quite strong elements of system coherence which guide the process of technological and market evolution. At the outset we ought to state that system coherence relates to two mechanisms. First, that of selection between competing varieties of products and their methods of production. Although selection is related to competition this is not the competition of the textbooks, as we explore below. Secondly, the development of technologies exhibits strong elements of path-dependent descent by combination and modification from prior technologies. This gives rise to the concept of canalized development; that is, development of technology along selfdefining and self-limiting pathways. The topics to be treated below are already beginning to illuminate three central questions in the economic development of modern industrial nations. They address the kinds of theories which can handle qualitative change and the processes by which it is generated, making sense of creative destruction. Secondly, they address historically useful theory, placing sequences of unique and non-repeatable events in a broader interpretive framework. History is a rich tapestry of variety and change, it is open-ended, it is the triumph of the unpredictable over dull regularity. Finally, they begin to address the nature of structural change, its sources and mechanisms. Steady growth with equal proportionate expansion of all activities is a chimera, there is no

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growth without development and development in its deepest nature is unbalanced. 2 EVOLUTION AND ECONOMIC BEHAVIOUR The discussion of evolutionary change by economists is not new: Schumpeter, Veblen, Marshall, Steindl, Alchian are distinguished contributors prior to the recent major contributions of Nelson and Winter. Their writings span more than three-quarters of a century but they have always been contentious, finding it difficult to make headway against mechanistic concepts and an ever narrowing focus to the study of equilibrium. It is important here to distinguish two quite different concepts: evolutionism and evolution. Evolutionism entails teleological theories of historical change, that unique inner logic which gives history a direction, in the case of Marx and Spencer towards more perfect or more complex economic structures of society. These discredited doctrines must be distinguished sharply from evolution proper, as in the Darwinian theory of populational change, which involves a process of selection across competing varieties of entity. Such evolution has no direction, it does not progress in any obvious sense, it simply involves change. The principle features of modern evolutionary theory are two in number. The population perspective (a concern with frequencies of phenomena and their distribution, and a dualism between the unit of selection and the selection environments operating at different economic levels), with variety in the units of selection providing the material on which selection operates. From this perspective evolutionary economic change involves all of the following mechanisms: mechanisms for generating economic variety so that members of the relevant population differ with respect to the characteristics which convey selective advantage; heredity mechanisms, copying mechanisms to ensure continuity, and substantial stability, over time, in the make up of the units of selection; selection mechanisms which distinguish between members of a population according to their ability to grow within that environment. Together these three mechanisms lead to the idea that the structure of a population becomes adapted to its environment and determines the nature of the environment in the process. Often this is given the label ‘adaptation’, but we need to distinguish at least three different kinds of adaptation all of relevance to evolutionary theory (Toulmin, 1981). Calculative adaptation occurs when units of the population change their behaviour to fit the environment, typically in terms of rational responses to different environmental signals. Developmental adaptation occurs as units develop in a particular interval way over time which influences their environmental fit, an approach typified in life-cycle theories of organizations. Finally, populational adaptation involves the change in the structure of a population in response to competitive selection. Notice that the first two kinds of adaptation involve internal change in the units of selection,

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while the last entails changes in the relative frequency of different units in the population. The units of selection don’t evolve, the population does. In the natural world this third adaptive mechanism is often described in terms of blind variation and selective retention (Campbell, 1987), or, more loosely, as chance and necessity (Monod, 1964). Blind variations in the units of selection are produced without knowledge of their selective properties, they are generated independently of environmental conditions, and errors in internal adaptation do not feed back to influence the future generation of variety. Selection is then trial and error without learning, the retention in the population of entirely contingent developments. As Elster (1983) has clearly indicated, change is myopic and leads to local optima with no concept of present sacrifice to gain future selective advantage. For the study of evolution in the economic sphere this is clearly too narrow an approach. Intentional behaviour is important; selective units such as firms can understand their environment and search for behaviours which improve their selective fit, they can anticipate future changes in their environment, and seek to modify the environment to their own advantage. They also have the ability to learn from past selective experiences of themselves and others, and this element of memory provides a Lamarckian basis for evolution in economies. However, these important differences between the economic and natural spheres do not weaken the relevance of evolutionary theory to economics; they considerably enrich it. They identify the key issue as the differential behaviour of economic units based upon their imperfect capabilities to adapt internally to their environments. In a world of uniform behaviour there would be no scope for selection because there would be no economic variety. The foundation of differential behaviour is to be found in the processes which generate, accumulate and disseminate knowledge. Costly processes for information acquisition and limited information processing capability imply that choice sets are partial not comprehensive. Creative abilities imagine different behaviours from those already experienced, while the differential capacity to learn means that the same data leads to knowledge of different kinds. These points apply to individuals and a fortiori to the organizations and institutional structures within which individuals act. Indeed the role of organizations is crucial, for as firms they are important units of selection in an economy. Organizations filter and pool the knowledge of their individual members to generate a collective knowledge base, on which its senior decision-makers base their actions. Different organizational structures generate different communication codes and different mechanisms for accumulating knowledge, both internally and by scanning the external environment. Moreover, organizational structures are not plastic but introduce degrees of inertia which limit internal adaptive response and reinforce the tendencies towards differential behaviour and the generation of economic variety. There is, of course, no conflict between rational, optimizing behaviour and evolutionary processes. Faced with choices, individuals and organizations

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choose, presumably, to gain the best outcome as they perceive it. But choice considered at this level is entirely mechanical; the fundamental question is what creates the range of choice-set over which rational calculations can be made. As Shackle (1961) has emphasized, genuine choice involves the construction of the list of alternatives. In this respect there is no more damaging friction that that of economic agents who are information perfect, for such a proposal rules out all change which is of significance in real economic situations. In modern economics, the division of labour reflects and reinforces the fundamental fact that individuals and organizations differ in what they know and in their capacity to learn and in their creative insight. To the extent they optimize they do so locally not globally. They live in one world but perceive and imagine different worlds, and it is on this basis that economic evolution proceeds. 3 TECHNOLOGY AND ITS CHARACTERISTICS It is hardly surprising that the analysis of technology plays such a central role in evolutionary theory for it is one of the chief routes by which the world’s economic furniture develops over time. Innovations and their diffusion play a major part in generating structural change. Moreover, the development of technology involves issues of learning and cumulative memory formation which introduce the elements of Lamarckian drift, path dependence and irreversibility into the evolutionary process. Every technology is a transformation process, an activity for turning materials, energy and information as one form into more highly valued forms. Work is done and value added is created by the transformation process. It is fundamental to appreciate that the technological context of any transformation process exists at three distinct levels: knowledge, individual skills and organizational competencies, and artefacts (Layton, 1974). The theory of evolutionary change has to attend carefully to all three levels. The artefact level, products and their methods of production, is the primary level of economic selection, the focus of competition in the market place. Moreover, all artefacts can be described in terms of product or process performance characteristics, the attributes which determine the value of the product and its costs of production, and the technological characteristics which underpin them. These provide the dimensions on which variety can be mapped and technological change measured over time (Saviotti and Metcalfe, 1984). The skills and competencies level is concerned with the ability to act, to design, develop and produce the artefacts in question. This inevitably involves more than technological knowledge proper, for many of their attributes of a technology are organizationally continent. Finally, the knowledge level deals with the ideas, facts, concepts and theories which generate an understanding of the transformation process. It is at this level that scientific understanding is brought into contact with technology, although the ability to act is often ahead of the

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ability to explain scientifically the properties of a transformation process. The mechanisms for changing technology differ very considerably between the three levels, involving different institutions, different regimes of appropriability and different mechanisms of accumulation with different cost structures. To talk of technical change simpliciter is always misleading unless the levels of technology are carefully distinguished. These distinctions lead us directly to the view of technologies as complex structures of facts, concepts and ideas, with trajectories of advance limited by the inner structure of relationships with the technology. These structures become self-limiting, narrowing the scope for change as the technology develops and promoting diminishing returns to further investments in technological advance. Organizations build comparative advantages in operating and developing a particular technology and often find it intensely difficult if not impossible to engage in a major shift in their technological activities. All of this bears directly on the generation of technological variety and on an understanding of the structure of technology as key elements in the evolutionary approach. 4 TECHNOLOGY AND THE PURSUIT OF COMPETITIVE ADVANTAGE In the remainder of this chapter I intend to deal with a supply side issue which does not normally appear under that heading; namely, strategy for technology. We shall treat strategy as the essential entrepreneurial dimension of an organization. It is commonplace to observe that the theory of competitive equilibrium finds no place for the entrepreneurial function, for equilibrium (with its given tastes, technologies and resources) rules out the variety which entrepreneurship creates. But, in the evolutionary context it is the key to change, the source of differential behaviour, and the locus of business strategy. We shall define a strategy for technology in an organization as a relationship between the development of technology and the pursuit of competitive advantage. Of course, technology is only one component of competitive advantage but over the longer term is has a dominant influence over the development of industries and firms, as any cursory acquaintance with recent economic history demonstrates. The way in which a firm positions itself in the spectrum of available technology, and the ability to develop its technology over time in all of the three dimensions discussed above, is of crucial importance. From an evolutionary viewpoint, strategy at the level of the firm, or more accurately operating business unit, is the outcome of the internal selection mechanism in that organization. In an evolutionary framework the units of selection are the business activities of firms, defined by sets of products and their associated methods of production. According to the price and performance attributes of these products relative to those of rivals, they will be gaining or losing ground in the marketplace.

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Depending on the costs of producing these products the profitability of the business unit is determined with the resource base for expanding capacity relative to rivals and for engaging in other forms of competitive rivalry. The attributes of the selection environment are thus crucial and relate to the nature of a firm’s customers, their sensitivity to differences between rival products and the frequency with which they make selection decisions (Metcalfe and Gibbons, 1989). From this perspective the business unit has three attributes: its efficiency and effectiveness, as measured by the characteristics of its products and production methods; its fitness, as measured by its ability to deploy profits to expand production and marketing capacity; and its creativity—its ability to transform its underlying product and process technology. Now our concern with strategy is predominantly with this latter question of creativity: the ability of the firm to maintain a momentum of technological change relative to its rivals. For it is the firm’s strategic vision of where it may go in terms of technology which determines the framework within which a momentum of change can be developed. Of these three dimensions of firm performance it is obviously the creative one which is of greater long-run significance for the survival and competitive position of the business unit: in the long term competition is very much a question of the survival of the wisest. But the long run is a cumulation of short-run events and here the roles of efficiency and fitness should not be underrated. Too great an emphasis on creativity at the expense of efficiency may bankrupt the firm or at least undermine its resource base and hamper its creativity. Too little emphasis on fitness may mean the firm losing market share and again limit the resource base for future creativity. Although we focus attention on the business unit as the basic unit of the selection process, it is its products and processes which are the direct subjects of market selection. To paraphrase Dawkins (1985), the artefacts are the replicators, the entities of which copies are made in the production process, and the business unit is the vehicle, the propagating mechanism for the particular technology. Business units are only normally equivalent to firms in the case of smaller enterprises. The modern larger firm is typically composed of an aggregation of business units, in which the centre stands as an umbrella organization, providing some business services and acting as an internal capital market. Firms, in this sense, are also subject to selective pressure in the broader capital market through mergers and acquisition. The crucial question then arises of strategy formation at the unit level and strategy formation for the firm as a whole and the potential conflict which can exist between the two levels. In this context the organizational structure of the firm is an operator, translating the skills and knowledge of the individual members into a collective competence. The organizational operator is defined in terms of rules of communication, both internally and with respect to the outside world, which filter, transform and store knowledge in the organization. They constitute what Arrow (1978) has called a code of communication, which determines who

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communicates with whom, about what, with what authority, and with what frequency. As Arrow indicates, there is no one operator which is best in all circumstances and firms must be expected to vary considerably in their internal codes. Since the operator is highly specific to a given firm it is not surprising to find that the same individual may perform quite differently in different firms, or that attempts at technology transfer or joint ventures between firms often run into severe difficulties relating to the lack of connectivity of different codes. Moreover, while leadership is always important within firms it is also operator dependent, the difference between success and failure in entrepreneurship not only depends on the attributes of individuals but also upon the organizational operators with which they work. Once we see the organization in this light, some immediate issues are raised. They include the openness of the organization to external information and the way in which this is allowed to mould its competence; the capacity of the organization to systematically explore its technological and market environment by entertaining different conjectures about its future; and the ability of the organization to learn from its errors. All of these issues are well recognized in the literature (Morgan, 1987) and often recur in debates about the relative creativity of large and small organizations or about the tension between the organizational requirements of efficiency and creativity. The notion of the organization as competence-defining operator has a further dimension; namely, as a competence-changing operator, a competence to change competence. In this respect we shall emphasize its crucial role as an internal selection environment which determines the evolution over time of the firm’s knowledge base and its revealed technological performance. This brings us immediately to the question of technology strategy, drawing upon our triad of concepts. 5 TECHNOLOGY AS STRUCTURED `KNOWLEDGE': SOME CONCEPTS It is elementary that every business unit must specialize to survive; it cannot do everything, and if it is to be even moderately efficient it must eliminate debate about fundamentals at an early stage in its development. The question then arises of what mechanisms provide the necessary forms for its activities, while permitting the requisite creativity to ensure its long-run growth and survival. In a seminal paper, Dosi has suggested that technologies have paradigmatic qualities akin to the natural sciences, and defined a technological paradigm as a ‘model’ and a ‘pattern’ of solution of selected technological problems, based on selected principles derived from the natural sciences and on selected material technologies.

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(Dosi, 1982, p. 152, emphasis in original). Such a paradigm indicates fruitful directions for technological change, defines some ideas of progress, and has a powerful exclusion effect on the imagination of engineers and organizations. Similar ideas, referring more or less explicitly to Kuhnian paradigms can be found in other literatures. Thus Constant (1984) writes of the normal technology of a community of practitioners, a framework which shapes the incremental steps by which a technology is developed. Similarly, Wojick (1979) has used the concept of evaluation policies, that set of decision-making procedures which define the conceptual framework (administrative, social and economic as well as technological) within which technology is applied to the solution of problems. Each of these approaches is driving at an important point: that the accumulation of technological capability is not random but structured by technological and non-technological factors, permitting variety in outcomes while placing limits on permissible variety which can only be transcended by a technological revolution (Hagedoorn et al., 1988). However, for our purposes these approaches are not specific enough, either with respect to technology or the organizational context in which it is articulated. Discussion of strategy requires more precision. It will be useful at this point to distinguish technological design configurations from technological regimes. As we have already suggested a business unit is organized around some specific transformation process which embodies quite specific design principles as to the nature of the product and the method of its production. This interconnected set of principles defines the artefact rather precisely so we can distinguish a jet engine from a steam turbine or a petrol internal combustion engine. This set of principles we call a design configuration, defining precisely the purpose, mode of operation, construction materials and method of manufacture of the relevant artefacts. It defines the evaluation framework for the technology and it is within the configuration that specific design puzzles emerge. There is considerable evidence that the temporal sequence of innovations which trace the development of a configuration are linked to the patterns of puzzle formation. Sahal’s (1981) concept of technological guideposts, Rosenberg’s (1983) concept of technological bottlenecks, and Hughes’s (1983) concept of reverse salients all fit within this idea of the configuration as the puzzle-generating framework. This is more narrowly focused than the broader concept of a paradigm, but the key point is that it is the individual design configurations around which business units specialize. Notice that any design configuration may enable the production of a wide range of products, designed for different market segments, and that it may experience substantial improvements in revealed technological performance over time. From an evolutionary perspective on technological competition the notion of a design configuration plays a fundamental role, allowing the distinction to be made between competition within a design configuration and competition

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between design configurations. How are different design configurations to be distinguished? There are no watertight taxonomic rules to employ in this task. We suggest, however, that different design configurations produce products aimed at a similar purpose while basing these products around different but overlapping sets of design principles. Thus the diesel and petrol engine are different design configurations for the internal combustion engine; water jet and air jet looms are different design configurations for weaving cloth, and so on. Typically, different configurations have many elements in common but differ in crucial technological aspects which result in different revealed performance characteristics (Saviotti and Metcalfe, 1984). It is convenient, for descriptive purposes, to draw together different design configurations under the umbrella of a technological regime and to recognize a further level of competition between different technological regimes based on radically different sets of design principles. Thus, in a sense which we hope is clear, the turbojet is in a different regime from the internal combustion vehicle. The fundamental issue here is the quite different knowledge bases of the technologies in question and the great difficulty firms have in moving between regimes. At this level we thus have a more precise basis for distinguishing radical from incremental innovations (Utterback and Kim, 1986) and from changes which are competence enhancing or competence destroying (Tushman and Anderson, 1986). In sum we see design configurations as the fundamental units around which to organise the discussion of technological change. They have Dosi’s paradigmatic qualities but they are also equivalent to Constant’s normal frameworks of technological activity. Gradations of technological change then fall into place, from the incremental change within configurations, to changes in configuration, and, most radical of all, changes in regime. These latter correspond to paradigmatic upheaval, especially in Constant’s sense of radically recombining basic technological and scientific disciplines, the building blocks of technology, and lead to the creation of a new community of practitioners. The second difficulty with the technology paradigm notion is that its organizational context is not specific. We see design configurations as organizationally specific. The business unit has to organise scientific and technological disciplines in an appropriate way and focus them on generating the required competence with respect to revealed technological performance. This leads us to a different approach to the notion of a paradigm which relates more directly to the competitive process. This is the idea of a strategic paradigm, through which the firm connects its business objectives with its technological capabilities. It clearly subsumes the technical element, but it must also address broader market objectives and this can only be done in the context of a specific business unit. The paradigm is a framework which conditions the internal selection environment of the firm, and firms differ in revealed performance in part because of differences in strategic paradigm. Unlike a scientific paradigm or a technological tradition, a strategic paradigm only requires consensus within the

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business unit. The broad function of the strategic paradigm is to set out the technological vision of the business unit, that framework which separates desirable from undesirable developments in technology. Moreover, this paradigm is embodied in the decision-making structures of the organization in three crucial respects—namely, with respect to the determination of the options constituting the technological agenda; with respect to the choice of options from that agenda; and with respect to the model of implementation of the selected options. Thus the strategic paradigm is a framework for non-empty, creative decision. It contains ‘models’ of the business unit’s modes of interaction with its external selection environment, and it generates the particular puzzles which drive day-to-day activity. It also generates the examples or standard procedures for problem-solving within the strategy. Thus the strategy paradigm determines the nature of the business unit as an experimental machine and reflects the decisions of its top competent team (Eliasson, 1988). What kinds of experiments will be conducted by what methods, over what time-frame, and with what resource commitment to internal or external activity are the central strategic questions. Experimentation is in this sense a method for exploration of its chosen design configuration. From a social constructionist viewpoint this raises the familiar questions of interpretive flexibility and method of closure within the strategic paradigm (Bijker et al., 1987). It is this which is the crucial role of the strategic paradigm: to act as the generator of hypothesis at the interface of markets and technological capabilities. Thus the strategy paradigm embodies that unique competence concerned with the creativity of the business unit. Differences in the long-run competitive performance of firms, we argue, are inextricably related with their strategic paradigms. These set the vision and lead to the choice of the design configurations from which all else follows. Unless the internal selection mechanisms generate hypothesis and experiments consistent with the external selection environment, the business unit cannot expect to enjoy selective advantage. At this point the question naturally arises of what happens when the strategic paradigm fails either in a functional sense (the increasing difficulty of improving performance), or in a presumptive sense (with the emergence of new scientific evidence, or, more likely, different, superior design configurations operated by competitors). Our study has shown how the strategy of dividing commitments between an existing and a threatening design configuration is often followed but rarely successful. Neither strategic paradigms, organizational operators nor knowledge bases appear to be sufficiently plastic for this to succeed (Cooper and Schendel, 1976). Be that as it may, we argue that the overthrow of a strategic paradigm requires the development of different organizational structures, and that this is the test for such a revolution. Either a new business unit is established by an established firm, or the existing activities are radically restructured, or a new entrant business unit emerges, any or all of which are the sine qua non of a strategic paradigm change.

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6 CONCLUSION In closing, it is worth returning to our opening themes of evolutionary change and strategy as the interaction between competition, technology and organization. Although evolutionary change is driven by the technological variety it is the creative strategies of firms which stimulate that process. We have suggested that strategies have a localized paradigmatic quality within each organization and that they are embodied in the rules and structures which define the organization’s operator for translating skills into competence. This paradigm generates variety but it also constrains the kinds of varieties which can be entertained. Normal technology generates hypotheses which are essentially conservative and possess inertia in the face of change emanating from the external selection environment. BIBLIOGRAPHY Arrow, K.J. (1978), The Limits of Organization, New York, Norton. Bijker, W.E., Hughes, T.P. and Pinch, T. (1987), The Social Construction of Technological Systems, London, MIT Press. Campbell, D.T. (1987), ‘Blind Variation and Selective Retention, in Creative Thought as in other Knowledge Processes’, in G.Radnitzky and W.Bartley (eds) Evolutionary Epistemology, Theory of Rationality and the Sociology of Knowledge, La Salle, Open Court. Constant, E.W. (1984), ‘Communities and Hierarchies: Structure in the Practice of Sciences and Technology’, in R.Laudan (ed.) The Nature of Technological Knowledge, Dordrecht, Reidel. Cooper, A.C. and Schendel, D. (1976), ‘Strategic Response to Competitive Threats’, Business Horizons, pp. 61–69. Dawkins, R. (1985), The Blind Watchmaker, London, Longman. Dosi, G. (1982), ‘Technological Paradigms and Technological Trajectories’, Research Policy, Vol. 11. Eliasson, G. (1988), ‘The Firm as a Competent Team’, Mimeo, Stockholm, Iiesr. Elster, J. (1983), Explaining Technical Change, Cambridge, Cambridge University Press. Freeman, C. (1985), Technology Policy and Economic Performances, London, Pinter. Hagedoorn, J., Kalff, P. and Korpal, J. (1988), Technological Development as an Evolutionary Process, Amsterdam, Elsevier. Hughes, T.P. (1983), Networks of Power, Baltimore, Johns Hopkins. Layton, E.T. (1974), ‘Technology as Knowledge’, Technology and Culture, Vol 15, No. 1, January, pp. 31–41. Metcalfe, J.S. and Gibbons, M. (1989), ‘Technology, Variety and Organization’, in R.Rosenbloom and R.Burgleman (eds) Research in Technological Innovation, Management and Policy, Greenwich, Jai Press. Monod, J.(1964), Chance and Necessity, London, Penguin. Morgan, B. (1987), Images of Organization, London, Sage. Rosenberg, N. (1983), Inside the Black Box, Cambridge, Cambridge University Press. Sahal, D. (1981), Patterns of Technological Innovation, London, Wiley.

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Saviotti, P. and Metcalfe, J.S. (1984), ‘A Theoretical Approach to the Construction of Technological Output Indicators’, Research Policy, Vol. 15. Shackle, G.L. (1961), Decision, Order and Time in Human Affaires, Cambridge, Cambridge University Press. Toulmin, S. (1981), ‘Human Adaptation’, in U.Jensen and R.Harre (eds) The Philosophy of Evolution, Brighton, Harvester. Tushman, M. and Anderson, L. (1986), ‘Technological Discontinuities and Organization Environments’, Administrative Science Quarterly, Vol. 31, pp. 439–465. Utterback, J.M. and Kim, L. (1986), ‘Invasion of a Stable Business by Radical Innovation’, in P.Kleindorfer (ed.) The Management of Productivity and Technology in Manufacturing, New York, Plenum Press. Wojick, D. (1979), ‘The Structure of Technological Revolutions’, in G.Bugliarello and D.B.Doner (eds), The History and Philosophy of Technology, Urbana, University of Illinois Press.

3 Technical change and labour displacement Some comments on recent models of technological unemployment Paolo Pini1

1 INTRODUCTION In this chapter recent models of technological unemployment are discussed. These models basically differ regarding the reabsorption mechanisms they consider: studies which emphasize market forces focus on automatic mechanisms operating through price compensation effects; analyses based on the role of autonomous spending decisions by different social classes focus on nonprice compensation effects—income and technology multiplier effects. It seems useful to identify at least four theoretical approaches: the neoclassical approach stressing the operation of spontaneous market forces; the classical Keynesian framework based on the dynamic relations between demand growth and productivity growth; multisectoral analyses along the lines of the more traditional input-output analysis and the more recent structural economic dynamic for subsystems; finally the new approach focusing on the transition to new technological systems and emphasizing either the diffusion process or the generation process of new technologies. The various models examined, developing either partial equilibrium analysis or general equilibrium analysis, seem to share the vision according to which economic growth, itself induced by technical progress, does not necessarily guarantee the reabsorption of the labour force displaced by the adoption of new technologies. Technological unemployment, in fact, can emerge either because of insufficient growth of aggregate demand compared with potential productive capacity growth or because of changes in the sectorial composition of demand for goods and labour with respect to the existing productive capacity structure. In both cases, automatic market mechanisms do not seem to be strong enough to guarantee a growth equilibrium path, implying therefore a macroeconomic disequilibrium and/or a sectoral disequilibrium.

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2 TECHNOLOGICAL UNEMPLOYMENT VERSUS KEYNESIAN AND STRUCTURAL UNEMPLOYMENT Studies of technological change do not appear to have given a sufficiently clearcut definition of the concept of technological unemployment, compared with other categories of unemployment such as Keynesian unemployment or structural unemployment (Standing, 1984). In some cases, in fact, it is associated with unemployment due to scarcity of effective demand, while in other cases its structural nature is emphasized. In the first case, unemployment is explained by an insufficient aggregate demand growth compared with the potential supply of goods allowed by innovations which increase the productivity of labour. In the second case, unemployment is seen as determined, given technical progress, by structural changes in the productive capacity of the economic system; changes which manifest themselves, for example, in a physical capital structure incompatible with the full employment of the available labour force. However, these possibilities are not alternative, for they could occur together. In fact, given the low growth rate of demand and technical progress leading to changes in the composition of the demand and of the production structure, unemployment can emerge with both Keynesian and structural characteristics. The prevailing characteristics will depend on the relative intensity of different compensation effects which determine the reabsorption of the labour force initially unemployed as a consequence of the adoption of labour-saving innovations. If the compensation effects principally operate by stimulating the aggregate demand, technological unemployment will assume mainly structural characteristics when there are relevant changes in the composition of the demand and the supply of goods; on the other hand, if the stimuli to the demand due to technical progress are not strong, and there are compensation mechanisms instead which do not trigger the appearance of structural constraints or which actually lead to the overtaking of these possible constraints, technological unemployment will mainly assume Keynesian characteristics. The analysis of technological unemployment and of its specific nature thus cannot ignore the mechanisms of reabsorption and the forces which govern the compensation effects, reminding us of the well-known theory, or better theories, of compensations. Theories of compensations are based on the notion that technological unemployment not only implies a reduction of labour coefficients in the production processes but also involves adjustments in the optimal combination of production factors and in the level and composition of the demand for goods in response to changes in their relative prices, and in the income level and distribution. The analysis of compensation effects essentially regards variations in the volume and composition of demand, and thus in the production, induced by technological change itself.

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These compensation effects mainly occur in places, firms and industries different from those in which the innovative activity has initially taken place and where it has produced its impact effects. This suggests the necessity of a general equilibrium analysis, which can tackle the various compensation effects induced within the economic system by technological change itself. This analysis will also enable us to consider macroeconomic aspects that intervene in the relation between technical progress and employment, which cannot be investigated through a partial equilibrium analysis focusing on microeconomic aspects. 3 COMPENSATION EFFECTS Compensation effects have usually been split up into three types: price compensation effects, income compensation effects and technology multiplier compensation effects (Stoneman, 1983b). This distinction does not simply .have a taxonomic function, but it is rather based on important questions of theory and economic analysis, for the relative importance of the different effects varies according to the specific theoretical approach adopted. In fact, while the theoretical approach emphasizing the role of market forces in the reabsorption process of technological unemployment privileges the analysis of price effects— considering them as automatic mechanisms guaranteeing the equilibrium— the theoretical approach emphasizing the analysis of income and technology multiplier effects privileges the quantity adjustments and the phenomena of disequilibrium characterizing the dynamic of the economic system. Thus the debate between theories of compensations regards the question of whether the economic system presents sufficient reabsorption mechanisms of technological unemployment functioning automatically through market forces, or whether, within this process, economic factors intervene enjoying an important degree of autonomy with respect to the forces that are endogenously triggered by technological change itself. The functioning of the compensation effects previously indicated can be summarized as follows (Stoneman et al., 1982). Price effects mainly depend, given nominal incomes, on price reductions of goods due to the use of new technology with lower production costs (Heffernan, 1980; Neary, 1981). The transferring of cost reductions to prices, clearly, is not independent of the prevaling market structure, so that the price effects will be characterized by a different intensity according to the existence of a competitive, oligopolistic or monopolistic market (Katsoulacos, 1986, Chapters 2 and 3; Stoneman, 1987). These reductions stimulate the (domestic and/or foreign) demand for goods (directly in the sector where the new technology has been adopted and indirectly in other sectors), determining an increase in production and a reabsorption of the labour force temporarily unemployed. These compensation effects depend substantially, given the market structure, on the

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own- and cross-price elasticity of the demand for the various goods, and thus on their degree of complementarity and substitutability. Price effects assume a particular relevance in the context of an open economy. In fact, in such a case, innovative firms and sectors can achieve relative increases in productivity involving higher competitiveness for their product on foreign markets and thus gains in market shares (Whitley and Wilson, 1981; Cyert and Mowery, 1987). This results in stimulus to domestic production and growth, inducing compensation effects with respect to the initial labour displacement of new technologies. Economic theory also includes, in the category of price effects, those effects mediated not by changes in relative prices of goods but by changes in relative factor prices. In fact, if competitive markets for factors exist, on a local and a national scale, a change in the use of production factors that implies a decrease in labour demand induces changes in relative factor prices that can lead to a greater use of the factor initially saved. These effects will depend on the degree of flexibility of the technology in use, represented also by the labour/ capital elasticity of substitution (Wilson, 1984; Bosworth, 1987; Ilzkovitz, 1987). Income effects are those occurring when technological change determines, given the price level and the relative prices, an increase in nominal incomes and a change in their distribution. To the extent that these changes stimulate the accumulation process or induce an increase in the demand for consumption goods, generating additional labour demand, there may be powerful compensation effects on the aggregate employment level in the long run (Stoneman, 1984; Sylos Labini, 1987a, 1987b). Let us consider two extreme, but illuminating, cases. The first is when only profits benefit from those gains in productivity obtained through technical progress. The second is when only wages benefit from technical progress. In the first case, the increase in profits can lead to spending decisions which expand aggregate demand. These decisions can concern consumption goods, but above all the growth in profits can stimulate the investment activity. However, as these decisions mainly concern investment demand, an increase in profits does not necessarily lead to a growth in labour demand which may compensate the initial displacement effects. In fact, if the investment decisions depend on expectations about the future trend of the consumption demand based on current demand, and if technological unemployment involves negative effects on the wage bill and thus a decrease in demand, the economic stimuli to the use of higher profits may be insufficient, determining a low level of investment (Katsoulacos, 1986, Chapters 6 and 7). On the other hand, growth in wages can stimulate the investment demand, for it induces a higher demand for consumption goods. At the same time, however, in this case constant profits, together with growing nominal incomes and in particular growing wages, can induce negative expectation about the pattern of production costs and the future investment profitability, with negative effects on their pattern. In both cases the potential income compensation effects are restrained by the significant changes in income distribution. It is also important

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to consider the relations between the profits and the investment pattern and diffusion process of new technologies. A low level of investment demand due to steady profits can, in fact, negatively influence the diffusion speed of new technologies, with negative effects on other compensation effects—price effects and technology multiplier effects. It seems likely, however, that in dynamic economic systems these extreme cases do not constitute the norm and that intermediate situations occur, for which the positive effects on aggregate demand induced by growth in nominal incomes are not balanced by negative effects determined by radical and unanticipated changes in income distribution. The analysis of extreme cases is in any case useful for it highlights the fact that income effects, unlike price effects, are not solely governed by market forces functioning automatically due to the adoption of new technologies, but rather depend strongly on autonomous spending decisions taken by economic agents, based on expectations about the pattern of future demand and income distribution. Technology multiplier effects, finally, are those occurring when technological change is embodied in capital goods. These effects do not take place in firms and industries where process innovations are adopted but in firms and industries specifically devoted to the production of capital goods (Stoneman, 1984; Howell, 1985; Leontief and Duchin, 1986). The higher production of these goods determines additional labour demand in these sectors, acting, therefore, as a compensation effect with respect to the labour saving implied in the use of the new technology. The extent and timing of these effects mediated by investment decisions depend on the speed of introduction of new technologies into the economic system; that is, on their diffusion rate. It is important to observe, however, that this effect is not necessarily positive. This is due to different reasons regarding the following factors: (a) the investment pattern in traditional technologies which are replaced by the new ones; (b) the relative capital intensity in the use of the old and new technologies; (c) the relative labour intensity in the production of the old and new technologies. In fact, the substitution of new technologies for old technologies can induce negative net investment in capital goods embodying traditional technologies with negative effects on labour demand in production sectors of such capital goods. Consequently, the net compensation effects can turn out to be lower than anticipated, or even nil. Moreover, if the new technology presents a lower capital/output ratio in its utilization than the old one, the technology multiplier effects could even be negative. This could also happen when the two technologies differ in their production characteristics: for example, if the new technology is characterized by a lower use of labour compared with the old one.

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Up to now, we have analysed the identification of the technological unemployment reabsorption mechanisms based exclusively on the induced effects of technological change characterized by process innovation. However, this emphasis can lead to an underestimation of these mechanisms for it does not take into account the particular technological changes occurring through product innovation, which can trigger powerful reabsorption mechanisms (Blattner, 1981; Cooper and Clark, 1982). Although, as is well known, the distinction between process and product innovations is often an artificial one, for example in the case of capital goods, it is useful for analysing the compensation effects induced by changes in the level and composition of aggregate demand. In fact, if on the one hand product innovations can signal in some cases the final phase of the life cycle of a traditional product replaced by a new one, with a consequent decrease in labour input for their production, on the other hand they can also trigger new market demand and thus induce the use of additional labour. While a traditional product can be characterized by a declining market demand, until saturation level is reached, new products that substitute old ones can present a sustained demand over a long period, thus triggering powerful reabsorption mechanisms in the long run. New products can also present extremely different characteristics with respect to the existing products, so that it is important to focus not on their substitutability characteristics, but on the emergence of a previously non-existent market demand (Stoneman, 1983b, Chapter 11; Katsoulacos, 1984, 1986, Chapters 4 and 5). As regards process innovation, product innovation is likely to induce positive employment effects, particularly if associated with growth in nominal incomes. At the same time, product innovations also imply adjustments in the level and composition of demand and supply for goods which involve important structural changes in the economic system. These changes regard both the technical relations of production and linkages among productive sectors and the spending decisions and behaviour of different economic agents (Pasinetti, 1981). Another factor linked to technical progress which can induce structural changes in the demand and supply composition, is the effect of growing nominal incomes on the time allocation decisions between work, consumption, and human capital accumulation (Pasinetti, 1981; Armstrong, 1984). As technical progress induces through increases in nominal incomes a reduction in work time and thus a decrease in the labour supply, the unemployment level may suffer less from the possible negative effects of the diffusion of new labour saving technologies. At the same time, the increase in time devoted to non-market activities can induce an increase in the demand for goods consumed in these activities, and in general this effect involves modifications in the demand composition due to the emergence of new consumption patterns. This, in turn, can imply an additional labour demand, together with a change in the structure of the existing one.

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4 PARTIAL EQUILIBRIUM VERSUS GENERAL EQUILIBRIUM ANALYSES Thus, from the analysis of the different ways through which compensation effects operate, it is clear that the reabsorption process of technological unemployment does not necessarily occur in the same sectors in which labour displacement takes place subsequent to the adoption of a new technology. On the contrary, these effects can often involve a large number of different industries producing and utilizing new technologies, as well as other sectors. At the same time, during the adjustment process of the demand and of productive capacity, there may be time lags between the adoption phase of new labour saving technologies and the various compensation effects which affect many sectors of the economic system in different ways through channels which involve different economic agents— consumers and investors. Moreover, technological change does not involve a simple adjustment of the technological structure to the emergence of new technologies, but actual structural change affecting both the demand side and the supply side of different markets. For these reasons it is not likely that the extent and timing of compensation effects will be able to guarantee the equilibrium between labour demand and supply in each period. It is more likely that disequilibrium positions will prevail, varying in time with significant structural changes in the economic system regarding the demand and supply of final goods, capital goods, and production factors. In recent studies the analysis of the employment effects of new technologies has been carried out by means of two main approaches: partial equilibrium and general equilibrium analyses. Among partial equilibrium analyses, it seems possible to include theoretical and empirical studies using a ‘case study’ methodology at different levels of aggregation: that is, at the level of the production process, plant, firm, industry, geographic area, or sector.2 Partial equilibrium analyses, however, have serious limits, both at a theoretical-analytical level and at an empirical one (Kaplinsky, 1986). At the theoretical-analytical level, partial analysis is unable to capture the interactions between technological change and employment, interactions involving variables such as relative prices, production, income and demand. In general, the more specific the level of analysis, the less it is able to capture the manifold implications of the new technology on the employment level and structure.3 At the empirical level, employment effects analysed through specific case studies are often generalized at a large aggregate level using a doubtful methodology which emphasizes impact effects without considering indirect effects mediated by changes in demand and production. There are thus several important reasons for caution in the evaluation of the employment effects of new technologies on the basis of partial equilibrium analysis.

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The theoretical approach that seems better able to grasp the manifold labour displacement and reabsorption effects caused by technological change is that of general equilibrium analysis, both aggregate and disaggregate, as it is able to capture the precise employment impacts of the adoption of new technologies and the consequent compensation effects. This does not mean that partial equilibrium analyses are not useful in the investigation of the employment effects of new technologies, but simply that they are unable to provide a complete evaluation of these effects. On the other hand, partial equilibrium analyses are necessary as part of general equilibrium analyses, as long as they are not used to support directly macroeconomic conclusions, but rather to supply adequate data on the technological characteristics of the adopted innovations—for example, capital/ output, labour/output and capitallabour sectorial ratios—to be embodied in the structural parameters of a general equilibrium model. Partial equilibrium analyses can thus be complementary to general equilibrium analyses, so that models adopting this approach should not be neglected. However, general equilibrium analyses also have certain limits, usually stemming from the theoretical framework adopted. These limits concern the different role assigned to the various compensation effects and in some cases also an actual failure to take them into account. The features of different theoretical approaches adopted strongly influence the ways in which the various displacement and reabsorption effects are treated. Here we will examine these features. This examination will be based on the selection, from the wide range of studies on this subject, of those analytical models which, on the one hand, allow us to distinguish the characteristics of the approach on which they are based, and on the other hand can fruitfully compare each other in order to highlight any connecting links emphasizing ‘coexistent’ elements as well as ‘antagonistic’ ones.4 5 AN OUTLINE OF RECENT APPROACHES TO TECHNOLOGICAL UNEMPLOYMENT It is well known that the origins of the economic analysis of compensation effects can be found in the early nineteenth-century debate over the consequences of the introduction of machinery in the production process. The controversy over technological unemployment and reabsorption mechanisms largely reflects the opposition between two separate theoretical approaches—one based on the key notion of exchange and the other based on that of production (Quadrio Curzio and Scazzieri, 1986). The former assigns a central role to the market mechanisms which can guarantee the reabsorption of technological unemployment. This leads to an emphasis on price compensation effects, treated as automatic mechanisms operating through changes in the demand for goods. On the other hand, the approach based on the key notion of production focuses the analysis on non-automatic factors determining non-price compensation

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effects, and in particular on the accumulation process which is stimulated by income effects induced by technical progress, still maintaining its own autonomy with respect to the mechanization process. Subsequent economic analysis, and in particular more recent studies, shows a significant alignment along the two different ‘points of view’ of the classics. At the same time, important developments within the single approaches must be stressed, in some cases emphasizing the elements of differentiation, in others stressing those of complementarity. The neoclassical approach is certainly the one in which the analysis of reabsorption process stresses the operating of spontaneous market forces sustaining the achievement of full employment equilibrium. Within this approach, attention is mainly focused on price effects changing demand in different markets. Recent contributions (Heffernan, 1980; Neary, 1981; Stoneman, 1983b, 1987; Venables, 1985; Katsoulacos, 1986), however, do not acknowledge the traditional neoclassical view according to which the possibility of technological unemployment is excluded by the performance of automatic market mechanisms. Indeed, the identification of the specific necessary conditions in order that technical progress should not determine negative employment effects appears to stress—both within partial and general equilibrium analyses, in the short and long run—their significantly restrictive character, leaving open the possibility of technological unemployment in numerous cases. These conditions concern absolute and relative growth rates of factor productivity, share of each factor in the production process, factor elasticity of substitution, economies of scale, supply and demand elasticities for the product, and market structure. At the same time, it should be emphasized that the theoretical analysis here does not call into question the neoclassical model or the hypotheses on which it is based, but merely its specific structure determined by specific values of single parameters. For this reason, these studies confine innovative aspects to the evaluation of the extent of price effects, with a minor consideration of non-price compensation effects. With regard to this, recent analyses (Katsoulacos, 1986) have mainly focused on: (a) the relationships between market structure, demand elasticity and employment effects; (b) the relationship between demand elasticity and share in total income of the innovative sector; and (c) the employment effects of product innovation. First, it is shown that at the industry level the market structure does not affect the equilibrium employment level unless the magnitude of the demand elasticity is constant. Instead, if the elasticity changes when demand changes, the employment effects of process innovation depend on the specific prevailing market structure, determining the extent of price compensation effects. Second, using a general equilibrium model, it is shown that when technical progress is

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sector-specific the employment effects of process innovation depend on the relative magnitude of the sectorial demand elasticities. If the innovative sector has a higher (lower) demand elasticity compared with the non-innovative sector, the aggregate labour demand increases (decreases). Moreover, for employment effects to be positive, the share in total income of the innovative sector should be small. Were it large, the proportional change of its income share would be less than the proportional change of the expenditure for a unit of labour, leading to a decrease of labour demand. Thus, when technical progress affects (gradually and mainly) one specific sector with a relative high demand elasticity, the share in total income of this sector will increase, negatively affecting the probability of positive employment effects. Finally, using two different models of product differentiation (horizontal and vertical differentiation), the employment effects of product innovation are investigated. In this respect, compared to process innovation, product innovation seems more favourable for employment. With horizontal differentiation, the employment effect is nil only if the new goods and the old ones are perfect substitutes; but when non-perfect substitutability prevails, employment increases. With vertical differentiation, even if the products are perfect substitutes, employment increases (the substitution effect is overcompensated by the welfare effect determined by the existence of a new product of better quality). Two other theoretical approaches, which adopt a different framework from the one discussed above, are that based on the construction of vintage-capital simulation models (Clark and Freeman, 1987; Clark et al., 1987) and that based on the dynamic relation between demand growth and productivity growth (Sylos Labini, 1989; Boyer, 1988). The theoretical framework adopted is a classical Keynesian one which focuses not on the operating of market forces but on the role of autonomous spending decisions—investment and consumption—by different social classes. Both emphasize the dynamic substitution between capital and labour—in opposition to a static substitution such as of the neoclassical view—and a concept of technological change that is not performable through simple movements of, or along, a production function. Technological change is conceptualized as the result of a strongly selective, cumulative innovative activity, developed along specific technological trajectories. It is studied in terms of dynamic analysis, where the long-run expectations on technical-scientific progress and the relevant and unexpected changes (demand and supply shocks) modifying the expectations themselves are important factors in determining the direction and the rhythm of innovative process (Freeman and Soete, 1987). At the same time, the benefits of technological change do not spread through the economy by means of changes in relative prices of goods and reductions in their absolute level. Vice versa, if oligopolistic and monopolistic market structures for goods and labour prevail, technological change brings about increases in nominal income, for any price vector, and changes in income distribution between different social classes (Sylos Labini, 1983–84, 1985a, 1985b; Boyer, 1988) These approaches differ

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over their interpretation of technological unemployment: the vintage capital approach emphasizes its structural nature linked to changes in the structure of productive capacity deriving from the adoption of technologies with specific capital/output and labour/output ratios; the classical Keynesian approach emphasizes instead the interaction among nominal income growth, labour productivity and relative factor prices, stressing the role of aggregate demand growth in the reabsorption process of technological productivity. However, the approaches can be considered complementary: in order to explain the possible emergence of technological unemployment the first focuses on supply factors and the second on demand ones. At the same time, they both stress that the reabsorption process of this unemployment does not require the functioning of price compensation effects, but the functioning of income effects triggered by nominal income growth induced by technological change itself. Alongside these approaches, developing mainly an aggregate analysis, there are two other approaches privileging a disaggregate analysis: the input-output analysis (Magun, 1984; Howell, 1985; Young and Lawsonn, 1987; Leontief and Duchin, 1986; Whitley and Wilson, 1982, 1987; Kurz, 1987) and the structural economic dynamics based on vertically integrated sectors (Pasinetti, 1981; Schilirò, 1985). Although technological unemployment is considered by both to be an intrinsic characteristic of dynamic economic systems determined by the adoption of labour-saving innovations, the analysis of the reabsorption process of this unemployment follows different lines. In fact, the approaches have a common problem; that is, of rendering endogenous the compensation effects modifying the final demand vector, but they put forward different answers. In the input-output dynamic analysis, research appears to have privileged, at different stages, first the investment component of aggregate demand, and second the consumption and net export components of aggregate demand. With regard to investment demand, the multiplier-accelerator principle has been employed to link changes in potential and utilized productive capacity, capital stock and investment goods (Szyld, 1985; Duchin and Szyld, 1985; Leontief and Duchin, 1986). This has led to an emphasis on technology multiplier effects occurring when the level and structure of capital stock change to adjust to the adoption of new labour-saving and capital-using technologies. As regard the non-investment components of final demand, research has led to integrate the multisectorial model with macroeconomic relationships embodying specific hypotheses on microeconomic behaviour of economic agents (Whitley and Wilson, 1981, 1982, 1986, 1987; Hagemann et al., 1985; Kurz, 1987; Franke and Weghorst, 1988a, 1988b; Kalmback and Kurz, 1989). This has led to a focus on the analysis of the ways in which variations in production costs, relative prices and real incomes influence market demands and on macroeconomic relations within dynamic models which emphasize price compensation effects. In the structural economic dynamic approach, on the other hand, the analysis tends to privilege the functioning of income compensation effects to the extent that it identifies the main effect of technological change not in changes in relative prices but in

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nominal income growth. Changes in relative prices, in fact, are not considered an important phenomenon for the dynamics of the economic system in the long run, compared to the pattern of nominal income growth induced by labour productivity increases. The emphasis on income effects has also led to an investigation of the link between productivity increases, nominal income growth and changes in aggregate demand, in terms of both its level and its composition. In the structural dynamic model, technological change determines both changes in the structure of productive capacity and in the level and composition of demand, generating a growth path which may easily differ from the full employment growth path. In fact, given the independence of the learning processes in production and consumption activities, no automatic mechanisms driven by market forces guarantee the readjustment of the economic system to full employment equilibrium. The macroeconomic dynamic equilibrium condition (regarding the pattern of aggregate demand and the growth of potential productive capacity) and the sectorial dynamic equilibrium condition (concerning the growth of existing capital stock and the pattern of sectorial production and final demand) could both turn out to be unsatisfied and result in an economic dynamic which continuously generates negative effects on employment at aggregate and sectorial level. Finally, we will consider a series of different studies which focus not on the employment level in the final equilibrium but rather on the employment path during the transition from a traditional technological system to an innovative one. Two different types of models seem to emerge from recent literature, one focusing on the diffusion process and the other on the generation process of new technological systems. In fact, some of these studies examine the industry and macroeconomic implications of specific diffusion processes of new technologies (Stoneman, 1983a, 1984; Water-son and Stoneman, 1985); others consider the whole generation process of new technological systems using a neo-Austrian approach (Stoneman, 1976, 1983b, Chapter 12; Amendola and Gaffard, 1988). Both approaches emphasize the role of autonomous decisions taken by economic agents regarding the adoption and even the generation of new technologies on the one hand, and the demand for capital and consumption goods on the other. The models based on diffusion process suggest that the employment path during the transition phase depends mainly on five factors: (a) the technical characteristics of old and new technologies; (b) the diffusion speed of new technology; (c) the pattern of income distribution between wages and profits; (d) the distribution of output between innovative and non-innovative firms; (e) the prevailing market structure. In these studies, income compensation effects assume a major role in the reabsorption process of technological unemployment, while price compensation effects cannot fully operate because of non-perfect competitive markets for

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goods. The models based on generation focus instead on the time characteristics of the production and utilization of new technologies. These characteristics mainly concern the capital goods dimension in which new technologies are embodied, but they extend also to the human resources dimension when it is recognized that they are not perfectly transferable and are specific to a particular technological system. In this case, the learning process involving human resources constitutes a major factor affecting the feasibility of innovative processes. These various studies demonstrate that employment effects during the transition phase can diverge notably from those in the final equilibrium and that the specific employment adjustment path in the transition affects the net employment effects associated with the prevailing of new technological systems. This means also that results obtained through traditional comparative static analyses of initial and final equilibria should be treated with extreme caution, for they neglect the effects emerging in the transition phase. 6 RESULTS, LIMITS AND RESEARCH PERSPECTIVES From the examination of different recent approaches and models used to analyse the employment effects of new technologies, a widely accepted view seems to emerge: while there is agreement on the fact that technical progress plays an essential role in sustaining the growth of economic systems, at the same time it cannot be argued that economic growth, itself induced by technical progress, necessarily guarantees the reabsorption of the labour force displaced by the adoption of new technologies. Technological unemployment, in fact, can emerge both because of insufficient growth of aggregate demand compared with potential productive capacity growth and because of changes in the sectorial composition of demand for goods and labour with respect to the existing productive capacity structure. In the first case, a macroeconomic disequilibrium occurs and unemployment assumes Keynesian characteristics; in the second case, the disequilibrium is of sectorial type with structural unemployment. In both cases, however, automatic market mechanisms do not seem to be strong enough to guarantee a growth equilibrium path. Moreover, Keynesian and structural disequilibria could even emerge together. As an initial approximation, it can be said that the different approaches seem to differ substantially over the relative importance given to price and income compensation effects in the reabsorption of techological unemployment. The approach emphasizing market mechanisms tends to focus on the analysis of price effects, while the approach oriented towards quantity adjustments considers price signals to be insufficiently efficient and thus privileges the analysis of income effects depending on the autonomous spending decisions of the different income earners. The technology multiplier effects, although they are not neglected in the different approaches, are not considered strong enough on their own to guarantee full employment. For, if new technologies need to show themselves to be

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superior to the old ones in terms of total labour productivity, the net use of labour in their production could hardly be superior to the labour saving resulting from them. At the same time, the technology multiplier effects could prove to be a factor that, instead of favouring the achievement of equilibrium positions, might reduce the feasibility of the technological unemployment reabsorption process induced by price and income effects. This might occur, for instance, when a specific physical capital structure imposes constraints on the potential growth of production, as shown in the vintage capital model considered above. It should be further observed that, in some respects, theoretical developments within the distinct approaches have led to an emphasis on ‘coexistence’ elements rather than ‘antagonistic’ ones. In the neoclassical approach, what is emphasized is how the existence of a non-competitive market structure or of quantity constraints on the demand or supply side of markets for goods and labour can significantly reduce the effectiveness of price effects (Katsoulacos, 1986). Within the input-output dynamic approach, on the other hand, the efforts to endogenize the final demand vector concerned not only the investment component through the multiplier-accelerator principle but also the consumption and foreign demand component, considered in relation to the relative price vector and to the real income of economic agents (Whitley and Wilson, 1982, 1987; Kurz, 1987). This led to a privileging of analysis of price effects compared to income effects. These different developments, then, seem to have led not so much to an emphasis on elements of differentiation between approaches as to an identification of common fields of research embodying in specific models elements that are typical of the alternative approaches. The models considered preserve, however, very different characteristics according to the specific theoretical framework adopted, re-proposing significant ‘antagonistic’ elements. Thus the identification of two alternative sequences characterizing the reabsorption process of unemployment through changes in market demand seems confirmed. The first sequence is the following: A. ‘technical progress productivity increases cost reductions price reductions demand growth’; while the second one is: B. ‘technical progress productivity increases cost reductions nominal income growth demand growth’. The first sequence is the one typically adopted by the neoclassical approach, within the competitive model. The effectiveness of price compensation effects acting through this sequence depends crucially on the different own- and crossprice elasticities of demand, on factor elasticity of substitution, on economies of scale, and on technical progress bias. This sequence can be extended from goods markets to factor markets in order to consider wage adjustments when disequilibrium characterizes the market for this production factor. In this case, the negative excess of demand triggers a process of wage reduction continuing until the greater incentive to employing labour results in the reabsorption of technological unemploy-ment. This process depends obviously on the degree of

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wage flexibility as well as on technological characteristics of the production processes; that is, their degree of flexibility in the relative utilization of factors. Instead, the second sequence is based on two elements which characterize the functioning of economic systems not taken into account in the previous framework—the existence of non-perfect competitive markets for goods and labour, and the behaviour of economic agents in response to variations in nominal incomes. These elements are used to develop models in which, when the price vector is not perfectly flexible, the benefits of technical progress spread throughout the economic system through changes in the level and distribution of nominal incomes. The analysis of compensation effects occurring through changes in nominal incomes allows various reasons to be put forward for treating the reabsorption process not solely as the result of spontaneous market forces. In the first place, it is worth noticing that this process is not independent of the way in which nominal income growth benefits the various social classes; namely, wage earners and profit earners. In the second place, it should be stressed that this process also depends on the composition, as well as on the level, of final demand changes in response to nominal income growth. If productivity increases result mainly in profit growth, different factors shape the possible sequence ‘profit growth investment demand increases technological unemployment reabsorption’. Among those identified in the economic analysis, it is worth noticing the following: (a) expectations on demand pattern affecting the choice between treasuring and investment; (b) how investments are made, i.e. whether they privilege productive capacity growth leaving the factors’ utilization ratio unchanged, or whether they tend to modify this ratio towards a particular direction; (c) the consequences of these investment decisions on the growth and composition of incomes and thus on final demand. When productivity increases result mainly in wage growth, the possible sequence ‘wage growth demand growth technological unemployment reabsorption’ is principally affected by changes in the level and composition of the final demand, given the growth of labour incomes, and thus by the spending decisions of consumers. In both cases, those models privileging the analysis of income effects point out that decisions taken by consumers and investors can lead to the non-fulfillment of both the macro-economic dynamic equilibrium condition regarding final demand and the potential income and sectorial dynamic equilibrium conditions regarding the growth of sectorial productive capacity (capital accumulation) and the composition of demand for consumer goods. However, there are important limits to both the approaches, although with different degree, regarding the conceptualization of technical progress. Generally, most of the models discussed adopt the traditional approach to the study of the effects of technological change according to which economic systems adjust in a

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passive manner to changes in technology. Technological change is conceptualized as fully defined and developed, and is commonly identified as a new and more efficient production process to be embodied within the existing production structure adjusting to the new technique. Technological change, then, both remains a dimension which is exogenously determined with respect to the economist’s model and is not seen as interacting with the production structure within which it is embodied, and in particular with the economic stimuli generated by it. This view of technological change is thus radically different from the more recent view according to which technological change is a process through which a new technology is gradually developed, in interactive ways, in response to specific economic stimuli emerging from a socio-economicinstitutional environment (Rosenberg, 1982; Dosi, 1982; Nelson and Winter, 1982; Nelson, 1987; Amendola and Gaffard, 1988). Some recent analyses of the employment effects of new technologies are thus based on the idea that a technology can be fully specified in terms of technical coefficients from which it is possible to infer its relative superiority with respect to its predecessors, in terms of productivity or distributive variables. This seems particularly true for those theoretical frameworks which represent technology by the existent technical relations of production. This is the case, for example, of the traditional neoclassical approach based on a production function with variable or constant coefficients, and of the dynamic multisectorial approach in which technology is represented by exogenously determined production coefficients. This view is also adopted in the analytical approach of the structural economic dynamics, where not only the initial production coefficients are given but even their specific sectoral patterns over a period of time; that is, the rates of sectorial productivity increases are exogenously determined with respect to the economic forces. At the same time, it does not seem to operate any distinction between technical progress (seen as the emergence of new technical opportunities of production) on the one hand, and technological change (seen as the progressive adoption and diffusion of these opportunities in the economic system), on the other. In particular, technical progress, although considered to be one of the fundamental forces of the economic development, is taken as given, external to the economic analysis. As well as change in consumer preferences, it is traced back to the general learning process, which has characteristics of irreversibility. However, while the learning process in consumption activity determines changes in the pattern of consumption demand—modifying its sectoral consumption—the learning process in production activity involves ‘only’ technical progress at a constant rate. The latter does not need to be uniform in every sector. When we focus on learning in production the emergence of new sectors can be identified particularly when we consider product innovation. The learning process in production, considered in this way, loses one of its essential characteristics emphasized in recent studies, i.e. its localized nature. The analysis which emerges from this framework seems theoretically reductive with respect to that aspect of

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the model—technical progress—which links the analyses of demand and of production. An attempt to endogenize technological change can be found instead in aggregate models which present some classical Keynesian characteristics (Sylos, Labini, 1989). In this case, technical progress resulting in productivity increases is principally determined by economic stimuli within a specific institutional environment. The rate of demand growth, changes in the relative prices of production factors and goods, the structure of specific markets and the degree of flexibility/rigidity of factor prices and goods prices are all factors which contribute to determining both the rhythm and the direction of technical progress. However, some limits characterizing the treatment of the determinants of the adoption process within these aggregate analyses cannot be ignored. In fact, the exclusive emphasis on demand factors and production-related factors lead to a neglect of important mechanisms emphasized by the literature on technological change, such as focusing and selection devices triggered by supply factors and by the interaction between the latter and demand factors. From this point of view, then, the integration between the analysis of the determinants and the analysis of the effects of technological change is still very far from complete. Moreover, it should be noticed that in this model the emphasis is placed principally on the adoption of new technologies, rather than on their generation and development. From this point of view, this approach captures an important dimension of technological change, i.e. the diffusion process of new technologies and its determinants. The importance of the adoption and diffusion process, within the broader concept of innovative process, as a phase of this process significant both in terms of the identification of its effects and of the feedback on the generation and development phases of innovations, has been particularly emphasized (Metcalfe, 1981; Stoneman, 1983b, Part II). It is thus important to recognize that recent aggregate analysis of the employment effects is attempting to include within the model specific determinants of the adoption and diffusion path of innovation, even if the determinants considered mainly regard the demand side of the market, neglecting the supply side and the interaction between them. The diffusion process is also the centre of attention in many papers which do not adopt an exclusively aggregate approach in the analysis of employment effects of new technologies. For instance, it is considered in the two-sector model considering the macroeconomic effects of a specific diffusion path and in the oligopolistic model analysing price compensation effects at the industry level (Stoneman, 1983a, 1984; Waterson and Stoneman, 1985). In this case, the aim is not merely to examine the net effects in final equilibrium, but to analyse the employment adjustment path in the transition from the old to the new technology. The diffusion process is also examined in multisectorial dynamic models for forecasting changes in labour/output sectorial elasticities (Leontief and Duchin, 1986; Whitley and Wilson, 1982, 1987; Kurz, 1987). In this case, the analysis is aimed at verifying the consequences of different diffusion rates among different

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sectors, and of changes in them regarding the employment structure. However, there are also important limits to these studies. Specific diffusion processes, in terms of models and rates, are exogenously given and derive from the vast literature on the subject, theoretical in the first case and empirical in the second case, without any attempt to endogenize them in order to relate them to economic stimuli. Moves in this direction have been made in models adopting a neo-Austrian approach in which not only the diffusion process but also the generation process of new technological systems are emphasized (Stoneman, 1976, 1983b; Amendola and Gaffard, 1988). This process, however, is not confined to the dimension of physical capital production embodying the new technology, but, in some particular recent models, concerns the dimension of human resources, with a specific examination of the learning process. The determinants of the selection of a specific innovative process are in fact, on the one hand, the economic stimuli and the shocks coming from the demand side of the market and, on the other hand, the emerging constraints as to financial and human resources affecting the supply side of the market. In these models, the feasibility of a particular innovative process does not seem independent of the effects on production and employment deriving from its adoption. For, in the transition period from a traditional technological system to an innovative one, the effective pattern of the economic system in terms of realized production and employment changes both the demand conditions and the financial and human resources constraints affecting the adopted innovative process. Thus, in the transition phase, an interaction between the determinant and the effects of the innovative process seems to occur. From the examination of these analyses, then, at least two distinct research perspectives in the theoretical and empirical studies on the employment effects of new technologies emerge. The first research perspective aims at endogenizing compensation effects involving changes in the level and composition of the demand for goods. Within this perspective, two modalities are suggested by the theory: on the one hand, changes occurring through variations in price vector and, on the other, changes occurring through variations in nominal incomes. The simultaneous treatment of these modalities within a single model appears hardly feasible for they constitute crucial elements of alternative views. In the first case, it is assumed that benefits of technical progress spread throughout the economic system mainly through decreases in the price of goods; in the second case, instead, it is assumed that nominal income increases due to productivity increases, given relative prices and the price level. It seems reasonable to assume also that future developments in technological unemployment models will reflect these ‘antagonistic’ elements, emphasizing the action of market forces or, alternatively, the role of the autonomous spending decisions of the different social classes, profit earners and wage earners.

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The second research perspective is characterized by the simultaneous treatment of determinants and employment effects of the innovative process. This perspective, unlike the previous one, seems to be present to a lesser degree in recent studies, although the need for this type of analysis is often stressed. However, it clearly emerges that integration of the analysis of determinants and the analysis of effects of technical change can be attempted only through the development of models which focus on the transition phase from traditional technological systems to innovative ones, or, even better, on the generation process of the latter. Vice versa, the construction of models providing solely a static comparative analysis of the initial equilibrium position and the final position in which the new technology is completely diffused does not seem to be a research perspective allowing this integration between causes and effects of technological change. NOTES 1 Financial support from the Italian Ministry for University and Scientific and Technological Research (MURST) 40 per cent ‘Sistemi Tecnologici e Struttura Dinamica’ is acknowledged. 2 This classification is suggested and applied, for example, in the survey by Kaplinsky (1986). 3 In fact, generally speaking (a) the results obtained only show the direction and the extent of impact effects of new technologies on employment, while the indirect effects, operating through compensation effects in the short and long run, are ignored; and in particular (b) often the only effects considered concern the adoption of a specific new technology, while the impact that this technology could have on the production and utilization of other technologies and consequently on the related labour demand is disregarded (this seems to be the case for studies at process or plant level); (c) relationships between employment changes and output changes due to technological innovation are ignored, and this leads to a biased evaluation of employment effects (this seems to be the case for studies at firm or geographic area level); (d) the interindustry and intersectorial effects—which may be positive or negative—are not investigated, in terms of the substitutability and complementarity relationships between technologies in the production and utilization of production processes and/or final goods (this seems to be the case for studies at industry and sector level, cf. Kaplinsky, 1986). 4 There are a number of surveys on the quantitative effects of new technologies on employment which, although extremely useful for an assessment of the ‘state of art’, are generally more descriptive than interpretative. Moreover, most of them are principally concerned with empirical evidence, and neglect the analysis and evaluation of the theoretical framework. Others, instead, identify only some of the theoretical approaches adopted, and then focus on specific aspects. Most recent and interesting surveys are Rada (1980), Stoneman et al., (1982), Stoneman (1983b), Standing (1984), Freeman and Soete (1985), James (1985), Kaplinsky (1986), Cyert and Mowery (1987).

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BIBLIOGRAPHY Amendola, M. and Gaffard, J.L. (1988), The Innovative Choice. An Economic Analysis of the Dynamics of Economics, Oxford, Basil Blackwell. Armstrong, P.J. (1984), ‘Work, Rest or Play? Changes in Time Spent at Work’, in P.Marstrand (ed.) New Technology and the Future of Work and Skills, London, Frances Pinter, pp. 26–44. Blattner, N. (1981), ‘Labour Dispacement by Technological Change? A Preliminary Survey of the Case of Microelectronics’, Rivista Internazionale di Scienze Economiche e Sociali, Vol. XXVIII, No. 5, pp. 442–448. Bosworth, D.L. (1987), ‘Prices, Costs and Elasticity of Demand’, in OECD, pp. 127–173 . Boyer, R. (1988), ‘New Technology and Employment in the 1980s: From Science and Technology to Macroeconomic Modelling’, in J.Kregel, E.Matzner and A. Roncaglia (eds) Barriers to Full Employment, London, Macmillan, pp. 233–268. Clark, J. and Freeman, C. (1987), ‘Quantitative Analysis of the Future of U.K. Employment’, in C.Freeman and L.Soete (eds) pp. 223–236. Clark, J., Patel, P. and Soete, L. (1987), ‘Future Employment Trends in U.K. Manufacturing Using a Capital-Vintage Simulation Model’, in C.Freeman and L.Soete (eds) pp. 99–121. Cooper, C.M. and Clark, J.A. (1982), Employment, Economics and Technology. The Impact of Technological Change on the Labour Market, Sussex, Wheatsheaf Books. Cyert, R.M. and Mowery, D.C. (eds) (1987), Technology and Employment, Washington D.C., National Academy Press. Dosi, G., (1982), ‘Technological Paradigms and Technological Trajectories’, Research Policy, Vol. 11, No. 3, pp. 147–163. Duchin, F. (1984), ‘Automation and Its Effect on Employment and Income’, in E. Collins and L. Tanner (eds), American Jobs and the Changing Industrial Base, Cambridge, Mass., Ballinger Publishing Co. Duchin, F. and Szyld, D.B. (1985), ‘A Dynamic Input-Output Model with Assured Positive Output’, Metroeconomica, Vol. XXXVII, October, pp. 269–282. Franke, R. and Weghorst, W. (1988a), ‘Complex Dynamics in a Simple Input-Output Model without Full Capacity Utilization Hypothesis’, Metroeconomica, Vol. XXXIX, February, 1988, pp. 1–29. Franke, R. and Weghorst, W. (1988b), ‘On a Stabilizing but Chaos Generating Mechanism in Behavioural Functions: Observations in a Multiplier-Accelerator Model’, in P.Flaschel and M.Kruger (eds) Recent Approaches to Economic Dynamics, Frankfurt am. Main, P. Lang. Freeman, C. and Soete, L. (1985), Information Technology and Employment: A Study of the Recent Debate and Some of Its Policy Implications, Science Policy Research Unit, University of Sussex. Freeman, C. and Soete, L. (eds) (1987), Technical Change and Full Employment, London, Basil Blackwell. Hagemann, H., Katterman, D. and Kurz, H.D. (1985), ‘Technological Change and Employment in Advanced Industrial Countries. The Case of the Federal Republic of Germany’ , Universität Bremen, Technologischer Wandel und Beschaftugung, Working Paper no. 1, Mimeo.

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Heffernan, S.A. (1980), ‘Technological Unemployment’, M. Phil. Dissertation, Oxford University. Howell, D.R. (1985), ‘The Future Employment Impact of Industrial Robots: An InputOutput Approach’, Technological Forecasting and Social Change, No. 28, pp. 297–310. Ilzkovitz, F. (1987), ‘Capital/Labour Substitution and Its Impact on Employment’, Economic Papers, September, no. 57, Commission of the European Communities, Directorate General for Economic and Financial Affairs, Brussels, Mimeo. James, J. (1985), ‘The Employment and Income Distributional Impact of Microelectronics: A Prospective Analysis for the Third World, Technology and Employment Programme’, Working Paper no. 153, September, Geneva, ILO, Mimeo. Kalmback, P. and Kurz, H.D. (1989), ‘Microelectronics and Employment. A Dynamic Input-Output Study of the West German Economy’, Paper presented at the Ninth International Conference on Input-Output Techniques, Keszthely, Hungary, 4–9 September 1989, Mimeo. Kaplinsky, R. (1986), Microelectronics and Employment Revisited: A Review. A Report for the World Employment Programme, ILO, University of Sussex, Institute of Development Studies. Katsoulacos, Y. (1984), ‘Product Innovation and Employment’, European Economic Review, Vol. 26, No. 1–2, pp. 83–108. Katsoulacos, Y. (1986), The Employment Effects of Technical Change: A Theoretical Study of New Technology and the Labour Market, London, Wheatsheaf. Kurz, H.D. (1987), ‘Technological Change and Unemployment. An Outline of the Theoretical Approach by the Bremen Research Unit’, Technologischer Wandel und Beschaftugung, Universität Bremen, Technologischer Wandel und Beschaftugung, Working Paper no. 12, Mimeo. Leontief, W. and Duchin, F. (1986), The Future Impact of Automation on Workers, Oxford, Oxford University Press. Magun, S. (1984), ‘The Effects of Technological Change on the Labour Market in Canada’, Paper presented at the Canada Employment and Immigration Commission, May, Ottawa, Mimeo. Metcalfe, J.S. (1981), ‘Impulse and Diffusion in the Study of Technical Change’, Futures, Vol. 13, No. 5, pp. 347–359. Neary, J.P. (1981), ‘On the Short-run Effect of Technological Progress’, Oxford Economic Papers, Vol. 33, No. 2, pp. 224–233. Nelson, R.R. (1987), Understanding Technical Change as an Evolutionary Process, Amsterdam, North-Holland. Nelson, R.R. and Winter S.G. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., Harvard Univerity Press. OECD (1987), Information Technology and Economic Prospects, ICCP, n. 12, Paris, OECD. Pasinetti, L.L. (1981) Structural Change and Economic Growth, Cambridge, Cambridge University Press. Pini, P. (1991), Progresso Tecnico e Occupazione. Analisi Economica degli Effetti di Compensazione agli inizi dell’Ottocento, Bologna, II Mulino. Pini, P. (1992), Progresso Tecnico e Occupazione. Una Rassegna di Recenti Modelli di Disoccupazione Tecnologica, Bologna, II Mulino.

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Quadrio Curzio, A. and Scazzieri, R. (1986), ‘The Exchange-Production Duality and the Dynamics of Economic Knowledge’, in M.Baranzini and R.Scazzieri (eds), Foundations of Economies. Structures of Inquiry and Economic Theory, Oxford, Basil Blackwell. Rada, J. (1980), The Impact of Micro-Electronics, Geneva, ILO. Rosenberg, N. (1982), Inside the Black Box: Technology and Economies, Cambridge, Cambridge University Press. Schilirò, D. (1985), Effetti del Progresso Tecnico sull’Occupazione, Studi e Ricerche, Milano, Mediocredito Regionale Lombarde. Standing, G., (1984), ‘The Notion of Technological Unemployment’, International Labour Review, Vol. 123, March–April, pp. 127–147. Stoneman, P. (1976), Technological Diffusion and the Computer Revolution, Cambridge, Cambridge University Press. Stoneman, P. (1983a), ‘New Technology, Demand and Employment’, in D.L. Bosworth (ed.) The Employment Consequences of Technological Change, London, Macmillan, pp. 82–96. Stoneman, P. (1983b), The Economic Analysis of Technological Change, Oxford, Oxford University Press. Stoneman, P. (1984), ‘Technological Diffusion, Wage and Employment: The Macroeconomic Implication of Micro Based Diffusion Process’, University of Warwick, Dept. of Economics, Mimeo. Stoneman, P. (1987), ‘An Analytical Framework for Analysing the Impact of Information Technologies on Economic Perspectives’, in OECD (1987), pp. 70–93. Stoneman, P., Blattner, N. and Pastre, D. (1982), ‘Information Technology, Productivity and Employment’, in OECD, Microelectronic, Robotic and Jobs, ICCP, no. 7, Paris, OECD, pp. 17–131. Sylos Labini, P. (1983–84), ‘Factors Affecting Changes in Productivity’, Journal of PostKeynesian Economics, Vol. VI, Winter, pp. 161–179. Sylos Labini, P. (1985a), ‘Produzione, Produttività e Occupazione in Italia: le Prospettive’, Economia e Lavoro, Vol. XIX, No. 1, pp. 81–89. Sylos Labini, P. (1985b), ‘Teoria Keynesiana, Analisi Marginale e Occupazione: Brevi Note’, Economia e Lavoro, Vol. XIX, No. 2, pp. 81–90. Sylos Labini, P. (1987a), ‘Occupazione e Disoccupazione: Tendenze di Fondo e Variazioni di Breve Periodo’, Temi di Discussione del Servizio Studi, Novembre, No. 97, Roma, Banca d’Italia. Sylos Labini, P. (1987b), ‘Anche la Teoria della Disoccupazione è Storicamente Condizionata’, Moneta e Credito, Vol. XL, No. 159, pp. 247–301. Sylos Labini, P. (1988a), ‘Rendimenti Decrescenti e Prezzo del Capitale’, Moneta e Credito, Vol. XLI, No. 163, pp. 269–296. Sylos Labini, P. (1988b), ‘Can Europe Reach Full Employment Again?’, Labour, Vol. 2, Autumn, pp. 57–70. Sylos Labini, P. (1989), Nuove Tecnologie e Disoccupazione, Bari, Laterza. Szyld, D.B. (1985), ‘Conditions for the Existence of a Balance Growth Solution for the Leontief Dynamic Input-Output Model’, Econometrica, Vol. 53, No. 6, pp. 1411–1419. Venables, A.J. (1985), ‘The Economic Implications of a Discrete Technical Change’, Oxford Economic Papers, Vol. 37, No. 2, pp. 230–248.

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Waterson, M. and Stoneman, P. (1985), ‘Employment, Technological Diffusion and Oligopoly’, International Journal of Industrial Organization, Vol. 3, No. 3, pp. 327–344. Whitley, J.D. and Wilson, R.A. (1981), ‘Quantifying the Employment Effects of Microelectronics’, Warwick, Institute for Employment Research, Discussion Paper no. 15, December, Mimeo. Whitley, J.D. and Wilson, R.A. (1982), ‘Quantifying the Employment Effects of Microelectronics’, Futures, Vol. 14, No. 6, pp. 486–495. Whitley, J.D. and Wilson, R.A. (1986), ‘Information Technology and Employment: Quantifying the Impact Using a Macroeconomic Model’, Research Report, Warwick, Institute for Employment Research, Mimeo. Whitley, J.D. and Wilson, R.A., (1987), ‘Quantifying the Impact of Information Technology on Employment Using a Macroeconomic Model of the United Kingdom Economy’, in OECD (1987), pp. 176–220. Wilson, R.A. (1984), ‘The Impact of Information Technology on the Engineering Industry’, Research Report, University of Warwick, Mimeo. Young, K. and Lawsonn, C. (1987), ‘What Fuels U.S. Growth? Changes in Technology and Demand on Employment across Industry, 1972–1984’, Washington DC, National Academy of Science, Committee on Technology and Employment, Mimeo.

4 Broad production factors and technological systems Gilberto Antonelli1

1 PRODUCTION SYSTEMS, TECHNOLOGICAL PROGRESS AND PRODUCTION FACTORS Economic revolutions can be interpreted as induced changes in the relationship between natural and environmental resources, human resources and produced means of production, mediated by the production system and market relations. In other words, we can maintain with Quadrio Curzio and Scazzieri (1983) that such revolutions alter the connection between what they call the ‘transformation apparatus’ and what they call the ‘structure or interdependence apparatus’, bringing about a relatively more important role of the latter compared to the former. The transformation apparatus employs given production techniques, based on available technological information, and depends quite strictly on the original production factors.2 It basically aims at the production of consumer goods. We can infer that such an apparatus is peculiar to the first stage of the industrialization process, and tends to prevail within economic systems characterized by a low growth rate.3 Within economic systems substantially dependent on a transformation apparatus, the role and the pressure of natural resources and raw materials become crucial; therefore, in this setting it is quite difficult to overcome stages characterized by relative scarcity. The situation radically changes in economic systems where a structure or an interdependence apparatus prevails. The interdependence apparatus is constituted of industries producing means of production, where the core of technological progress has progressively concentrated. The great interdependence between industries producing means of production promoted the spread of technological progress, which has increased the ‘distance’ between final demand of consumption goods and the relative endowment of natural resources and raw materials. According to these general lines of reasoning the notion of the production system is based on the simultaneous existence and the interaction of the two

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apparatuses. However, the fact that the system depends on both production apparatuses and market power structure (Storper and Harrison, 1991) makes the concept of the production system become even more complex and dependent on institutional factors. Moreover, the increasing weight of knowledge and capabilities in the functioning of modern economic systems forces us to investigate more deeply the reproduction process of immaterial components. The mercantile revolution, which was started in the fifteenth century, brought about an increase in available resources within European economies, especially because of the new systems of sea transport. This stage of development was chiefly characterized by animal driving power in agriculture, by sea-driving power in transports and by scarcity of natural resources. The structure apparatus played a more important role with respect to previous stages because of the new systems of sea freight. The Industrial Revolution which followed in the eighteenth century brought about much more radical changes. The structure apparatus could benefit from a brand new driving power produced by machines. These developments had been strengthened at the end of the nineteenth and the beginning of the twentieth century by the introduction of electric power and of the internal combustion engine, up to the point that someone spoke of a second industrial revolution. The whole thing had a double effect on natural resources. On the one side, they were employed as a means of production much more than they were before and, on the other, their substitutability increased. These two aspects, taken together, can explain why a reshaping in relative scarcity occurred. At the end of the twentieth century another economic revolution is taking place. Innovations in integrated circuits, in information elaboration and telecommunications make us call it the ‘information revolution’. Though, since innovations actually have taken place in many different fields,4 the revolution could simply be called the ‘technological revolution’. The new form of power obtained by the structure apparatus is science itself: shares of mental ability are transferred to machines, as computers, robotics, genetic engineering and artificial intelligence confirm. These commercial and technological revolutions—however briefly and arbitrarily sketched—show a phenomenon that needs to be underlined. It concerns the deep changes which have occurred in the character of production factors and in their mutual relationships during the considered evolution. In the last stage new and broad production factors seem to have outpaced the traditional ones: environment, human resources and technology have been coupled with land, labour, physical capital. Their immaterial dimension, their discontinuous character and the crucial importance of the social dimension in their renewal and reproduction5 are evident. The new broad factors, or components, are heterogeneous and characterized by variety. They can be assembled in a wide range of specific combinations, according to the specific contracts and markets. Besides, one of the relevant joint

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products of virtuous productive process is the increase in their own endowment and variety. Both factual-historical and analytical-theoretical considerations enhance a joint treatment of the three components. Besides, it looks like such a joint treatment can develop our explanatory ability in practical matters too.6 The economic growth of industrialized countries has displayed, during the past twenty years or so, four main stylized facts: 1 the primary role achieved by highly skilled human resources and by intangible resources within productive processes; 2 the perception of complex and contradictory dynamic connections, rather than the presence of a univocal and direct conflict, between natural environment preservation and economic growth within a framework of increasing global risk; 3 the perception of the systemic character of technological change, increasingly based on interactions among agents and knowledge, and of the need to disentangle its effects on human resources and the natural environment from the ones linked to the international trade displacements; 4 the attempt, subject to the constraints set by the structure of the dominant technological system, made by the management of business enterprises and large financial groups to negotiate and control rules and constituent parts of the environment where they act, rather than limiting themselves to a passive conduct. Moreover, either the awareness about the complexity and the scale of interactions between human resources, technology and the environment, or the care about the entity of the resulting intergenerational effects are considerable. Side by side with these facts, analytical analogies justifying a joint treatment of the broad production factors can be underlined. Even if such analogies do not always imply uniformity in research programmes and methodology, they certainly press for an endogenous treatment of growth determinants. We can list these analogies as follows: (a) At the definition level, connections between technology, natural and environmental resources and the combination of individual working tasks into functions are pretty strong. The notion of input itself depends on both knowledge and technological diffusion, as well as on the degree of scarcity. (b) Economic literature has favoured, at least in the past, a treatment of these broad factors as if they were ‘black boxes’. The prevailing approach dealt mainly with the study of the effects of exogenously given phenomena, concerning the single factors. Instead, it would be better to use an approach where the demarcation of the research field, even if sharply needed from an analytical point of view, is based on a preliminary investigation of the real phenomena crucially affecting the theoretical assumptions.

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(c) Analyses have often shown how important the stocks of broad production factors are when we aim at studying the input flows and at exploring the boundaries between private costs/benefits and social costs/benefits, associated to the productive use of broad production factors (Kapp, 1963). (d) Finally, the broad production factors and their interactions seem to play an important role in the identification of ‘technological systems’,7 understood as networks connecting production factors, sectors and techniques; they also underline the weight of relative proportions and institutional factors either when different economic systems are compared or in the analysis of adjustment processes. 2 THE DYNAMIC NATURE OF PRODUCTION FACTORS UNDER CONTINUOUS TECHNOLOGICAL CHANGE: THE CONTRIBUTION OF THREE SPECIFIC PERSPECTIVES OF ANALYSIS Looking at the recent evolution of economic thought on the three broad production factors outlined in the first section, it is possible to point out a meaningful analogy. In every perspective of analysis the distinction between two different generations of studies is evident. And this distinction embraces both the core of theoretical analysis and applied research. Such a distinction is due to the inward evolution of economic theory, but especially to the need of explaining new real phenomena, of studying institutional and structural ‘anomalies’ taking place in the course of economic growth. This, of course, might imply the necessity of abandoning a systemic outlook or giving up the steady state equilibrium configurations. The distinction concerns quite different fields of analysis and it is often due to the need to focus attention on those characteristics of broad production factors that, at least in the framework of traditional theoretical paradigms, have never received a satisfactory treatment or a systematic observation. Furthermore, the evolution not only induced a greater specialization in studies but actually started an inclination to review the codified boundary lines between sub-disciplines according to new aggregation principles. One of the main outcomes of such an evolution is the growing awareness that pure theory often does not allow the attainment of deterministic solutions, independently determined by the specification of structural reference conditions.8 Bairoch (1988) maintains that structural changes which occurred in almost all domains of economic and social life during the last half century are more relevant than those which occurred during the previous two centuries. As a result, between 1948 and 1988, in Western Europe per capita GNP has grown 3.5 times, while it grew 3.2 times between 1750 and 1948. Also, the evolution of economic literature is probably affected by the awareness that those structural changes

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which determined such a growth have been matched by deep changes in relationships between economic agents and between human resources, technology, environment and economic activity.9 The growing awareness of the role of institutional and structural factors seems to bring about a whole set of approaches ranging between two poles: on the one hand we can observe several lines of research aimed at reconsidering and regenerating the analytical capabilities of the systemic approach,10 on the other hand we can track a huge number of scholars following a line of research centred on the ‘problem-solving’ approach.11 Another partition, not necessarily coincident with the previous one, refers to the role ascribed to knowledge in the economic sphere. In this respect, we can distinguish between research lines in which knowledge tends to be identified with information and research lines in which the focus is on the reproduction of capabilities. As will be argued in section 3, knowledge plays a pivotal role in the interaction among broad production factors. 2.1 Labour and human resources In the perspective of labour economics several scholars have emphasized the distinction between two generations of studies.12 The first generation mainly focused its attention on labour demand and labour force participation, mostly considering labour as a homogeneous factor and analysing it at an aggregate level. Labour market interactions were often considered as exogenous to the core of economics. During the second generation of studies, starting from the beginning of the 1960s, labour economics, while progressively stressing its linkages with general theory in the attempt to foster it in the interpretation of empirical phenomena, has not turned out to be just a particular field of application of the general theory itself. The still growing feeling is that, even starting from a specific sub-discipline, the study of topics concerning the employment, performance and reproduction of labour really promotes a better understanding and the solution of important general analytical issues. Moreover, on the whole, this generation of studies is characterized by a special concern for institutional factors, for agents’ performance in the labour market and for their interactions (Tarantelli, 1986). Such an inter-disciplinary attention is carried into effect through different strategies (Marsden, 1986). The emphasis granted to the discretional and rational behaviour of labour supply, to the intertemporal character of individual choices, is very significant and typical of this generation of studies. Research design in such a field is supported by a strong interaction between the refinement of the theoretical model, the implementation of estimation techniques, the understanding of observed behaviour and the assessment of economic policies.13 Context specificity can play an important role in this respect, especially when the model application is not too blunt.

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Studies concerning either supply or demand have paid special attention to labour heterogeneity. The various dimensions of heterogeneity analysed include those relating to the life cycle and to the training of human capital (Becker, 1975), or simply to the ability of signalling efficiency differences (Spence, 1974), or to the coupling of vacant jobs with workers (Holt, 1970a, 1970b), or to the peculiarities of the female working force (Bettio, 1988). 2.2 Physical capital and technology Technology has received more and more attention in economic literature vis-àvis physical capital in the last quarter of a century. Also, in the perspective of the analysis of technological change several works have stressed the distinction between two generations of studies.14 In the first generation technology was mostly conceived of as exogenous to the functioning of economic systems, and diversity in innovating behaviour was overruled. Apart from the notable and fruitful exceptions of the classics, Marx, Schumpeter and Kuznets, it is only from the second half of the 1950s that research programmes have tried to analyse in a systematic way the determinants and nature of technological change, both from a theoretical and empirical perspective. Analyses starting from very different origins15 have converged towards technological change as a crucial field of investigation, all making clear that the fully rational representative agent is often a misleading theoretical device. The width of such a research field allows me to point out, in the present circumstance, one thing only: that is, the existence of features that are very similar to the ones underlined when referring to the second generation of studies in labour economics. In particular, a strong persuasion is evident that advances in this specific field of study can contribute to the solution of important general analytical issues. Technology also plays an active role, as a control variable, in firms’ behaviours. This looks like enlarging the space of discretionary action, even if embedded in a specific context and subject to risk and uncertainty. Finally, a greater attention is paid to technological variety within either descriptive and explanatory16 or normative17 analyses. Because of the importance of firm theory in this perspective, it is also necessary to remember the distinction between the so-called ‘Industrial Organization and Public Policy’ approach, prevailing until the mid-1970s, and the so-called ‘New Industrial Economies’ approach, and those features the analysis of technology undertakes in these two approaches (Bianchi, 1989). In the second approach the relationship occurring between strategic conduct and performance is underlined in welfare terms, while the importance of the analytical scheme based on the triad ‘structure,’18 conduct, performance’ declines. Merely adaptive conduct is avoided and the ability of the firm’s internal organization to model itself on pre-set strategic targets is assumed. Agents’ behaviour leads to alternative choices in association with the different

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outcomes to be achieved when feedbacks occur. To a certain extent, the structure seems endogenously determined. 2.3 Natural resources and the environment The literature of global and forecasting models of the 1970s can be considered one of the first results of the enhancement of research programmes on the environment at national and supranational levels. As several studies make clear,19 a renewed attention to the problems of externalities due to economic activities on the environment had already emerged in the late 1960s. However, sectoral studies had already considered environmental issues.20 More generally, a brand new way of investigating the interactions between production and consumption processes, on the one hand, and the environment, on the other, has been developed. This can be interpreted as a mere consequence of the fact that it was only in the mid-1950s that the economic profession started to consider ‘collective consumption goods’.21 The analysis of environmental resources, of the externalities associated with them, and of welfare outcomes due to social choices concerning their employment, gains an increasing importance when moving from the so-called ‘public finance’ to the so-called ‘new public economies’ (Petretto, 1987). It was only recently that theories specifically concerned with natural resources and energy economics were developed. Previously, theories concerned with the generality of goods were mainly used. The same distinction between renewable resources,22 exhaustible or nonrenewable resources23 and environmental resources,24 as partly renewable resources, has been focused on only since the 1970s. In recent studies great attention has been paid to environmental variety on both normative and positive grounds. Many analyses have pointed out the need for a joint effort to be made by natural scientists, social scientists and lawyers for a better understanding of the comprehensiveness of the issues and, in particular, for a better evaluation of the services supplied by natural resources. The complexity of this latter task is emphasized by the need to consider, in addition to the economic values associated with the utilization of resources, the values associated with their nonutilization. Moreover, it is quite difficult to establish the real discount rate when an assessment of the actual or potential damages over time has still to be made. 3 BROAD PRODUCTION FACTORS AND KNOWLEDGE The notion of the technological system constitutes an attempt to outline an evolving synthesis of the main components of an economy: i.e. production systems, technological knowledge and institutions. The study of a

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technological system should lead us to a clearer understanding of the relationships which develop between production, technological competencies and social capabilities. While the production system is mainly characterized by a material basis, the knowledge and the social capability dimensions are mainly characterized by an immaterial basis. A technological system can also be considered as a set of linkages defined in the context of the interactions between the growth potential implicit in the system itself, and the realization of this potential (Antonelli, 1993). In this macroeconomic framework, technological knowledge is the knowledge developed in R&D laboratories together with the learning opportunities implicit in the stock of knowledge already existing in the system, which means that there exist unexploited opportunities which can be referred to as latent or tacit knowledge.25 Social capabilities—which can be conceived both as ‘factors’ (active elements) and ‘constraints’ (passive elements) that affect growth potential (Abramovitz, 1991)—consist of the actual availability of general and specific technical and cultural skills and of political, financial and industrial institutions. In an open technological system we can consider two kinds of environment. On the one hand, the system depends on elements which lie outside of it; on the other, there are environmental elements dependent on the system. In both cases there is only a one-way influence. Therefore environmental elements influencing, or influenced by, the system should not be considered as components of the system itself. However, over time technological systems tend to incorporate more and more environmental elements in themselves. There are three main implications of this way of reasoning with respect to the present analysis. A standard application of this scheme to the production process, based on the assumption that all elements are material objects, entails the derivation of a typical input-output system. However, the inclusion of immaterial objects among the active elements in the scheme has rendered more and more relevant the consideration of the interaction between artefacts, knowledge and skills (De Liso and Metcalfe, 1996). In addition, it must not be forgotten that natural and environmental resources also contribute to this interaction. Moreover, in considering the relationship between the technological system and its environment, we face a twofold problem. On the one hand, the system is circumscribed and distinct from the outside world and strictly connected to its specific context. On the other, in order to evolve and develop, it needs to be connected to the outside world, to be open even if only in a selective way. All this implies a trade-off between path dependence and commensurability. Thirdly intentional actions and intellectual ingenuity co-operate to generate economic values by transforming context-specific knowledge into general codified knowledge. The same meaning and working of the production function could be reinterpreted following this line of reasoning. This tends to shape the

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profile of the accumulation cycle of knowledge, leaving an important role to the theory of action (Rullani, 1995). ‘Embedded action’ is the resultant subject of study. Trade-offs often arise. The repeated phenomena of competition between rival technological systems of different vintages, and even the diffusion of ‘parallel systems’, can be seen as evidence of this. Experience, or the capability to extract and decodify the relevant elements from the context, is what is needed most. When the context is understandable in a relatively static and simple framework, routines and ‘static’ algorithms26 are adequate to support economic actions. But if the complexities of a context are not fully understood so that they can be standardized, an action of economic translation is crucial. Translation is based, apart from information, on knowledge and skills. In this microeconomic framework, knowledge is the intellectual and psychological power to derive new general principles and to develop them in the economic field with the help of science, culture and other sources of understanding. This process can follow both a deductive and an inductive chain with respect to context specificity. The amount of knowledge available to each agent depends on the extent of the knowledge present in the system, the accumulation of skills in the population, and the level and quality of existing artefacts.27 Moreover, knowledge can be acquired by investing in it.28 Skill refers to specific capabilities, abilities, credentials, experience and beliefs accumulated by humans by means of education, training, learning and informal practice. Formal as well as informal channels of learning are involved; this is why so-called ‘tacit knowledge’ is also relevant in this respect. They can be acquired both in general, context-specific and firmspecific activities. The amount of skill available to each agent depends on the extent of the knowledge present in the system, the accumulation of skills in the population, and the level and quality of existing artefacts. Skill can also be acquired by investing in it.29 Artefacts are both machines, products and the applied knowledge embodied in them. We argue that traditional analysis of production systems focuses mainly on this dimension.30 An input-output system can be considered as a possible representation of it. The effectiveness and productivity of artefacts available to each agent depend on the extent of the knowledge present in the system, the accumulation of skills in the population, and on the level and quality of existing artefacts. Of course artefacts can be acquired through investment. The same definition of natural and environmental resources is dependent on the knowledge, skills and artefacts prevailing in the system. Stocks and flows of materials supplied by nature, environmental beauties and the waste disposal capacity of nature, which are three dominant components of environmental services, are all strictly linked to the general characteristics of economic activities. Moreover, as the social attention on environment preservation issues increases, the knowledge base needed to manage the interactions between nature and economic activity also increases.

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In other words, new knowledge, on the one hand, contributes to the deepening of opportunities offered by the present setting on the other, and tends to augment the opportunities available to challenge uncertainty. Knowledge plays a pivotal role in the interaction discussed above. Besides pure knowledge, we also have to consider the skills which are not only the result of existing knowledge but which also create it, while artefacts supply a contribution as they are the outcome of the combination of knowledge, materials and labour. Even natural environment preservation and management imply huge amounts of knowledge. Knowledge is therefore a common ingredient of all the four components of system dynamics. In a dynamic framework of analysis, the interaction between the evolution of the potential for growth and the evolution of the actual growth is important. This interaction is originated by the active as well as the passive action of human resources on the basis of evolving institutions and social capabilities designed to reduce uncertainty and to facilitate choice. Capabilities can be defined as a mix of knowledge, experience, skills and the information needed by organizations to carry out ‘the large number of activities related to the discovery and estimation of future wants, to research, development and design, to the execution and coordination of processes of physical transformation, the marketing of goods, and so on’ (Richardson, 1972, p. 888). Capabilities are essential in explaining the development of the power to create and increase specific competencies, a relevant dimension of the division of labour. They are endogenous and idiosyncratic (Loasby, 1996, pp. 1–2), and both the cumulativity associated to the former and the context specificity of the latter stress the importance of irreversibility in this setting. Moreover, capabilities are also organized around different sets of rules in which private property works through different channels and in different ways. In other words they are institution-specific.31 A general framework of analysis is provided by Loasby (1996, pp. 2–8), who also refers to Richardson and Ryle. Two distinctions are stressed which relate capabilities to knowledge, the first one being based on the distinction between knowing that (‘knowledge of facts and relationship, the primary subject matter of formal education and the news; it may be sub-divided into knowing what and knowing why’) and knowing how (‘ability to perform the appropriate actions to achieve a desired result’).32 The focus is on the nature of knowledge. The second dimension is based on the distinction between direct knowledge (‘we know a subject ourselves’) and indirect knowledge (‘we know where we can find information about a subject’) (Rosenberg, 1994, p. 12). In this case the focus is on the relationship between the source of knowledge and the forms of its diffusion. It must be added to this that only some of the flows of new knowledge are characterized by a simultaneous impact on economic performance. From this we can induce that a further dimension of knowledge is to be taken into account in economic analysis. This dimension is focused on the time sequence between

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knowledge acquisition and its effects in the economic domain. In this respect we can therefore distinguish between knowledge as a stock that must be accumulated before being able to make any economic use of it, and knowledge as a learning process continuously flowing over time or as an information organizer with simultaneous impact on economic performance (Antonelli and Pegoretti, 1996). Knowledge can be conceived of as a stock of results achieved by intentional investments in research activity.33 This knowledge affects future economic activity. Additional knowledge increases the stock, and its economic use does not necessarily imply depletion. The main instances of this kind of knowledge are R&D and human capital accumulation through schooling, also impinging on formerly acquired knowledge. Orthodox economic literature has focused almost all its attention on it. But knowledge can also be conceived of as an ongoing learning process which can affect current as well as future economic activity. In this case knowledge is a flow of new ideas generating a stream of economic effects starting from the very time interval in which these ideas have been generated. We could refer to incremental innovation and organizational change, as they increase productivity, but also to improvements in human resources quality through experience, off-thejob and on-the-job training, learning by doing and other forms of learning as byproducts of economic activity (Antonelli and Pegoretti, 1996). With the notable exceptions of the institutionalist school, the neo-Schumpeterians and the scholars of the local systems of production, little attention has been paid to this form of knowledge by economic literature. We can also conceive of another form of knowledge which affects current as well as future economic activity; that is, as a filter and organizer of available information. A relevant instance of this form can be found in imitation, or in the gains in productivity deriving from the improvement in the organization of a firm’s information flows. We can note that during the last decades increasing attention has been paid to this kind of knowledge by several lines of research in economics. 4 THE ASSESSMENT OF INTANGIBLE RESOURCES UNDER NATURAL AND ENVIRONMENTAL RESOURCES SCARCITY After the discussion of the preceding sections, it is evident that the economic use of intangible resources, including knowledge as their basic support, has acquired strong importance, both for private and public agents.34 On the other hand, it is also evident that scientific and technological progress did not cancel out scarcity in natural and environmental resources, while even they are becoming more and more intangible in kind. The problem of assessing the economic value of

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intangibles therefore arises. The way in which the problem itself can be outlined, if not solved, requires a sequence of at least three conjectures. 1 Policy-makers, as well as laymen, are more and more aware of the fact that ignorance about the dynamics of interactions among economic, social and natural processes, added to uncertainty and risk associated to the effects of human actions on the environment, implies an under-estimation of the economic value of intangible resources. 2 On the other hand, the amount and the distribution over time of investments needed for a sustainable development, suited in satisfying wants often expressed in merely qualitative terms, abundantly exceed actual constraints. 3 However, the available assessment criteria and techniques, essential in rationing these investments, are characterized by a large degree of discretion, which seems to reintroduce ‘paternalistic’ suggestions. Actually, such a sequence of conjectures is partially contaminated by inconsistent assumptions in phases 1 and 3 respectively, which reveal themselves when we try to define the states of the world corresponding to these conjectures. Conjecture 1 originates in a disequilibrium framework: the danger of underestimation is due to the inability or impossibility of the economic system to properly evaluate either the relative scarcity or the role played by intangible resources in production and consumption. On the contrary, conjecture 3 originates in an equilibrium framework. In fact, the evaluation process is usually carried out starting from a specific equilibrium configuration and from the assumption of completeness in its basic conditions. If this argument is consistent, then the assessment problem can be broken down into three stages. (a) First of all, a detailed investigation is needed of the initial disequilibrium setting and of its determinants. (b) Secondly, the configuration towards which the considered economy is leaning, due to objective forces and/or explicit strategies, must be determined. (c) Thirdly, the adjustment mechanisms working in transition stages are to be investigated, their completeness, swiftness and reliability examined, and the crucial parameters which are able to generate changes in the endogenous variables ascertained . With respect to stage (b) the notion of the technological system can play an important role in drawing the network of basic relationships connecting the agents, and in determining the economic values. However, when the natural environment is economically relevant, the notion of ‘persistent configuration’ can be even more helpful. This is evident in the case in which the rate of technological change is low and natural and environmental resources generate

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dominant constraints. But it can also be true in the case in which the rate of technological change is high, because it helps in making explicit those constraints implicit in the mechanisms which help to concentrate the technological efforts of the community in a certain direction. 5 PERSISTENT CONFIGURATIONS AND SUSTAINABLE DEVELOPMENT Particularly with respect to stage (b) in the previous section, it seems important to pay attention to the availability in a given system of broad production factors and to the capability to endogenously reproduce them. In other words, in order to answer the question concerning the assessment of the economic value of intangible resources, we must start from a taxonomy of configurations giving full relevance to the relative scarcity induced by natural and environmental resources endowment. Within such a context, by ‘persistent configuration’ of growth I mean a quantity/price system capable of surviving and reproducing itself over time conforming to specific environmental, structural and institutional characteristics. The system persistence, more than the achievement of a spontaneous equilibrium mechanism, can be conceived as the result of the interaction between institutional and structural factors and market forces: that is, of a more or less temperate political influence on economic development. Each configuration can also be thought of as an intersection, or a corner solution, of a transformation frontier describing the social opportunity cost of immaterial resources, including environmental resources, with a set of social welfare functions describing the political weight given to such resources. With reference to the notion of sustainable development (Pearce and Redclift, 1988), a ‘weak’ and a ‘strong’ version can be spelled out. The first one, emphasizing the absolute or relative scarcity of exhaustible and renewable natural resources, has been part of economic theory since Ricardo and Malthus. Moreover, referring to the problem of resource allocation, general equilibrium analyses are an important representation of the weak version. The strong version, on the one hand, extends the analysis to the whole of environmental resources, to the so-called ‘natural capital’, and sets, on the other, the need of keeping at least constant the stock of natural capital over time. This is aimed at reaching equitable solutions for the poor, the future generations and nature, and at avoiding risk and uncertainty coming from ignorance and reduced margins of resilience. This constraint to preserve natural capital can be thought of as an instance of political influence on growth. However, we could imagine a whole array of different configurations of growth. Just as a first illustration, I can single out four such configurations.

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5.1 Configuration of growth through extinction (CGE) In this configuration the natural capital reproduction rate is negative. This should not be necessarily considered as an effect of blind irrationality, but as an outcome of consolidated and specific proportions between the many functions of the environmental resources and of their relationships with the different sectors of the economy. If supply functions of material inputs with a narrow temporal horizon or objects of alternative destination for natural factors35 prevail, a configuration which within certain limits can be called persistent might consolidate itself. Still, it does not allow the achievement of an equilibrium between different resource functions and factor destinations. This might happen because some of the wants are not made explicit and because not all agents, present and future ones, insiders and outsiders, are able to reveal their own preferences. On the other hand, one cannot tell ex ante what sort of pattern will prevail: that is, whether a configuration taking into account self-reproduction and equilibrium will dominate, rather than one involving a more costly enviromental involution.36 5.2 Configuration of ecological modernization (CEM) In this configuration, environmental protection becomes profitable, not as a joint outcome of allocative efficiency but as an effect of specific political strategies concerning public subsidies, regulation, business- and state-financed R&D and individual behaviours. These are aimed at maintaining a growth strategy while transforming it from time to time. Equity for the poor and future generations involves accumulation and transfer of wealth in real terms, together (or alternatively) with the maintenance or increase in natural capital. Among the main determinants of similar strategies we can recall the diffuse perception of degradation factors as, for instance, acid rain which, being international, is partly exogenous to the national economic system. Causes and effects can be easily singled out. Operators’ clear perception of risk, and its translation into compatible expectations, facilitates strategies by mutual consent, even if these strategies imply pervasive adjustment effects for the whole economic system.37 5.3 Configuration of sustainable development (CSD) In such a configuration programming and market forces allow for the efficient management of environmental resources functional to the maximization of their own reproduction rate.

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In this case environmental resources play a dominant role within the economic system. Therefore the capabilities concerning the management of productive destinations of environmental resources and of basic inputs are more developed. Moreover, the standard of allocative efficiency applied to production factors automatically induces convergent choices, which are consistent with the environmental preservation standard. The most meaningful degradation risks are those that come from outside, on a continental or planetary scale, since proportions reached between different functions and relationships between environmental resources and economic sectors help to control domestic behaviours harmful to the environment.38 5.4 Configuration of preservation (CP) In this configuration environmental resources are given a value that is independent of the direct and derived demand coming from the economic system. Direct and indirect costs of environmental preservation are considered negligible on an ethical ground. In a small-scale and closed setting, where uncertainty and risk make any economically aimed decision potentially harmful to the environment due to the pervasiveness of multiform and increasing external degradation sources, direct economic functions of natural resources are excluded. Intensity and location specificity of effects, which are often connected to such a configuration, raise the emblematic case, characterized by a strong education potential but also by conflicts and counter-actions due to the rationing on economic resources.39 6 THE NEED FOR KNOWLEDGE AND INFORMATION IN DECISION-MAKING It is almost universally agreed that government failure matches market failure. From the point of view of social decision-making, there are many substantial and procedural references to take complexity in to due account. But difficulties are not mainly due to the lack of techniques, even if further progress is possible in this area. The main problems can be ranked as follows. First of all, with regard to public policy, as long as the complexity of available techniques increases, information needs and skills are required to increase too. Second, on the one hand, the widening of all sorts of interdependence requires a sound international standardization of monitoring and analysis techniques; on the other, the specificity of local problems and the enlarging heterogeneity of affected agents increase the need of specific techniques and information. Third, it is important to adopt rational and consistent decisional procedures, but at the same time they should also be flexible and able to handle latent

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information and ‘usable ignorance’ (Ravetz, 1986) because huge investments in the acquisition of substantial informative bases are not necessarily profitable. Finally, in order to discourage arbitrary action and transactions restricted to the insiders, a transparent decision process is needed—though this does not easily reconcile itself with timeliness and efficiency. The setting-up of systemic schemes of analysis is likely to be crucial in this sphere; the heuristic models needed are not highly developed yet, this probably being due to diffuse pessimism about the ability of explaining ‘complexity’ and the pressure to solve specific problems. At the firm level, the decision-making concerning intangibles is more compact and simplified when the firm reaches the relevant dimensions or when it organizes networks with other firms. When choosing how to employ or to distribute intangibles, firms follow explicit and implicit rules. Often these imply social dimensions of costs and benefits involved even at the micro level. A translation of implicit transactions in explicit ones and an open accounting of intangible resources of course complicate the decision-making process. However, this can make for profitable new activities and innovation. Two main ways for a firm to face the problem seem to prevail. When decisions are dominated by information and strategy, firms tend to act without any substantial interest in the quality of the immaterial resources they use. As sellers, they signal competitive features of intangibles and, as purchasers, they select suitable intangible inputs within a context characterized by uncertainty. Such a signalling and selection process assumes that intangibles for the community remain unchanged in quality. In this respect, techniques and information bases on the signalling of intangible with given characteristics are developed. When decisions are dominated by knowledge and structural interaction, a firm’s competitiveness depends on its ability to adapt to the institutional and environmental context, while trying to change it in such a way that the firm itself can take advantage of it. In this respect, the quality of immaterial resources is relevant and techniques and information bases centred on the need to improve intangible characteristics are developed. In this case, very likely a greater demand for public services and for an effective ‘normal administration’ is to be expected. 7 CONCLUSION A new way of conceiving production factors’ role in economic dynamics is needed. Both stylized facts and the inner evolution of economic theory of ‘labour-human resources’, ‘physical capital-technology’ and ‘land-naturalenvironmental resources’ seem to urge this conceptual change and to foster the need for new analytical tools appropriate in the study of ‘broad production factors’.

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The issue of the reproduction of broad production factors implies, together with other key problems connected to the use of intangible resources in the economic sphere, a huge effort in the study of the role of knowledge in the evolution of economic systems. This seems to suggest that systemic analysis is extremely useful and should supplement problem-solving schemes. In developing a systemic analysis, a clear distinction must be drawn between notions focused mainly on technological and production characteristics (i.e., technological systems, national systems of innovation, local systems of production) and notions taking explicitly into account the interaction of natural and environmental resources with technology (i.e., persistent configurations). In a systemic view both the individual and social dimension are important and they could be studied by implementing an approach based on ‘embedded action’. NOTES 1 The author thanks Nicola De Liso and Riccardo Leoncini for their invaluable comments and suggestions. Of course, the usual caveat applies. 2 That is, labour and land. 3 Of course, these can be more or less developed. 4 As, for instance, in the biotechnological field. 5 Both factors flows and stocks have to be considered in adequate studies. 6 For instance, in singling out why market economies have performed better than the centralized ones. 7 Under this heading a whole series of concepts, including the so-called ‘national systems of innovation’ and ‘local production systems’, can be conceived. 8 Including institutional setting, technological and other structural foundations relevant in the analysis of real economic systems. 9 In what follows, the reference to economic literature, far from pretending to be updated or exhaustive, is mainly focused on the transition phase. 10 In the form either of a general equilibrium model or of a structural change approach. This approach tends to emphasize the need to arrive at general solutions and explanations before reaching definite prescriptions. 11 This approach tends to emphasize the need to arrive at definite prescriptions, in terms of solutions and policies in response to specific issues, independent of a general explanation of the functioning of the economic system. 12 Among the most significant contributions stressing the above-mentioned distinction, I would like to draw attention to those by McNulty (1980); Heckman et al., (1981); Killingsworth (1983); Freeman (1987); Soskice (1992); Appelbaum and Schettkat (1990). 13 Representative instances of this approach are Mincer (1974); Colombino and Zabalza (1982); Ashenfelter and Layard (1986); Leoni (1987). 14 See, for instance, Rosenberg (1976, 1982, 1984); Freeman (1982); Stoneman (1983); Coombs et al., (1987); Amendola and Gaffard (1988); Dosi (1988); Lundvall (1992); Pasinetti (1993); Metcalfe (1995).

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15 For example, economic growth differentials, market structure and industrial economics, effectiveness of public investments in R&D and agriculture economics, comparative advantages and international trade. 16 With reference to the diffusion process, for example. 17 With reference to innovation policies, for example. 18 Scale economies, output differentiation and entry barriers play a central role in this respect. 19 Among them, see, for instance, Kneese and Sweeney (1985); Fisher (1987); Kneese and Russel (1987). 20 The analysis of the role of natural resource scarcity has a long tradition in economics (Quadrio Curzio et al., 1994). Highly theoretical studies on land rent and optimal depletion have grown side by side with case studies mostly out-lined by the institutionalist school. 21 See Samuelson (1954) and Bulckaen (1994). 22 For example, agricultural land, timber and fishing. 23 For example, minerals and several power sources. 24 For example, air, water, open space, fauna and even cultural goods. 25 These are important determinants of the growth potential. 26 Which can be often easily incorporated in machines. 27 Positive externalities arise, of course, in this framework. 28 We could mention, for instance, R&D expenditure. Knowledge shares some common characteristics with public goods. However, since it is excludable, incurring a cost, and exposed to congestion, it can also be privately produced and employed, depending on the institutional setting. 29 Comments similar to the previous ones made with reference to knowledge can be made with respect to the components of skills acquired in general activities (e.g., public and private schooling, vocational training). 30 Both in the approach emphasizing transformation and interdependence apparatus. 31 Different models of capitalism are associated with different mechanisms for the generation of capabilities. 32 The two dimensions of knowledge have a parallel in the two dimensions of intellect (cognitive vs. practical) and rationality (unbounded vs. bounded). 33 For example, principles, formulae, patents, algorithms, prototypes. 34 It can be shown that in cost computation it is not possible even to distinguish sharply between a private and a public domain, because the social dimension is inescapable (Antonelli and Pegoretti, 1996). 35 Spaces and lands, for instance. 36 Very roughly, such a configuration can be exemplified by referring to Mediterranean countries or to less developed ones. 37 West Germany can be thought of as the best example of such a configuration. 38 Scandinavian countries and, in general, developed countries with a small population in which production and export of timber is a relevant activity could well illustrate such a configuration. 39 In such a case, specific plans, such as those concerning the creation of natural parks, are good examples.

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BIBLIOGRAPHY Abramovitz, M. (1991), ‘The Elements of Social Capability’, Paper presented at the International Conference on Economic Development of LDCs , Seoul, Korea Development Institute, July, Mimeo Amendola, M. and Gaffard, J.-L. (1988), The Innovative Choice. An Economic Analysis of the Dynamics of Technology, Oxford, Basil Blackwell. Antonelli, G. (1993), ‘Cambiamento Tecnologico e Crescita Economica: Nuove Acquisizioni e Quesiti Aperti nella Riflessione Teorica’, Economia Politica, Vol. 10, No. 2, pp. 175–202. Antonelli, G. and Pegoretti, G. (1996), ‘Paths of Technological Change, Production Factors and the Social Cost of Knowledge’, IDSE-CNR, Milano, September, Mimeo. Appelbaum, E. and Schettkat, R. (eds) (1990), The Impacts of Structural Change and Technological Progress on the Labour Market, New York, Praeger. Ashenfelter, O. and Layard, R. (eds) (1986), Handbook of Labor Economics, Amsterdam, North-Holland, Vol. I. Bairoch, P. (1988), ‘The Paradoxes of Economic history. Economic Laws and History, Schumpeter Lecture’, EEA Congress, University of Bologna, August, Mimeo. Becker, G. (1975), Human Capital. A Theoretical and Empirical Analysis, with Special Reference to Education (2nd edn), Chicago, Ill., The University of Chicago Press. Bettio, P. (1988), The Sexual Division of Labour, Oxford, Oxford University Press. Bianchi, P. (1989), Appunti per una Storia dell’Economia e Politica Industriale, Bologna, Dipartimento di Scienze Economiche, Mimeo. Bulckaen, F. (1994), Beni Pubblici: Condizioni di Efficienza Allocativa, Torino, Giappichelli Editore. Colombino, U. and Zabalza, A. (1982), ‘Labour-Supply and Quantity Constraints. Results on Female Participation and Hours in Italy’, Discussion Paper, Centre for Labour Economics, No. 125, London School of Economics. Coombs, R., Saviotti, P. and Walsh, V. (1987), Economics and Technological Change, London, Macmillan. Eatwell, J., Milgate, M. and Newman, P. (eds) (1987), The New Palgrave. A Dictionary of Economics, London, Macmillan, Vol. IV. De Liso, N. and Metcalfe, J.S. (1996), ‘On Technological Systems and Technological Paradigms’, in E.Helmstädter and M.Perlman (eds), Behavioral Norms, Technological Progress and Economic Dynamics: Studies in the Schumpeterian Economics, Ann Arbor, University of Michigan Press, pp. 71–95. Dosi, G., Freeman, C., Nelson, R., Silverberg, G. and Soete, L. (eds) (1988), Technical Change and Economic Theory, London, Pinter. Fisher, A.C. (1987), Natural Resources, in J.Eatwell, M.Milgate and P.Newman (eds), Vol. III, pp. 612–614. Freeman, C. (1982), The Economics of Industrial Innovation, London, Pinter Publishers. Freeman, R.B. (1987), Labour Economics, in J.Eatwell, M.Milgate and P. Newman (eds), Vol. III, pp. 73–76. Heckman, J.J., Killingsworth, M.R. and McCurdy, T.E. (1981), ‘Empirical Evidence on Static Labour Supply Models: A Survey of Recent Developments’, in Z.Hornstein,

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J.Grice and A.Webb (eds) The Economics of the Labour Market, London, HMSO, pp. 75–122. Holt, C.C. (1970a), ‘Job Search, Phillips’ Wage Relation, and Union Influence: Theory and Evidence’, in E.S.Phelps (ed.), Microeconomic Foundations of Employment and Inflation Theory, London, Macmillan, pp. 53–123. Holt, C.C., (1970b), ‘How Can the Phillips Curve Be Moved to Reduce Both Inflation and Unemployment?’, in E.S.Phelps (ed.), 224–256. Kapp, K.W. (1963), The Social Cost of Business Enterprise, London, Asia Publ. House (revised edition, 1978). Killingsworth, M.R. (1983), Labor Supply, Cambridge, Cambridge University Press. Kneese, A.V. and Russel, C.S. (1987), Environmental Economics, in J.Eatwell, M. Milgate and P.Newman (eds) Vol. II, pp. 159–164. Kneese, A.K. and Sweeney, J.L. (eds) (1985), Handbook of Natural Resources and Energy Economics, Amsterdam, North-Holland, Vol. I. Leoni, R. (1987), Le Teorie Economiche dell’Offerta di Lavoro. Modelli Teorici e Verifiche Empiriche, Roma, La Nuova Italia Scientifica. Loasby, B.J. (1996), ‘On the Definition and the Organisation of Capabilities’, Paper presented at the IDSE-CNR Seminar, Milano, June, Mimeo. Lundvall, B.-A. (ed.) (1992), National Systems of Innovation. Towards a Theory of Innovation and Interactive Learning, London, Pinter Publishers. McNulty, P.J. (1980), The Origins and Development of Labor Economics. A Chapter in the History of Social Thought, Cambridge, Mass., MIT Press. Marsden, D. (1986), The End of Economic Man? Custom and Competition in Labour Markets, Brighton, Wheatsheaf Books. Metcalfe, J.S. (1995), ‘The Economic Foundations of Technology Policy: Equilibrium and Evolutionary Perspectives’, in P.Stoneman (ed.) Handbook of the Economics of Innovation and Technological Change, Oxford, Blackwell, pp. 409–512. Mincer, J. (1974), Schooling, Experience and Earnings, New York, Columbia University Press. Pasinetti, L.L. (1993), Structural Economic Dynamics: A Theory of Economic Consequences of Human Learning, Cambridge, Cambridge University Press. Pearce, D. and Redclift, M. (eds), (1988) ‘Sustainable Development’, Futures, Special Issue, Vol. XX, No. 6. Petretto, A. (1987), Manuale di economia pubblica, Bologna, Il Mulino. Quadrio Curzio, A. and Antonelli, G., (1988), ‘The Development of an AgroTechnological System between Market Pulls and Structural Pushes’, in G. Antonelli and A.Quadrio Curzio (eds), The Agro-Technological System towards 2000. A European Perspective, Amsterdam, North-Holland, pp. 1–16. Quadrio Curzio, A. and Scazzieri, R. (1983), Sui Momenti Costitutivi dell’Economia Politica, Bologna, Il Mulino. Quadrio Curzio, A., Fortis, M. and Zoboli, R. (eds) (1994), Innovation, Resources and Economic Growth, Berlin, Springer-Verlag. Ravetz, J.R. (1986), ‘Usable Knowledge, Usable Ignorance: Incomplete Science with Policy Implications’, in R.E.Munn and W.C.Clark (eds) Sustainable Development of the Biosphere, Cambridge, International Institute for Applied Studies, pp. 415–434. Richardson, G.B. (1972), ‘The Organisation of Industry’, Economic Journal, Vol. 82, pp. 883–896. Reprinted in G.B.Richardson (1990), Information and Investment, Oxford, Oxford University Press.

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Rosenberg, N. (1976), Perspectives on Technology, Cambridge, Cambridge University Press. Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, Cambridge, Cambridge University Press. Rosenberg, N. (1984), ‘Innovazioni tecnologiche: cosa possiamo imparare—se c’è qualcosa da imparare—dal passato’, in G.Antonelli (ed.) Innovazioni tecnologiche e struttura produttiva, Bologna, II Mulino, pp. 27–44. Rosenberg, N. (1994), Exploring the Black Box: Technology, Economics and History, Cambridge, Cambridge University Press. Rullani, E. (1995), ‘Contesti e Varietà nell’Economia d’Impresa’, Paper presented at the XXXVI Scientific Meeting of the Italian Economic Society, Florence, October, Mimeo. Samuelson, P.A. (1954), ‘The Pure Theory of Public Expenditure’, Review of Economics and Statistics, Vol. 36, pp. 387–389. Stoneman, P. (1983), The Economic Analysis of Technological Change, Oxford, Oxford University Press. Soskice, D. (1992), ‘Skill Mismatch, Training Systems and Equilibrium Unemployment: A Comparative Institutional Analysis’, in F.Padoa Schioppa (ed.) Mismatch and Labour Mobility, Cambridge, Cambridge University Press. Spence, M., (1974), Market Signalling: Informational Transfer in Hiring and Related Screening Processes, Harvard, Mass., Harvard University Press. Storper, M.J. and Harrison, B. (1991), ‘Flexibility, Hierarchy and Regional Development: The Changing Structure of Industrial Production Systems and their Forms of Governance in the 1990s’, Research Policy, Vol. 20, October, pp. 407–422. Tarantelli, E. (1986), Economia Politica del Lavoro, Torino, Utet.

5 Different dimensions in the analysis of technological change Giovanni Pegoretti

1 INTRODUCTION I believe there is a general agreement over the fact that when addressing the field of technological change, an analysis at different levels is required. It is clear that with all economic phenomena it is possible to find a time (or a series of periods) at which decisions are taken at the microeconomic level that lead to a certain outcome; it is also evident, on the other hand, that an overall view is required which gives indications about the general context in which the phenomenon takes place. The problem with overall views is that they tend to crystallize general principles into models that are not sufficiently flexible to include several many-sided or changeable—but nevertheless important—features of economic reality.1 There are, however, a number of quite general approaches that do not force a theory into over-tight and rigid ‘frameworks’ although being at the same time powerful enough to provide, through their guidelines, a different perspective in the perception and evaluation of the problems tackled. This applies to other scientific fields. Without resorting to the usual examples from the history of physics or astronomy, one needs only cite the advances in biomedical research that resulted from the idea of the human organism as an ‘internal sea’. The study of cells and tissues as isolated entities, on the one hand, and of organs as morphological and mechanical structures on the other, is then viewed within a perspective that gives significance to the relationships between different parts of the body, to the chemical exchanges which take place, etc. This is, therefore, a perspective which has two important features: 1 It is more general than schemes modelling the relationships between single parts of the system (either empirically assessed or simply assumed), and it is able to give complex significance to different interrelationships. 2 It does not consist of a ‘closed’ set of statements about single facts, but provides an interpretative reading which orients research at various levels.

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I would argue that the ‘evolutionist’ approach represents a similar phenomenon in the field of economics. The recognition of an environment that interacts with the firm and activates dynamic selective forces opens up prospects for significant research—especially because it provides an escape route from interpretative schemes in which ‘generality’ closely resembles tautology. There is another level which research cannot ignore. Between analysis of the individual parts of the system—in relation to the environment with which they interact—and the overall view that gives significance to the various facts under observation, there must also be room for study of the interrelationships among the various parts of the system and of the circumstances under which such interrelationships condition general or particular behaviours. In an economic system, there are interdependencies that cannot be analysed completely by considering their single elements or by considering the system as comprising only a small number of large aggregates. Such interdependencies should be studied within an adequate analytical framework according to the type of problem being confronted. In particular, the study of the evolution of the ‘technological system’2 cannot ignore the interdependence relationships created by the structure of the productive system. This entails seeking links with different approaches, developed for the purpose of analysing problems which are not evolutionary and sometimes not even dynamic; approaches, nevertheless, which can provide the framework for the examination of some crucial interrelationships in order to determine the directions in which the economic system is moving. This topic will be dealt with in the last section of this chapter—where an example will be given of an approach which may be usefully reconsidered – while the first sections draw attention to some particular aspects at the microeconomic level which, in my view, among others deserve to be further explored: the financial aspects of innovation activity and the attitude of firms towards innovation. 2 THE FINANCING OF INNOVATION Studying, developing and selling a ‘new’ product on the market (whether it is really new or whether it has evolved out of a pre-existing product) often requires financial resources at various stages. It is difficult to generalize about the economic mechanisms able to supply funds where they are needed; if on the one hand they are integral and fundamental parts of the market mechanisms, on the other they are one of the most delicate aspects from the point of view of the way they work and of the behaviour of subjects involved.

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This holds both within the firm and outside it. In the latter environment it is evident, let’s say, at a ‘primitive’ level, a relationship constituting a fundamental component of the ‘engine’ of modern economies: the relationship between the capitalist (almost always referred to in the second person by the financial intermediaries) and the entrepreneur. Although this relationship also operates within the individual firm, externally it appears more immediately as a contrast between the separate (though sometimes converging) interests of those who, on the one hand, feel they have an idea of interest and value to the market and those, on the other hand, who compare the alternative returns (in quantity and quality) on a certain capital investment. There is no need to emphasize here that an innovative idea is often introduced by setting up a new firm, for which purpose finance capital is sought from external resources. Entrepreneurship often stems from an innovative idea, and, as Schumpeter stated, ‘the entrepreneur—in principle and as a rule—does need credit…in order to produce at all, to be able to carry out his new combinations, to become an entrepreneur’ (Schumpeter, 1961, p. 102). Any discussion of innovation, therefore, cannot be restricted solely to the firm where an idea was conceived, developed and then released onto the market; one must also establish in what way the economic system makes it possible to move from one stage to another. It is clear that in this field we are faced with the non-codifiability of the behaviour and attitudes of potential financiers, whose ability to evaluate a project may determine to a large extent its conditions of existence. Many examples may be adduced of this. No one is surprised by the fact that Steve Jobs, the founder of Apple Computers, managed to raise about one hundred million dollars to finance a company—Next—that did not exist at the time and whose programmes were partly ‘futuristic’. Although this time he was able to pledge his own store of credibility, which was considerable, he initially found himself in the typical situation of an inventor rich in ideas, but lacking in resources. History gives us many examples of personal successes of this kind, as well as of failures or, at least, of substantial obstacles; the case of Meucci and his telephone, or Matthew Boulton and the vicissitudes of Boulton and Watt, are significant cases in point. During the present century, innovations developed and realized within the structures of pre-existing firms, particularly in the laboratories of large companies, have become increasingly common, and inventions that are ‘economically viable without further R&D’ seem ‘a rare occurrence’ nowa-days (Nelson and Winter, 1982, p. 263). Nevertheless, it is important not to ignore the observation, supported by several empirical surveys (Jewkes et al., 1969), that the individual inventor has often made a decisive contribution to the most innovative ideas. He is less constrained by the rigidity to which structured research within an industrial laboratory is necessarily subjected, whereas steady research activity, requiring considerable means and the technical realization of the conditions for production on an adequate scale, may be undertaken only by established

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organizations (Gould, 1972). Moreover, it is the endeavours of the individual inventor to finance his idea and give it concrete form that reveal the nature of the relations that may lead to the establishment of an enterprise; they also reveal, by contrast, the advantages (and disadvantages) of organization in selecting and sustaining new ideas.3 Our attention should be directed to two types of mechanisms operating within the economic system: (a) the market mechanisms of the allocation of financial resources; and (b) policies designated to stimulate the creation of innovative firms, in that the individual inventor often makes his own product available when he becomes an entrepreneur.4 The latter point concerns the relationship between the potential entrepreneur, his abilities, and his willingness to take risk in the face of data from the surrounding economic environment that the policy decision can improve. The former point should be considered the other way round: that is, in terms of how potential financiers evaluate—and to what extent they are in a position to do so— the strength, the validity and the risk of an individual enterprise that has not yet shown its worth. Both points relate to the core of the market economic mechanisms, those that govern the choices—especially in the long term—of operators.5 I would like to draw attention to some aspects that require deeper analysis, specifically in consideration of the second point. What is the normal working of what we can define in a broad sense the ‘credit mechanism’? When we eliminate the ‘imperfections’ of the market, when the flow of information is sufficient, when everyone receives credit under the same conditions (naturally, weighted according to the appropriate parameters), the mechanism appears to work well— in theory. But when we are dealing with the financing of a not-yet-existent, innovative enterprise (for which the information that may be available on the related assets does not pertain to the enterprise as such) it is not, in my opinion, sufficient to solve the problem in a parametric form by setting an appropriate value to the ‘risk’ parameter. This is because the risk is perceived and evaluated subjectively by the (potential) financier, and in this evaluation his cultural level and his ability to anticipate the economic significance and the future importance of the proposed innovation must be considered. Here, beyond any exact calculation of the expected return, what is important is the experience of the financier and his ability to appraise not only in the strictly financial sense. The asymmetry of the relationship between the enterprise and the financier has been emphasized many times; analysis, however, has above all concentrated on the behaviour of already existing firms, in terms of their willingness to finance with borrowed capital—as an alternative to financing out of profits—their own investment projects.6 Here the most important opinion is that of the firm’s managers, who must evaluate the expected yields from investment projects in conditions of uncertainty, and compare them with the terms of finance offered by creditors. In the case under examination here (that of the new enterprise), however, we must emphasize the viewpoint of the financier, who can only assess

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a project in terms of its expected commercial success since the guarantees offered by the inventor are weak. The financier, in other words, should himself acquire a little of the entrepreneurial spirit and assume some of the entrepreneurial risk.7 A few conclusions seem to be appropriate here on the specific point of financing a new enterprise in general (and an innovative enterprise in particular). 1 The functioning of the credit market displays features that make standardization of conditions virtually impossible, inasmuch as the subjective nature of the evaluation is the dominant element on both sides. 2 This fact cannot be blamed on market ‘imperfections’, but is part of the very essence of entrepreneurial mechanisms. 3 The availability of credit—which for the (potential or effective) firm is one of its environmental data—is for the financier the outcome of a series of behavioural patterns that should be investigated analytically. Empirical evidence8 should also be reinforced. 3 THE PROPENSITY OF FIRMS TO INNOVATE In the case of a new innovative enterprise, the immediate aim of financing is to introduce the innovation. In the case of an already established firm, the immediate link between innovation and financing is less clear, inasmuch as funds are also required to carry forward production and investment programmes that do not necessarily have innovative content. The firm’s evaluations of the activities to be implemented and the ways in which they are to be financed are more complex. Similarly, the evaluations made by the possible external financier are more elaborate. In this context the issue of finance for the project may become less crucial for the development of a particular innovation, in contrast to the firm’s overall strategy. Therefore, whereas for the enterprise which is being set up to introduce an innovation, this also represents the dominant horizon for quite a long period, the already existing firm formulates its possible innovative strategies by targeting a market where it is already in operation (or a new market on which, however, it can profit from its experience and organization) and it submits these projects to its decisional processes as well as it does in order to evaluate various types of strategies (e.g. marketing policies). Thus, the direct link between finance and innovation becomes weaker. It is true that some research projects may be explicitly financed from an external source, with the active involvement of the financier in the evaluation process; however, the most important factor is the firm’s strategy, which places R&D activity in the context of other operating activities. Here the immediate aim of the planning and realization of the innovation may be to maintain or to recover market shares within an evolving ‘technological regime’,9 or else it may be directed towards an expansive strategy.

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However, when considering innovative activity, a firm’s behaviour may not be clear: it can sometimes be contradictory, so that it is difficult to classify in general terms. For example, it is possible to conceive of firms operating in hitech sectors—where the continuous evolution of the product is often a fundamental objective—which are forced to maintain a direct link between research activity and production activity, constantly incorporating the results of the former activity into the latter. Moreover, one would expect this type of firm to have a ‘dynamic’ attitude towards innovation; one so deeply rooted as to be evident in any of its activities. However, there are some cases—important ones, too—in which short/ medium term commercial policies gain the upper hand, so that the firm’s attitude towards the innovative activity is ‘inertial’ in character. This may lead to underestimation of the necessity for a specific commitment to research (specific to a line of products, while research may continue in other fields of interest for the firm), and to reliance on minor improvements or on marketing policies. Alternatively, the firm may be induced to consider the outcome of the recently performed research activity as providing an adequate basis for its long-term marketing policies. It may thus under-estimate the stimulus to further research efforts that derive from these policies to competitors, and ultimately yield to the temptation of basing long-term strategies on shortterm calculations. An example of this type is provided by IBM’s recent experience in the field of microcomputers. The creation of the IBM Personal Computer was not the result of great endeavour by the company in terms of research, even though its computer sector has a formidable research structure. As a result, we have witnessed the progressive spread on the market of PC ‘clones’ placed on sale by external manufacturers, above all Asiatic ones; a challenge which IBM found difficult to meet because it had not transferred a sufficient amount of its own patented know-how into its new product. As a reaction to a situation in which IBM risked its share in a rapidly growing market, the company developed a new product line, the Personal System/2, based on new architecture and supplied with a series of patented components, including the new Micro-Channel ‘bus’. The development of an advanced version of this component had two effects. First, it met the technological requirements for improvement in calculation performance. Second (and here the commercial strategy emerges), since this was a key component, patenting it and choosing not to publicize its technical specifications forestalled the possibility that external competitors could produce equipment with complete hardware compatibility. This strategy—the innovation of a crucial component for this product line and the adoption of a commercial policy of shutting out competitors (together with claims for high royalties for concessions on patented components)—has brought about a series of consequences. The prestige of the IBM trademark and the announcement of its massive commitment (also in research) to the sector,10 together with the advanced characteristics of the new product, have certainly brought advantages in terms of strengthening the company’s market position.

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On the other hand, its commercial policy of shutting out the competition rebounded on the company itself, inasmuch as: (a) the manufacturers of complementary products, such as add-on cards, the specifications of which must conform exactly with those of the bus, have encountered obstacles in the development of their products; it must be remembered that the appeal of a PC for the buyers lies partially in their ability to expand the machine and add accessories; (b) users saw their previous investment penalized, in that the hardware of the new system was incompatible with the old one; (c) some of the strongest competitors (Intel, Hewlett-Packard and Compaq) jointly developed an alternative bus (EISA) offering equivalent performance (which, according to some experts, is even better than Micro-Channel’s). The policy adopted by the competitors was the complete opposite of that chosen by IBM: the development of a bus compatible with the previous standard architecture (ISA) (which enables users to utilize the old add-on cards), no royalties, and public access to the technical specifications. As a result, IBM had partially to abandon—though rather late—its obstructive policy. Now, however, the company found itself facing competition which was no longer only based on the performance of machines produced, but also on a different standard. In other words, its initiative created an internal barrier, which caused a segmentation in the market which will be extremely difficult to eradicate. A partly similar case is Adobe, a software company that made its fortune by creating the Postscript language for laser printers. Postscript was protected by copyright, and from the very beginning the software house earned substantial royalties. The first producer of microcomputers to direct its attention to laser printers was Apple, which created a working alliance with Adobe and also purchased a minority share in it (which did not exempt Apple, however, from high royalties). With the recent success of desktop publishing and the demand for new languages to give greater precision to the printed page on the computer screen, the position of the company has suddenly become critical. Since the new problems required radical changes to Postscript, and not merely its simple evolution, large producers like Apple could have continued to rely on Adobe’s capacity and on a well-established working partnership; instead, Apple preferred to make a new alliance with Microsoft, a decision which was certainly influenced by Adobe’s commercial policy. Adobe was rather late in trying to recover its position by publicizing the technical specifications of the mythical Postscript; a policy which has at least partly halted falling demand for its software package by enabling other software houses to use it. These examples confirm once again the complexity of the links among the research and development of an innovation, company strategy and commercial policy, and market dynamics. While research activity in the industrial field must

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obviously responds to commercial objectives, the predominance of the latter, the lack of synchronization between R&D activity and market presence policy, may have adverse consequences for the firm. These may arise, for example, from an attempt to establish or protect dominant positions on the market before a fully satisfactory product line has been developed; or, by contrast, pursuing a policy of merely exploiting an exclusive innovation, which will probably induce competitors to make further efforts to bypass the obstacle. On the other hand, the winning formula is sometimes a determined commercial policy that promotes a product which, on the technological level, matches competitors’ products of superior quality and squeezes them out of an already crowded market, especially when the competition is between two different and incompatible standards.11 Bearing in mind that, apart from quality, what is always and above all important on the market is price, it is evident that a product’s cost advantages are a strong point against its competitors, even if these latter are superior in quality. However, this has sometimes proved to be a shortlived advantage. Since technological evolution often brings progressively lower costs, products disadvantaged from this point of view may turn out to be winners if their development and production are not abandoned. Two factors militate against this potential outcome: (a) the achievement of a dominant market position by other highly distinctive competing products—based sometimes on different and incompatible standards—which faces the firm with the prospect of high market penetration costs, as well as the development costs of the product; (b) the rapidity itself of technological change, which constantly branches out along new paths, and tends to determine advantage positions (in terms of a less uncertain reception by the market and a shorter time lapse between investment in research and commercial outcome) for the regimes that evolve out of the mainstream and from ramifications nearest to it in time. Notwithstanding the conventional belief that progress prevails in the end (meaning, in this particular context, that the most valid technical solutions are bound to emerge), interaction with the logic of the market may lead to the failure of some product lines to the advantage of others of inferior quality, but supported by better commercial policies or more favourable cost conditions; an initial disadvantage of this type can, therefore, sometimes halt development of an innovative line that could compete in time, also cost-wise, with the dominant one. This phenomenon is very well known;12 different models have been developed, giving account of markets locked in by path-dependent processes. Though, a deeper insight should perhaps be given to the nature of ‘historical’ or ‘random’ events which give rise to lock-in, and are often tightly connected to the organization of markets and firms.

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4 EFFECTS OF INNOVATION AND INTERDEPENDENCES IN THE ECONOMIC SYSTEM The firm operates in an environment from which it receives stimuli, information and responses to its own actions and strategies;13 this flow of signals is in turn generated and elaborated at different points in the system which may also lie outside the firm’s range of action. For example, relatively undifferentiated articles (e.g. nails) may enter a commercial network whose outlets are unknown to the firm; an ignorance which sometimes influences the firm’s perception of the factors affecting the demand for its product. The greater the differentiation of a product, the better the information that the firm has about its demand; in certain cases, however, the cost of acquiring or improving such information may outweigh the foreseeable benefits. A simple observation follows from this: the study of the effects of an innovation cannot be limited to the viewpoint of the firm, whose capacity to perceive (or alertness) is closely governed by its objectives, and, therefore, pertain to that portion of the economic environment that directly concerns the firm itself. For instance, the market performance of one particular oil-derived product among the many lines of the petrochemical industry may be totally irrelevant to the industry as a whole—for which the demand for the derivatives used by vehicles may be quantitatively much more important; on the other hand, in terms of the whole economic system, the characteristics and properties of such a product may give it a strategic position in the productive process. Therefore, we need instruments of analysis—not alternative, but complementary to those used to analyse the firm and single productive sectors— with which to study the real or potential repercussions of innovation for the whole economic system. Clearly, as far as relationships of a material type are concerned, the models of sectorial interdependencies can provide an instrument of this kind. Several studies have been made on the basis of this instrument, mainly directed to detect empirically intersectorial flows of technology.14 Other schemes derived from the former—e.g. the vertical integration approach—have proved to be useful in both theoretical and empirical analysis.15 When we pass to consideration of those values indispensable to evaluation of economic activity, the problem becomes far more complex, at both the empirical and the theoretical levels. The problem is one of ascertaining the character to be attributed to the prices considered in the analysis. What we have here is a trade-off in which the terms, even among various possibilities, may be effectively represented by the following two alternatives. On the one hand, it is possible to think of prices modelled on the bases of the particular conditions of each sector, without a priori hypotheses about the rules of price formation; these rules may be introduced in relation to the specific

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characteristics of the single market and be established perhaps on principles different from that of maximization.16 This approach to the problem is usually applied, for understandable reasons of complexity, to models that consider only one sector, or at least a limited number of sectors. On the other hand, it is possible to conceive of prices formed on the basis of general rules concerning some ‘tendency’, derived from clear hypotheses about the working of the economic system. For instance, we can refer to the neoclassical concept of perfect market competition, or to the classical view that fundamental adjustments take place in the long run and that the most important factor is free entry to the market. This latter approach offers, in my opinion, various advantages, but also certain disadvantages that can be partly overcome through closer integration with sector analysis and a pragmatic type of approach. Let us begin with the disadvantages, taking as our reference point a multisectorial model where prices (‘production prices’) include (a) a uniform rate of profit added to the materials input costs, and (b) wages (at a uniform rate) for the work. We assume linear technology; we may also consider fixed capital, natural (non-produced) resources and joint production. The first disadvantage derives from the nature of the price system, which assumes two conditions: (a) free entry to the various sectors and (b) a sufficient time span between possible structural changes so that operators can adapt to them. Point (a) does not constitute a critical problem, since it is possible to consider the coexistence of different rates of profit in such models (Grillo, 1976), linked to the particular situations of individual sectors.17 As regards point (b), this entails considering the system ‘as if it had already reached an equilibrium position— which means, obviously, ignoring the changes that could occur during the process of adjustment. This is the really crucial aspect, since in fact the system never moves into equilibrium and the configuration it tends towards can continuously change. Note, however, that we are using the ‘classical’ model for a purpose other than that for which it has been revised and improved in recent theory (Sraffa, 1960): that is, as a critique of the neoclassical theory of value and distribution. The utility of a multisectorial approach in the present context may derive from a pragmatic use of the model that takes these criticisms into account (in particular, the impossibility of ranking techniques according to capital intensity independently of the distributive variables, and the dependence of prices on an ‘open’ distribution established on the notion of surplus) and gives prices a less restrictive meaning than that of ‘gravity centres’ activated in the long term by the movement of capital towards the more profitable sectors. What matters here is that businessmen may see this system as a configuration which the system moves towards in the absence of significant disturbances. It might be, for example, a system of prices made relatively stable (in the projects or the perception of firms) by the presence of dominant firms or of non-antagonistic behaviour in oligopolistic markets.

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In this way it is possible to examine the pressures on the system and analyse some critical points in the system itself. Even though the perspective that the model provides is static in character, it may be useful to see under what circumstances the system comes near to triggering processes of critical change, with the conveying into the entire productive system of the effects of technological change originating in one sector, its possible repercussions on income distribution, and the strategies devised by operators as a consequence. To illustrate this point, let us begin with an example that seems to be quite distant—but, as we shall see, not so distant—from the case of the introduction of an innovation: the rationing of a scarce resource monopolized by a single owner or group of owners. This rationing would force the system to shift to ‘resourcesaving’ techniques which, if chosen from among those available, would turn out to be more expensive (otherwise they would already be in use). This situation may be foreseen by the holders of the rationed resource, who plan to exploit it by claiming a rent with respect to the non-rationed situation. Yet, the probability of their expectations being fulfilled will be affected by the configuration of the price-distribution system with the technique being presently employed in the economic system and with the one to be employed. For instance, if the change affects profits,18 the pressure to withdraw from the situation of scarcity will be stepped up, resulting in increased efforts to develop resource-saving techniques not available at the moment—that is, increased efforts to innovate. The point is that the structure of the economic system and the interdependencies among productive sectors (together with those among prices, income shares and demand) play a crucial role in determining the possibilities of producing—and, therefore, of distributing—surplus. As for using this model of analysis, although in the present case the shift in technique changes the structure of the productive system, study of the interaction among technological data, distribution variables and prices make it possible to detect the triggering of a dynamic process of change in the system, its point of origin, and the structural characteristics of the system that determine its possibility and/or its probability. In some sense, it is thus possible to monitor the accumulation of pressure throughout the entire system and not in a single sector only. Similarly, we can consider the case where the introduction of an innovation (say in the production of a commodity being used as a means of production) brings a relatively persistent advantage for the innovative firm and penalizes its competitors. If on the one hand this may affect their respective market shares, on the other it may lead to a situation where the system is hampered in its shift towards the threshold of a superior technology (the commodity in question being utilized for productive purposes) made potentially available by the innovation. It is true that this innovation could become available to the whole system following a successful marketing policy; however, it is likely that competitors will be able to adopt defensive strategies—relying on customer loyalty (suitably galvanized by adequate marketing policies), on possible incompatibility between the new

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product and previous standards, on possibly enhancing the performance of their products by progressive improvements, etc. Any obstacle to the spread of the innovation could trigger tensions that would inevitably accelerate the tendency towards a new change; tensions that at the micro-economic level appear principally in the alterations of the market shares and profit levels of the firms concerned with the production of that particular commodity. In the system as a whole, however, the potential advantages arising from exploitation of the innovation (or the potential losses which may derive from obstacles to its utilization) and which may trigger tensions towards change, are evident at the level of the technology frontier and of the capability of the system to produce and distribute a greater surplus. These examples show the usefulness of methods of analysis which provide, not exact and precise indications (precision which is practically impossible with a multiplicity of sectors) but of synthesis derived from broad-based facts and comprising the directions in which the system may be moving, the critical points in the system, the possible consequences of changes, and the variables that may help to bring them about. Such information should be examined in detail and linked with more exact and precise analyses. This does not alter the fact, however, that analysis of the repercussions of technological progress is still indispensable. NOTES 1 As a meaningful example we can cite the dichotomy between the importance of the impact of technological change at the microeconomic level, and the way macroeconomic models deal with it. Many macroeconomic models can account for technical progress, but no visible link is established between the level at which technical change is generated and the way it is incorporated in the whole economic system to affect macroeconomic variables. 2 I employ here the term ‘technological system’ in the sense of a set of technical knowledge which can be currently utilized in planning and operating production activities. 3 On the important role played by the new technology-based small firms as the seminal sector of the economy, see Vercelli (1988). 4 This, obviously, is not his only course of action: he may sell the rights to exploit his idea, temporarily or definitely. 5 ‘Individual initiative will only be adequate when reasonable calculation is supplemented and supported by animal spirits, so that the thought of ultimate loss which often overtakes pioneers, as experience undoubtedly tells us and them, is put aside as a healthy man puts aside the expectation of death’ (Keynes, 1973, p. 162). 6 See, e.g., Wood (1975). 7 That entrepreneurial spirit and attitude towards innovation are closely connected is also demonstrated by some empirical results. 8 See, e.g., Prakke (1988) for a very interesting analysis. 9 For a definition, see Nelson and Winter (1982, pp. 258–9).

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10 Among other things, the announcement of a new ‘philosophy’ concerning the search for the maximum integration between calculation systems of different levels. 11 This is the case, for example, of the VHS video system that squeezed the Betamax system out of the market. 12 See Arthur (1989). 13 This is already a concession to simplification, since the firm in itself is a complex system, of which certain parts also interact separately with the outside world. 14 See, e.g., Scherer (1982). For a methodological survey, see Marengo and Sterlacchini (1990). 15 See Pasinetti (1973, 1988), Marengo and Sterlacchini (1990), Oda (1990). 16 An approach of this kind, for example, is adopted in the formulation of Silverberg’s model (1987). 17 The rate of profit assumed for the single sector could be, for instance, the ‘average’ rate, or the rate of the firm leading the sector. 18 This could happen after an initial period in which wages bear the brunt of the change; this situation will probably cause tension on the level of profits too.

BIBLIOGRAPHY Arthur, W.B. (1989), ‘Competing Technologies, Increasing Returns, and Lock-In by Historical Events’, Economic Journal, Vol. 99, pp. 116–131. Gould, J.D. (1972), Economic Growth in History, London, Methuen. Grillo, M. (1976), ‘Introduzione di saggi di profitto differenti in uno schema di interdipendenze settoriali’, Giornale degli Economisti. Jewkes, J., Sawers, D. and Stillerman, R. (1969), The Sources of Invention, London, Macmillan. Keynes, J.M. (1973), The General Theory of Employment, Interest and Money, London, Macmillan Press (1st edn. 1936). Marengo, L. and Sterlacchini, A. (1990), ‘Intersectoral Technology Flows. Methodological Aspects and Empirical Applications’, Metroeconomica, Vol. 41, No. 1, pp. 19–39. Nelson, R.R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., Belknap Press. Oda, S.H. (1990), ‘The Application of Pasinetti’s Vertical Hyper-integration to Timesaving Technical Progress and the Input-output Table’, Cambridge Journal of Economics, Vol. 14, No. 2, pp. 241–246. Pasinetti, L.L. (1973), ‘The Notion of Vertical Integration in Economic Analysis’, Metroeconomica, Vol. 25, No. 1, pp. 1–29. Pasinetti, L.L. (1988), ‘Growing Subsystems, Vertically Hyper-integrated Sectors and the Labour Theory of Value’, Cambridge Journal of Economics, Vol. 12, No. 1 , pp. 125–134. Prakke, F. (1988), ‘The Financing of Technical Innovation’, in A.Heertje (ed.) Innovation, Technology and Finance, Oxford, Blackwell. Scherer, P.M. (1982), ‘Inter-Industry Technology Flows in the United States’, Research Policy, Vol. 11, pp. 227–246.

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Schumpeter, J.A. (1961), The Theory of Economic Development, New York, Oxford University Press (first published 1934, Harvard University Press, Cambridge, Mass.). Silverberg, G. (1987), ‘Technical Progress, Capital Accumulation, and Effective Demand: A Self Organization Model’, in D.Batten, J.Casti and B.Johansson (eds), Economic Evolution and Structural Adjustment, Berlin, Springer-Verlag. Sraffa, P. (1960), Production of Commodities by Means of Commodities. Prelude to a Critique of Economic Theory, Cambridge, Cambridge University Press. Vercelli, A. (1988), ‘Technological Flexibility, Financial Fragility and the Recent Revival of Schumpeterian Entrepreneurship’, Recherches Economiques de Louvain, Vol. 54, No. 1. Wood, A. (1975), A Theory of Profits, London, Cambridge University Press.

Part II Theoretical models of technological change

6 On diffusion and the process of technological change John Stanley Metcalfe

1 INTRODUCTION In this chapter I have sought to provide an overview of some aspects of the diffusion of innovation in relation to the process of investment and structural change. Several extensive reviews of the literature have recently been published and my purpose here is to highlight some central issues explored in them, not to discuss details.1 It scarcely needs saying that the diffusion of innovation plays a pivotal role in the process of technological change, yet it is surprising how little economic policies are explicitly aimed at the promotion of diffusion. Technological awareness is one matter, diffusion is quite another. If new technologies are not diffused they have negligible economic weight and negligible economic impact. Thus, it is not sufficient for a nation’s firms and knowledge-generating institutions to be technologically creative (to think great thoughts and to plan grand designs); investment in the new technologies must also follow to displace in whole or in part existing patterns of activity. Without a process of diffusion the return on technology creation will be negligible. We will focus our discussion on a limited number of topics; namely, the diffusion of technology as streams or trajectories of related improvements rather than as single innovations; the different levels at which technology diffusion takes place —artefacts, skills, knowledge; aspects of investment decision-making in relation to diffusion; and the importance of supply factors in the balancing of supply-side and demand-side factors in the diffusion process. The argument is deliberately discursive, drawing attention to major research issues rather than to welldocumented solutions to various problems. The empirical picture is clear. Research from a wide variety of disciplines has shown that new technologies are not diffused instantaneously into the prevailing economic and social structure and that the pattern of diffusion can vary greatly across technologies and industries, in some cases being drawn out over decades. The conceptual question is also clear. Why does the world change in the way it does and why is new technology not adopted instantaneously? The study of the

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diffusion of innovation seeks answers to this most perplexing of all questions in the social sciences. When economists turned to the study of economic growth after 1945 it was not surprising that they did so in terms of aggregate macroeconomic models, forgetting perhaps the rich micro growth literature that had accumulated in the 1920s and 1930s. The conditions for equilibrium growth were the focus of their attention, and models with fixed sets of commodities produced and consumed in unchanging proportions became the basis for a macroeconomic investment and growth analysis. While such models were claimed to be consistent with the socalled stylized facts of economic growth they clearly violated one of the most fundamental historical features of economic growth; namely, that it entails continual structural change. A picture of an economy expanding all its activities in the same proportion is a chimera as any historian will testify. Two issues are fundamental here. Technical progress has involved the development and injection into the economic structure of qualitatively different production activities and the displacement in whole, in part, or in total, of previously existing ones. The technology set of 1989 is quite different from that of 1939 and even more so from that of 1889. Secondly, the relative importance of different activities, as measured by their economic weight, is subject to continual change both within nations and between nations, the latter being reflected in the changing patterns of international trade and investment. It is this aspect of the development process which is inseparable from the question of the diffusion of technology. The plain fact is that economic growth has a complexity and a variety which cannot be captured by macroeconomic patterns of thinking, and this variety is fundamental to any understanding of the dynamics of economic change. Economic development takes place in an open-ended fashion, driven at root by business creativity in developing technologies and matching organizations, and there may be no equilibrium state to serve as a reference point or ‘natural’ attractor for the development of the system, at any level of aggregation. What is to be understood is not equilibrium and state but process and change, and the way in which policies and institutions influence dynamic processes of change. In understanding the process of economic growth it is vital to comprehend that it is inseparable from structural change, and, as such, it is driven by two basic mechanisms: mechanisms of experimentation which generate different varieties of economic behaviour, and mechanisms which spread or diffuse ‘successful’ varieties through the economic structure to the detriment of their ‘inferior’ rivals. These mechanisms of innovation and diffusion do not proceed independently since experimentation is resource dependent and the diffusion process influences both the distribution of resources to experiment, the patterns of accumulated experience which condition the process of experimentation, and the very incentives to experiment. It is because of the central role of creativity and experimentation that the process of economic growth is open. There is an inherently novel element in growth. While no one can forecast what the

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economic structure of the advanced nations will be twenty years from now we can be confident about the economic processes shaping the way in which economic systems evolve. Among these processes those relating to the spread or diffusion of new technology are crucial. It is not difficult to find particular questions relating to the economics of growth and technical change where diffusion processes are central to the phenomena under investigation. The movement of average practice productivity in residual calculations reflects the mix of technologies in a sector or whole economy and this mix changes over time according to the relative rates of diffusion of the constituent component technologies in that sector or economy. We elaborate further on this matter below. The return to investments in R&D, public or private, depends on the rate at which the associated technologies are diffused into the economic structure, while the structural impacts of new technologies upon employment and the labour skill mix depend on the rates of diffusion of those technologies. Finally, patterns of international trade are deeply affected by national differences in the rate of diffusion of technologies; that is, differences between ‘supply’ rates of diffusion and ‘demand’ rates of diffusion which are balanced by corresponding flows of exports or imports, as in the product life-cycle theories. The diffusion of technology is a complex matter, involving changes in the behaviour of economic agents, in which new opportunities emerge to rival often long established patterns of activity. In the study of diffusion, the hypothesis of rational behaviour does not take us very far. The key issue here is not whether agents optimize, we take it that they do in the sense of being able to consistently rank alternatives and select the most desirable one judged in terms of their ultimate objectives. Rather the key question, as Shackle for one has repeatedly emphasized, is how they come to construct meaningful choice sets, which in our case must also include new technologies. How do new technologies come to be on the agenda of organizations of all kinds (Arrow, 1974)? What are their strategic responses to new opportunities? These are central issues in diffusion research. In general terms we also consider it a mistake to consider diffusion as a phenomenon separate from the competitive process. At root, the diffusion of new technology is the outcome of competition between rival alternatives for achieving particular objectives. The pace of technological competition is driven by the variety of the range of alternatives available and by the nature of the environment in which choices between technological options are made. Here the technological strategies of firms and their openness to variety are of fundamental importance. We comment subsequently on some of the policy implications which flow from this.

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2 TECHNOLOGY A crucial issue in any discussion of diffusion is to identify what it is that is being diffused, and here one must confront different levels of technology. In most studies a technology is equated with given and unchanging artefacts, either products or the machines or materials with which they are produced, and this has been a dominant approach ever since the pioneering work of Griliches (1957) and Mansfield (1961). In this sense diffusion research has had a narrow focus, for the artefact is only a limited representation of any technology and two other dimensions deserve close attention (Layton, 1974). The first is technology as sets of skills embodied in individuals and shaped into competencies within organizations. This is technology as the ability to act, the ability to perform productive transformations. How skills are acquired and articulated is surely vital to the study of technology diffusion and, indeed, this is often captured explicitly in the literature on technology transfer. The second dimension of technology to emphasize is technology as knowledge, the ability to think about and explain the transformation process in terms of facts, concepts and theories. Study of diffusion at this level raises quite different issues relating to the creative, cognitive and assimilative capacities of different organizations, the nature of learning processes and the degree to which knowledge can be appropriated and capitalized. The point to emphasize is that knowledge is structured and has its own specific rules of accumulation which differ from technology to technology (Vincenti, 1984). Unless we take a very narrow perspective focusing on the diffusion of an unchanging artefact, we must recognize the three different levels of technology accumulation and their interaction during the diffusion process. Diffusion involves investment in skills and knowledge as well as investment in material artefacts. Several implications follow from this. The first is that what is being diffused is not a single artefact but rather a sequence of artefacts or post-innovation improvements (Georghiou et al., 1986) as the original design is enhanced and extended to new applications. A family of related products and processes is being integrated into the economic structure. Each improvement may be incremental in nature but cumulatively they may transform the competitive advantages associated with a particular technology. Uniting these different artefacts is a set of design principles, concepts and facts which define their ‘common’ tranformation process. Such a related sequence forms what might be called a design configuration. On a broader scale a set of design configurations which draw elements from a common knowledge base may then be defined as a technological paradigm (Dosi, 1982). A radical innovation sets up a new design configuration, while incremental innovations relate to improvements within a design configuration (Freeman, 1987). The second implication is the importance of skills and knowledge accumulation in underpinning the development of the artefacts within a design configuration. This sequence of innovations is not exogenous but rather is shaped

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as to its rate and direction of development by the accumulation of skills and knowledge. The profitability of firms supplying and adopting the new technology determines the resources at their disposal to underpin R&D. The importance of learning phenomena, by doing, by using (but not by forgetting!) is well documented (Rosenberg, 1982; Sahal, 1981), and learning in at least some of its dimensions cumulates with the diffusion of technology. Several scholars have drawn attention to the importance of interactions between users and suppliers in shaping the development of technology during the diffusion process (Lundvall, 1989; Rothwell and Gardner, 1985; Pavitt, 1984), and others have identified various mechanisms by which firms and other technology-generating institutions exchange knowledge (Allen, 1983; Von Hippel, 1988). Moreover, it is apparent that the knowledge base of a technology is structured both with respect to the institutional location of that knowledge and the types of knowledge concerned, the distinction between codifiable and tacit or skill-based knowledge being crucial here (Vincenti, 1984; Valentin, 1989). Incentives to accumulate different types of knowledge vary across institutions and require different mechanisms, some, of which are dependent on the rate of diffusion of a technology. All this points to the hypothesis that technologies and their diffusion co-evolve in ways which are closely connected to the institutional structures which shape skills formation and the accumulation of knowledge. This suggests that diffusion research should extend its concern with producer-user interactions to cover the generation of skills and knowledge as distinct from technological artefacts. The industrial communication structure may even be more significant than the expenditure on R&D in determining the rate of advance of a technology. 3 ADOPTION AND DIFFUSION It is important to be clear on the distinction between the diffusion of technology and the adoption of technology. In the analysis of adoption one considers the decisions taken by agents to incorporate a new technology into their activities. Here one is concerned with the nature and timing of decisions usually in relation to specified characteristics of the adopting population. A typical measure of adoption would be the number or the proportion of eligible firms in an industry which is using a given technology. Gort and Klepper (1982), for example, have used this approach to study the growth in the number of firms producing products in forty different technological areas. They found that a five-stage taxonomy can adequately summarize the observed patterns of adoption, and that the stage of rapid entry is also associated with rapid rates of development of the technologies. In the final stages they also find very rapid net exit rates as competition eliminates a significant portion (in some cases up to 70 per cent) of the firms operating in the prior rapid growth stages. In the analysis of diffusion, by contrast, one is concerned with the consequences of adoption in terms of the changing economic significance of a technology with the passage of time. A

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typical measure of relative economic significance would be the fraction of an industry’s output accounted for by a particular process or the fraction of a market taken by a particular product. The analysis of diffusion is in these terms closely related to the analysis of technological substitution in which the displacement of one technology by another is the focus of attention (Linstone and Sahal, 1976). The adoption and diffusion curves, of course, are closely interrelated, the former under-pins the latter but there is no reason for them to follow an identical time path. Empirical studies have found that a wide variety of sigmoid curves (logistic, Gompertz, cumulative lognormal, etc.) provide good statistical fits to adoption and diffusion data (Stoneman, 1984; Lekvall and Wahlbin, 1973). However, the shape of these curves is not the main issue, since they are but the hands of the clock tracing out the movements generated by a hidden mechanism, and it is the mechanism we need to understand. Clearly one cannot judge the nature of the underlying mechanism simply from the shape of the corresponding diffusion curve. For any realized curve is in effect an envelope curve shaped by changes in a variety of exogenous factors which have influenced the diffusion process. If under given conditions diffusion is governed by a logistic process this does not mean that the realized diffusion curve will also be a logistic curve. Depending upon the timing and magnitude of exogenous events, quite markedly non-logistic curves may fit the realized diffusion data. 4 INVESTMENT DECISIONS AND THE ADOPTION PROCESS In many cases the adoption and diffusion of a new technology requires that investments be made in durable capital assets and so the nature of investment decision-making processes has a considerable impact on the rate of application of new technology. Nothing in our understanding of the economics of investment decisions suggests that new technology is necessarily economically superior to existing technology or that today is always the best time to invest in new technology. Delay may well be rational. A number of issues are relevant here, all of them quite independent of questions of managerial ineptitude and lack of entrepreneurial spirit, which are indeed slender threads on which to support an explanation of ‘slow’ technological diffusion. The first reflects the complex nature of many production technologies as systems of multiple interdependent parts. An innovation which changes one of these parts may be incompatible with the rest of the system and require corresponding and costly system changes to make it fit. These costs of interrelatedness (Frankel, 1955) must be taken into account, along with the original capital cost of the innovation, in any rational investment decision. Obviously, the greater the degree of interrelatedness the less likely it will be that any given innovation will be compatible with the currently prevailing

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technological system. No doubt this helps explain the increasing importance of minor incremental process innovations over the life cycle in the development of some manifestly system technologies, e.g. in the automobile industry (Abernathy, 1971). Paul David (1979) has shown how interrelatedness elements, the costs of changing the landscape to fit the technology, slowed down the adoption of mechanical reaping in Victorian Britain, and the interrelatedness question has been explored in different directions by Feller (1966) in textiles and by Rossegar (1979) in the steel industry. However, the relevance of interrelatedness is more general. It applies to the changes in human capital (skills) which may be needed to accommodate an innovation, and to the abandonment of work practices, modes of organization and habits of thought which are incompatible with the new technology (Kindleberger, 1964; Lazonick, 1983). Quite possibly these interrelatedness elements are more pervasive in the case of intangible technological capital. A second issue concerns the distinction between replacement decisions and capacity expansion decisions in relation to durable equipment. As Marshall emphasized, the relevant notion of capital cost is different after an investment decision is made compared to before the decision is made. Ex ante, capital costs are based on the supply price of the equipment in question. Ex post they are based upon the value of that equipment in its next best alternative use, which may be its scrap value, which may even be negative if break-up costs are high, and which in general depends upon the value in second-hand markets of the equipment in question. When capacity expansion is being considered, the capital costs of all the alteratives are evaluated in the same ex ante terms in relation to the supply prices of different equipments. For replacement decisions, however, the capital costs of continuing with the current technology are evaluated in the ex post sense while the alternative technologies are evaluated in the traditional ex ante sense. When the alternative use value of the current equipment is zero this leads to the well-known result that the new equipment will only be adopted if the savings it achieves in variable costs are greater than the capital charges associated with it (Lutz and Lutz, 1953; Salter, 1960). Thus, it is not automatically the case that new technologies are economically superior to existing installed technologies. A rational basis for delay can exist, the length of which depends upon the rate at which wages or other variable cost increases enhance the economic advantage of the new technology. An important, but neglected, factor here is the role of second-hand markets for existing equipment in affecting the pace of diffusion. For the higher the second-hand value, the greater will be the capital charges and the opportunity cost of continuing with the existing plant and the less the economic disadvantage possessed by the alternative technologies. It has been suggested to me that firms in the UK shoe industry found it profitable to re-equip with modern machines in the 1970s when a lucrative market for their old machinery emerged in some less developed countries.

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The final issue to be considered in this context is the timing of investment decisions and the question of the optimal delay in switching to a new technology. There can be no presumption that today is necessarily the best time to invest in new technology. There are gains and losses from delay and the best date to invest is that at which the additional gain from a further small delay has just fallen into equality with the additional cost of a further small delay. The cost of delay is the forgone profit from not investing in the new technology. The gain from delay has two components: the saving on capital changes by not investing, and the differential gains from future improvements in the profitability of operating the new technology. These latter gains, which are necessarily based on expectations, may relate to the future growth of the market (Barzel, 1968), changes in factor prices, or to improvements in the technology itself (Rosenberg, 1976; Balcer and Lippman, 1984). This point can be illustrated as follows. Suppose a firm can adopt one of two technologies which it knows will become available at dates T and T respectively in the future, T > T and L= T — T. If these options are mutually exclusive, so that investment in one precludes investment in the other, and if, for simplicity, the capital cost of the rival technologies, K, is the same, we can write the present values of the options as

where (t), (t) are the profit streams associated with each option and is the constant discount factor. We assume that both present values are positive. Let (t) > 0 be the difference between the two profit streams. The difference in present value between the two investment options is

The first term is the loss from not investing in the first technology; the second term is the incremental gain from choosing the second technology; and the final term is the payment of interest which is avoided by investing at T rather than at T. Whenever the difference G(T, T ) is positive it is rational to wait for the arrival of the second technology before making the investment. Since T, T , (t) and (t) are necessarily expected quantities the role of technological expectations is immediately obvious. In particular, the anticipation of a greater rate of progress between T and T may induce expectations of a greater value for the differential stream (t) and thus bias the choice in favour of the later technology. Notice the crucial step in this argument, how expectations of technological change are translated into expectations of profit streams. In this latter respect the paradox can arise that the expectation of a rapid rate of technological change can

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result in a greater delay in the adoption of a new technology, as potential adopters anticipate the gains from future improvements. The introduction of technological expectations raises some major issues for the study of innovation diffusion: whose expectations are relevant, and expectations about which characteristics of a technology in relation to their magnitude and timing?; how are these expectations formed, by extrapolating past experience or in terms of more or less rational forecasts based on an underlying understanding of the development of the technology?; how does interaction between producers and users shape the formation of expectations, and what role do other knowledge generating agencies (e.g. universities) play in shaping perceptions of how technologies will develop? Each of these questions might be the basis for a substantial research programme in the diffusion of technology. Antonelli (1989), for example, has used patent data as proxies for technological expectations in his study of the diffusion of open-ended spinning rotors, and finds evidence of induced delays in adoption. The role of technological expectations reinforces our previous discussion that what is being diffused is not a single artefact, but rather a stream of related innovations linked together by common elements of skill and knowledge. When these improvements depend in part on experience effects, we see a material interdependence between diffusion and the evolution of the technology (Sahal, 1981). Diffusion leads to improvements extending the market for the innovations concerned and so accelerating their rate of diffusion which creates further improvements. It is then possible to conceive of equilibrium diffusion paths when the rate of diffusion calls forth a rate of learning and induced technical improvement which exactly validates that diffusion path (Jovanovic and Lach, 1989; David and Olsen, 1984). Indeed, if the expected rate of diffusion were too high, these optimistic expectations may prevent diffusion taking place, a result which is neatly explored in the David and Olsen paper. A further point which comes out of the multi-technology context is the need to carefully specify both the properties of the environment in which diffusion is taking place and the properties of the competing technologies. It is not sufficient to explain the pattern of acceptance of a new technology solely in terms of the properties, e.g. profitability or required investment, of that technology alone. New technologies are not diffused into a vacuum, but into structured environments which determine the dimensions of the advantages and disadvantages relative to existing technologies. Also just as the new technology can be improved, so the existing ‘old’ technologies which are under competitive threat can also be improved to meet and offset the advantages of the new. The pace of diffusion depends on relative competitive advantage which is determined in relation to sequences of innovations from competing design configurations and knowledge bases. The history of maritime transport is illustrative. It took virtually fifty years of improvement to the steamship before it displaced a greatly improved sailing ship from the world’s major trade routes (Graham, 1956). Barely had this happened before the steamship itself came under threat from a new technology in

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the form of the motor ship. Here we see the elements of competition between coevolving technologies, and the openness of the diffusion process to new technological surprises which may cut short the spread of one technology well before its full technical and economic development is reached. In some cases the old technology reaches its peak performance after competition from a new technology has emerged. Moreover, firms which are active in an old technology rarely make a successful transition to the new technology, primarily because of the barriers placed by the knowledge, skills, attitudes and assumptions which underpin success in the old technology and which prove to be inappropriate to the new context (Cooper and Schendel, 1976; Utterback and Kim, 1986). So far we have considered the difficulties in identifying the unit of analysis for the study of technology diffusion. A quite different set of questions relate to the balance of demand- and supply-side factors in the diffusion process. From Mansfield’s pioneering work onwards, empirical studies of the diffusion of new technology have been, in the main, studies of the demand for specific artefacts (Romeo, 1977; Oster, 1982; Levin et al., 1987). The standard economic explanation is that an innovation is diffused more rapidly when the (broadly defined) rate of return from adopting it is greater. High profitability (relative to alternative technologies) stimulates rapid diffusion. But in this view only one blade of Marshall’s scissors is in operation. An innovation cannot be diffused from the demand side unless it is profitable for firms to supply it; and the more profitable it is to supply the less profitable it is likely to be to adopt. Once supplyside factors are introduced, quite different questions arise from those normally considered, particularly those relating to the growth in productive capacity around the new technology (Metcalfe, 1981; Metcalfe and Gibbons, 1989; Stoneman 1984). Foremost here are questions of finance, the supply of skilled personnel and the availability of materials and other means of production. Many of the constraints on diffusion lie deep in the chain of activities underpinning a particular new technology. Rosenberg has aptly named them ‘complementarities’ (Rosenberg, 1979). 5 A SIMPLE MODEL OF A DIFFUSION PROCESS In this section we outline a process of competition between an old and a new technology which generates the traditional diffusion curve and the relative diffusion or substitution curve. The approach is Marshallian, both in terms of the use of partial equilibrium methods and in illustrating the fundamental distinction between expanding and contracting industries. The former are in a long-period situation of investment and capacity expansion, while the latter are necessarily in a short-period situation with given productive capacity. The approach also allows an explicit analysis of the role of profitability as incentive to adoption and as stimulus to capacity expansion. Demand and supply sides of the diffusion process are brought together to simultaneously determine patterns of output,

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prices, unit costs and profitability for the competing technologies. Diffusion is investment-and profit-driven. The approach also permits an integration of equilibrium and disequilibrium approaches to diffusion, precisely to emphasize their complementarity. This integration, however, is bought at a price; namely, the reliance on path-independent methods of analysis. For we construct positions of equilibrium which are quite independent of the paths towards those longperiod positions. While this method has an honorable history in both classical and neoclassical economic thought, it seems to be peculiarly inappropriate for the analysis of the process of competition and technological change. We do not, therefore, address the issues of path dependency and irreversibility which, as Arthur (1989) has shown, depend upon the existence of increasing returns of various kinds. Finally, it is worth noting that the type of process outlined below has direct parallels in the ecological literature on inter-species competition. It is in fact an example of a density dependent selection process, in which competition settles the two technologies into their respective niches. With appropriate modifications it can be applied also to the case of ‘commercialization’, where, instead of being substitutes, the two technologies support each other in a complementary fashion. 6 THE PROBLEM The process of competition is between two technologies which supply the same productive service to users. We choose units such that one unit of the old commodity provides one unit of productive service, while units of the new commodity ( ) also provide one unit of the productive service. The coefficient a measures the qualitative superiority of the new technology and thus its equilibrium price premium. In equilibrium the price of the old, Po, and the new, po, commodities must satisfy the condition Pn/ . The two commodities also have different, constant returns technologies of production, and correspondingly different competitive equilibrium supply curves. Within each technology, all firms produce under the same cost conditions. The market environment is uniform, and there is a given demand curve for the productive service. The two technologies draw upon different markets for their productive inputs, and as the output of each expands, external diseconomies are encountered which increase the equilibrium supply price for each commodity. 7 THE EQUILIBRIUM NICHE At the date of innovation of the new technology, the old technology supplies the entire market for the productive service. This market is in long-run equilibrium with price po* and output level Co*. The entry of the new technology redefines this position and creates a new set of niches for the two technologies. Either the

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Figure 6.1 The equilibrium niche

old one is completely eliminated or the two technologies share the market for the productive service. The outcome depends on the demand curve for the productive service, the two supply curves, and the qualitative superiority of the new technology ( ). By comparing prices and quantities in terms of units of the new material we can depict the possible outcomes in Figure 6.1. Dn is the excess demand curve for the new material, which is derived by subtracting, at each price, the supply of productive services from the old technology from the corresponding market demand for the productive service. With Sn as the equilibrium supply curve for the new technology, its equilibrium niche is Cn and corresponding price pn. Prior to the innovation date the old technology has output Co*. In the new niche the output from the old technology has contracted to Co on its supply curve. The long-run market is shared between the two technologies in the proportion s1 given by (1) In Figure 6.1, the two technologies coexist. This need not be so. If hn > po* then the new technology cannot be established, while if >pn then the old technology certainly cannot survive. Some supply inelasticity is crucial to its survival (Harley, 1973). 8 TRANSITION TO THE EQUILIBRIUM NICHE Consider the process by which the long-run equilibrium position is attained. For the new technology to displace the old, its productive capacity must be built up

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through investment and users must be induced to switch demand away from the old technology. Neither effect can take place instantaneously. On the capacity growth side, we let investment be proportional to the profitability of the new commodity, with the added constraint that unit cost now depends on the scale of production. We can write this as (2) where S(xn) is the long period supply curve of the new commodity. For given pn this gives a determinate growth path of productive capacity. The coefficient f depends on the capital:output ratio for the new technology, the fraction of internally generated profits which are reinvested in capacity expansion and the extent to which these are supplemented by funds from outside of the new activity. The old technology is, of course, in a quite different situation. It is marginal, with firms just earning the normal return required to keep them in production. Investment decisions are no longer relevant for them, and the only decisions to be made concern the rate of contraction down the long period supply curve. On the demand side, users have to learn of the attributes of the new technology before they will adopt it. Following a traditional argument, this leads to a learning process in which non-adopters learn by observing the experience of existing adopters, so creating an interactive element in the diffusion process. This leads to the following representative differential equation for the growth of demand for the new commodity. (3) where is the constant adoption coefficient and Dn(pn) is the long-run demand curve for the new commodity. Now for a given value of pn we have two fully determined growth paths for the new commodity. Moreover, if Dn(pn) and Sn(xn) are linear functions then (2) and (3) reduce to a pair of logistic differential equations. For arbitrary pn they also generate different time paths for capacity and demand. In a closed economy this cannot be so. Capacity and demand cannot grow in an inconsistent fashion, for entrepreneurs will tolerate neither capacity shortages nor excess capacity. In the short-term such deviations are probable but not in the long-term. So we seek balanced paths of diffusion to generate the secular trend of the diffusion process (Kuznets, 1930; Burns, 1934), along which pn varies to maintain the growth rate of capacity equal to the growth rate of demand. In the particular case where Dn(pn) and Sn(xn) are replaced by linear approximations, then the balanced diffusion curve of the new technology can be derived explicitly. Along the balanced path we find that (4) where Cn is the niche defined in Figure 6.1, and B is the diffusion coefficient. Both depend on the parameters of the old and the new technologies. One cannot

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interpret either coefficient in terms of the economic atttributes of the new technology alone. The solution to (4) leads to the familiar logistic equation (5) where A is a constant depending on the initial scale of output of the new technology. Despite the complexities of this competitive process, with endogenous changes in price, profitability, and unit costs, the output of the new technology follows a logistic curve towards its equilibrium niche. The properties of this process are more readily seen in Figure 6.2. In Figure 6.2a we have the curve of balanced logistic growth for xn, and in 6.2b the curve of the growth rate in output of the new technology showing the familiar pattern of retardation. In 6.2c is the path of decline in the output of the old technology, while 6.2d shows the path of price and output for the new technology. Given that f and are both finite, the path starts from point ‘a’ and reaches the long-period position at ‘d’. Two special cases then fall into place. With there is immediate customer acceptance of the new technology and throughout the diffusion process. The path followed is along the demand curve from ‘c’ to ‘d’. With we have the corresponding case of no financial or other supply-side constraints on the diffusion process, and the path followed is along the supply curve from ‘b’ to ‘d’. Furthermore, along any balanced path with we find that pn(t)/ po(t) < , which means that there is a differential profit advantage in favour of the new technology just sufficient to overcome consumer resistance to it. Any adopt0r switching to the new commodity will lower the cost of acquiring the productive service, although this cost advantage declines over time. Only in the long-run position will the new commodity sell at its full premium, and only then will the marginal user be economically indifferent between the two technologies. Thus the diffusion process has built into it a clear incentive to switch to the new technology once its attributes are properly understood. Indeed, without this economic incentive, the market for the new technology could not begin to grow. 9 THE SUBSTITUTION CURVE Having determined the outputs of the old and new materials we may now derive the substitution curve for the new technology. An extensive literature exists on technological substitution (Linstone and Sahal, 1976; Mahajan and Peterson, 1985) but no adequate theory exists on the determinants, as distinct from the empirical properties, of substitution curves. The significance of the substitution curve is that it provides an index of the state of the selection process across competing technologies or the relative economic weight attached to competing technologies. Within this two-technology framework the substitution curve corresponding to a balanced path can be derived as follows. One must first

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Figure 6.2 Competition between technologies and transition to the equilibrium niche

decide how market shares are to be measured. The obvious method is to employ current prices and measure shares in total expenditure but this is unnecessarily complex and permits of no easy solution since the prices are changing over time. However, since the materials are physical substitutes, we can justifiably use their relative efficiency in supplying production services to compare them on a common basis. Thus the market share of the new material at time t can be computed as (6) which is equivalent to combining the old and new commodities at their long-run relative prices. From (6) we have (7)

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Figure 6.3 The substitution curve

Provided the growth rate of output of the new material, gn(t), exceeds the corresponding growth rate of the old, g0(t), then s(t) increases over time. At what rate it increases is not transparent, since the two output growth rates are themselves varying over the substitution process. However, these growth rates are also determined once we know the balanced diffusion path. Taking account of this we find that the substitution curve is given by (8) where s1 is determined in (1) and R is the ratio of sizes of the equilibrium markets for the productive service in the old and new equilibrium positions R is a measure of the long-run impact of the new technology upon the market for the productive service. All the remaining coefficients in (8) are as defined in (5). Figure 6.3 shows the typical path of substitution, which follows a logistic curve from a value s(0)=RA, towards the upper asymptote s1.It is not surprising that the coefficient BCn determines simultaneously the rate of diffusion and the rate of substitution, since the output of the old technology is simply responding passively to the growth of the new commodity, and it is the dynamics of the new industry which is driving the process. For our purposes the significance of (8) lies in its non-arbitrary nature. The substitution curve is an explicit reflection of the process of competition between the old and the new materials as reflected in the relative profitability of using and producing them. Notice again how the parameters of this substitution curve are to be interpreted in terms of the parameters of both technologies and of their diffusion environment. Using this framework, all manner of factors affecting the substitution process may be made explicit and precise in terms of a comparison between different substitution curves. Moreover, such exercises provide a basis not simply for comparing different substitution processes but for discussing the more complex consequences of changes in parameters during the substitution process. Taking

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account of such changes, we can generate a family of logistic substitution curves which typically form a non-logistic substitution envelope. The precise shape of this envelope depends on the nature and magnitude of the parameter changes and their temporal incidence. The cautionary implication is clear. Without knowledge of the changes in determining conditions during the diffusion process, it is not possible to infer the dynamics of substitution from an empirical knowledge of the substitution envelope. Even simple logistic substitution, processes of the kind discussed above, may be associated with markedly non-logistic diffusion and substitution envelopes. The framework of our Marshallian model is perhaps too simplistic. We have not allowed for foreign trade (Soete and Dosi, 1988) or for situations in which the old technology is not in its long-run niche when the innovation occurs. Nonetheless, this simple model does make explicit the factors shaping the diffusion and substitution process within the competitive context. 10 SOME POLICY IMPLICATIONS Some general policy implications flow from this analysis of diffusion as a competitive process. The first follows from the distinction between the technological and the performance characteristics associated with a new technology. What is important for diffusion is the economic performance characteristics of the new technology and the breadth of their potential application. A technology may be radical when looked at in terms of its technical characteristics and the change in knowledge and skills which it entails, but this may still leave it with performance characteristics inferior to those of prevailing technologies. This may be particularly so when the new technology is first developed; the initial artefacts may be inferior and have narrowly circumscribed applications. Considerable faith in and commitment to the long-term superiority of this technology may be needed if its true potential is to be revealed (Constant, 1980). In many situations the function of supporting infant technologies is likely to fall to government. In others it will fall to sufficiently far-sighted private firms and research organizations. The crucial point is to see the diffusion process as an open discovery process with a multiplicity of possible outcomes. Many experiments may have to be tried before a technology finds its proper economic position. Hence, duplication of effort and failure of many of these should be seen as necessary components of an effective diffusion process. Failure, it should be remembered, often produces information of great value. Building diversity of approach and consequent failure into the support of new technology may require institutional approaches to technology policy which are not yet in evidence. There is here a more general issue to be addressed. Economic theory in general has tackled policy issues in relation to innovation from a market failure perspective, showing how such issues as appropriability and public goods aspects of knowledge production distort market incentives and lead to inefficient

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allocations of resources to knowledge production. Such a view permeates much of the rationale for government support at different stages of the innovation process. It is not that this emphasis on market efficiency is misguided but rather it diverts attention from an alternative perspective: namely, that the purpose of policy is to enhance the creativity of the national innovation system. Questions of static market failure have then to be placed in the context of the processes by which technological capability is accumulated and diffused. As one example, it may be more important for the rate of progress in a technology not to spend more resources on R&D but instead to build communities of interaction between the different organizations articulating the technology in question. Who speaks to whom with what frequency and to what purpose, may be the crucial factor in determining the returns from an R&D programme. Rapid diffusion of superior technology requires a competitive selection environment which is open to change, and which distributes profits according to the relative economic superiority of the competing technologies. From the viewpoint of producers and users of a new technology the efficient operation of the profit mechanism is crucial. For profits provide the resources and the incentives to build capacity and to invest in R&D and training activities. All three elements are crucial to the diffusion of a national productive capability. Lack of skills will constrain diffusion as effectively as a lack of finance. Here governments also have a role to make sure that national support of R&D in a new technology is matched by the development of the national skill base. One must not only ‘know’, one must know how to ‘act’; that is the challenge of technology policy. In particular, national programmes intended to develop artefacts may come unstuck if there is no corresponding emphasis on training and skills formation to underpin the diffusion process. With respect to finance it is important that a spectrum of sources of capital is in place to supplement the internally generated profits of firms: formal and informal venture capital, corporate venturing, and the acquisitions market all have a role to play in meeting the financial needs of producers and users, large and small. An inelastic supply of capital funds is certain to slow down rates of technological diffusion. Of course, all this leaves open the question of the optimal rate of diffusion and its application to the demand and supply sides of the diffusion process (David, 1986), although I am not convinced that such concepts will find scope for practical policy-making. We have noted the link between diffusion patterns and international trade. Any barriers to building up the capacity to supply a new technology, relative to barriers to using it, will be reflected in an adverse balance of trade in the artefacts concerned. In such cases, the encouragement of direct investment by foreign suppliers may be an appropriate policy to follow, either jointly or independently from domestic suppliers. In concluding this rather selective overview of some issues in the study of innovation diffusion, it is perhaps appropriate to emphasize three areas when

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future research might profitably be located. The first is the development of taxonomic structures to capture sequences of related technological innovations (Department of Trade and Industry, 1987). The second is the interplay between artefacts, skills and knowledge as the different levels of accumulation and diffusion of technology. The third area is that of technological expectations, this formation bringing back into the picture the, by now, somewhat unfashionable field of technological forecasting (Arai, 1989). By way of summary, there is a close relation between the rate at which new technologies are diffused and the enterprise characteristics of an economy. A willingness to generate innovation experiments and the mechanisms to grow the successful ones rapidly are at the heart of the diffusion process. This is precisely what competition is: the commitment to change driven by ever-present technological variety. This, no doubt, is why diffusion is a difficult topic to analyse for in the last analysis, it is about the creativity of individuals and organizations and creativity is inherently open as to its rate, direction and determinants. NOTE 1 Cf. Dosi (1988), Mahajan and Peterson (1985), Metcalfe (1988), Thirtle (1987), Stoneman (1984).

BIBLIOGRAPHY Abernathy, W. (1971), The Productivity Dilemma, New York, Norton. Allen, R.C. (1983), ‘Collective Invention’, Journal of Economic Behaviour and Organization, Vol. 4. Antonelli, C. (1989), ‘The Role of Technological Expectations in a Mixed Model of International Diffusion of Process Innovations. The Case of Open Ended Spinning Rotors’, Research Policy, Vol. 18, No. 5, October, pp. 273–288. Arai, T. (1989), ‘Forecast of Assembly Automation in the Automobile Industry: Technological Progress in Robotics’, Technological Forecasting and Social Change, Vol. 35. Arrow, K.J. (1974), The Limits of Organization, New York, Norton. Arthur, B. (1989), ‘Competing Technologies and Lock-in by Historical Events’, Economic Journal, Vol. 99, March, pp. 116–131. Balcer, Y. and Lippman, S. (1984), ‘Technological Expectations and Adoption of Improved Technology’, Journal of Economic Theory, Vol. 34, pp. 292–318. Barzel, Y. (1968), ‘The Optimum Timing of Innovation’, Review of Economics and Statistics, Vol. 50, pp. 348–355. Burns, A.F. (1934), Production Trends in the United States since 1980, New York, NBER. Constant, E.W. (1980), The Origins of the Turbojet Revolution, Baltimore, Johns Hopkins University Press.

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Cooper, A.C. and Schendel, D. (1976), ‘Strategic Responses to Technological Threats’ , Business Horizons, Vol. 19. David, P.A. (1979), Technical Innovation and Economic Growth, Cambridge, Cambridge University Press. David, P.A. (1986), ‘Technology Diffusion, Public Policy and Industrial Competitiveness’, in N.Rosenberg (ed.), The Positive Sum Strategy, Washington, DC, National Academy of Sciences Press. David, P.A. and Olsen, T. (1984), ‘Anticipated Automation, A Rational Expectations Model of Technological Diffusion’, Stanford University, Mimeo, Centre for Economic Policy Research, Working Paper no. 24. Department of Trade and Industry (1987), Technology Taxonomy, Report prepared by PREST, University of Manchester. Dosi, G. (1982), ‘Technological Paradigm and Technological Trajectories’, Research Policy, Vol. 11, pp. 147–162. Dosi, G. (1988), ‘Sources, Procedures, and Microeconomic Effects of Innovation’, Journal of Economic Literature, Vol. 26, No. 3, September, pp. 1120–1171. Feller, I. (1966), ‘The Draper Loom in New England Textiles 1894–1914: A Study of the Diffusion of an Innovation’, Journal of Economic History, Vol. 26. Frankel, M. (1955), ‘Obsolescence and Technological Change’, American Economic Review, Vol. 45, June, pp. 296–319. Freeman, C. (1987), Technology Policy and Economic Performance, London, Pinter. Georghiou, L., Metcalfe, J.S., Gibbons, M., Ray, T. and Evans, J., (1986), Post Innovation Performance, London, Macmillan. Gort, M. and Klepper, S. (1982), ‘Time Paths in the Diffusion of Product Innovations’, Economic Journal, Vol. 92, September, pp. 630–653. Graham, G.S. (1956) ‘The Ascendancy of the Sailing Ship’, Economic History Review, Vol. 9, No. 1, pp. 74–88. Griliches, Z. (1957), ‘Hybrid Corn: An Exploration in the Economics of Technological Change’, Econometrica, Vol. 25, No. 4, October, pp. 501–522. Harley, C. (1973), ‘On the Persistence of Old Technologies’, Journal of Economic History, Vol. 33, pp. 372–391. Jovanovic, C. and Lach, S. (1989), ‘Entry, Exit, and Diffusion with Learning by Doing’, American Economic Review, Vol. 79, No. 4, Septe.ber, pp. 690–699. Kindleberger, C. (1964), Economic Growth in France and Britain, 1831–1950, New York, Harvard University Press. Kuznets, S. (1930), Secular Movements in Production and Prices, Boston, Houghton Mifflin. Layton, E. (1974), ‘Technology as Knowledge’, Technology and Culture, Vol. 15, No. 1, January, pp. 31–41. Lazonick, W. (1983), ‘Industrial Organization and Technological Change: the Decline of the British Cotton Industry’, Business History Review, Vol. 57. Lekvall, P. and Wahlbin, C. (1973), ‘A Study of Some Assumptions Underlying Innovation Diffusion Functions’, Swedish Journal of Economics, Vol. 75. Levin, S., Levin, S. and Meisel, B. (1987), ‘A Dynamic Analysis of the Adoption of New Technology: The Case of the Optical Scanner’, Review of Economics and Statistics, Vol. 69, No. 1, February, pp. 12–17. Linstone, D. and Sahal, D. (1976), Technological Substitution’. Forecasting, Technologies and Applications, New York, Elsevier.

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Lundvall, B.A. (1989), ‘Innovation as an Interactive Process: From User-producer Interaction to the National System of Innovation’, in G.Dosi et al., (eds) Technical Change and Economic Theory, London, Pinter. Lutz, F. and Lutz, V. (1953), The Theory of Investment of the Firm, Princeton, Princeton University Press. Mahajan, V. and Peterson, R. (1985), Models for Innovation Diffusion, London, Sage. Mansfield, E. (1961), ‘Technical Change and the Rate of Imitation’, Econometrica, Vol. 29, No. 4, October, pp. 741–766. Metcalfe, J.S. (1981), ‘Impulse and Diffusion in the Study of Technological Change’, Futures, Vol. 13, pp. 347–359. Metcalfe, J.S. (1988), ‘The Diffusion of Innovation: An Interpretive Survey’, in G. Dosi et al., (eds) Technical Change and Economic Theory, London, Pinter. Metcalfe, J.S. and Gibbons, M. (1989), ‘Technology, Variety and Organisation: A Systematic Perspectus on the Diffusion Process’, in R.Rosenbloom and R. Burgleman (eds) Research in Technological Innovation, Management and Policy, Vol. 4, Greenwich, JAI Press. Nelson, R. and Winter, S. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., Belknap. Oster, S. (1982), ‘The Diffusion of Innovation Among Steel Firms: The Basic Oxygen Process’, Bell Journal of Economics, Vol. 13. Pavitt, K. (1984), ‘Sectoral Patterns of Technical Change: Towards a Taxonomy and a Theory’, Research Policy, Vol. 13, pp. 343–374. Romeo, A. (1977), ‘The Rate of Imitation of a Capital Embodied Process Innovation’, Economica, Vol. 45, February. Rosenberg, N. (1976), ‘On Technological Expectations’, Economic Journal, Vol. 86, September, pp. 523–535. Rosenberg, N. (1979), ‘Technological Interdependence in the American Economy’, Technology and Culture, Vol. 19, January, pp. 25–50. Rosenberg, N. (1982), ‘Learning by using’ in N.Rosenberg, Inside the Black Box: Technology and Economics, Cambridge, Cambridge University Press. Rossegar, G. (1979), ‘Diffusion and Technological Specificity’, Journal of Industrial Economics, Vol. 28. Rothwell, R. and Gardner, P. (1985), ‘Invention, Innovation, Re-Innovation and the Role of the User: A Case Study of British Hovercraft Development’, Technovation, Vol. 3. Sahal, D. (1981), Patterns of Technological Innovation, Reading, Mass., AddisonWesley. Salter, W.E.G., (1960), Productivity and Technical Change, Cambridge, Cambridge University Press. Soete, L. and Dosi, G. (1988), ‘Technical Change and International Trade’ in G. Dosi et al., (eds), Technical Change and Economic Theory, London, Pinter. Stoneman, P. (1984), The Economic Analysis of Technological Change, London, Oxford University Press. Thirtle, C. (1987), The Role of Demand and Supply in the Generation and Diffusion of Technological Change, New York, Wiley. Utterback, J. and Kim, L. (1986), ‘Invasion of a Stable Business by Radical Innovation’, in P.Kleindorfer (ed.) The Management of Productivity and Technology in Manufacturing, New York, Plenum Press.

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Valentin, F. (1989), ‘Where Companies Learn: Innovations and the Generation of Knowledge in Small and Medium Sized Danish Companies’, Mimeo, Institute for Industrial Research and Social Development, Copenhagen. Vincenti, W. (1984), ‘Technological Knowledge Without Science’, Technology and Culture , Vol. 25, pp. 540–576. Von Hippel, E. (1988), The Sources of Innovation, Oxford, Oxford University Press.

7 A note on the diffusion of innovation Luigi Filippini1

1 INTRODUCTION In a series of contributions J.S.Metcalfe has considered the diffusion of innovation. It is concerned with ‘the process by which new technological forms are integrated into the economy to impose changes upon its structure …[and] with how the economic significance of a new technology changes over time’ (1988, pp. 560–561). The latter case is closely related to the analysis of the process by which new technologies partially or completely substitute for existing technologies. Therefore, it is one dimension of the problem of the transition between different economic equilibria. The approach developed stresses the interaction between capacity expansion and market demand as explanations of the diffusion of technologies into their niches. The demand and supply sides of the diffusion process are, therefore, brought together to determine simultaneously patterns of output and prices. The assumptions made are a competitive market and a technology with decreasing returns to scale. This note provides an extension which considers increasing returns to scale and shows that most of the results are still valid in the new frame-work. So the assumptions made in the seminal contributions are restricted and not necessary. This chapter is divided into three parts. The first gives the reader an introduction to the problem—namely, the process of substitution between two technologies; the second deals with the dynamics of the substitution path, where a logistic diffusion process for the new technology is assumed; the last determines the substitution curve between the old technology and the new one.

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2 THE EQUILIBRIUM NICHE J.S.Metcalfe has analysed the diffusion mechanism for a single innovation in many contributions (see Metcalfe, 1981, 1986, 1988; Metcalfe and Gibbons, 1987; Cameron and Metcalfe, 1987). His central idea is ‘that the development of the new technology acts as an impulse to growth and structural change and that this impulse can usefully be analysed via a distinction between the long-period niche for the new technology and the process of adjustment into that niche’ (Metcalfe, 1986, p. 40). In a partial equilibrium framework he considers the interactions between the growth of demand and the growth of productive potential for a non-durable material, assuming that the learning process generates a sigmoid growth curve of demand and the accumulation of capacity to produce the new material depends on the rate of profit. He also presents a theory of substitution between the old and the new technology, each of which produces a non-durable material employed as an input in subsequent stages of the production process, along the lines of the justmentioned diffusion mechanism. The model is so general that it could be applied to many different technologies: new materials, new energy sources, new uses for material. The approach followed is based upon separation between the conditions determining the degree of substitution in the long run and the ones determining the pattern of adjustment towards this long-run position. In particular, the longrun position is denned independently of the adjustment path.2 The transition to the long-run position is determined by the interaction between the dynamics of market growth (and decline) and the dynamics of capacity expansion (and contraction) appropriate to the technologies in question. The process of substitution is between two technologies which supply the same productive service to users. As more materials appear, more niches also appear and few materials disappear completely. The outcome depends on the demand curve for the productive service, the supply curves and the qualitative superiority of the new technology. It is assumed that a units ( ) of the new material are equivalent from the user’s viewpoint to one unit of the old material so that the two materials are perfect substitutes. In a market closed to foreign trade in the two materials, the new material is introduced at a given point in time when the old material has settled into its longrun niche and supplies the entire market for the productive service. The substitution of the old for the new is analysed in two stages. In the first stage, redefining the long-run niche for the old material and creating a new set of niches for the two technologies. In the second stage, the new material might eliminate or share the market for the productive service with the old material. A new long-run position is created which may be quite different in terms of output

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and price from the previous equilibrium. The two materials also have different technologies of production and correspondingly different equilibrium supply curves. In this note decreasing returns to scale are assumed for the production of the old material and increasing returns for the new material. Increasing returns to scale are of particular theoretical interest because of their preventing entry of new firms. An important instance of that may be the need to acquire a patent in order to start the productive process. Therefore, we assume, a single firm is in the market and prices are formed as a cost-plus pricing model. The conditions of long-run demand for the productive service are static and given as follows: (1) where xo=quantity of the old material which supplies users with a corresponding unit of productive service. and po = price of the old material. On the supply side the unit cost of production, co, inclusive of a competitive rate of return on the capital employed, increases with the output of the old material as: (2) and in equilibrium it must be that: (2’) On combining (1–2') the long-run niche for the old process, , and the market clearing price, , are determined: (3) The introduction of the new material redefines the equilibrium as the two materials are competitors. To determine the new long-run position let the new material be produced by the following unit cost function subject to increasing returns to scale (Varian 1984, pp. 18–20 and pp. 68–69): (4) and in equilibrium it must be that: (4') For the reason discussed above, in equilibrium, the relative price of the old and of the new material must satisfy the following condition: (5) From (2), (4), and (5) it is possible to derive the supply curve of the old material and the new one at each price. This curve is discontinuous and may be represented by Figure 7.1. Three regions are considered: in the first region, the supply is given by the old material; in the second, there is a competition between the old material and the new one; and in the third region, the new material prevails. The discontinuity depends on the fact that at

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Figure 7.1 The process of technological substitution

the two materials have the same price in terms of the productivity factor. If the demand schedule is in the first region only the old material is used; in the second region, the new technology becomes profitable and an incentive to switch to it is built and, finally, in the third region, the new material is consolidated. Therefore, the coexistence of the technologies depends on the parameters of the demand curve, of the supply curves, and of a. In particular, suppose that and solve (4). For xn greater than (or equal to) xd (ho), where xd is the demand function, the new technology substitutes the old one if: It is interesting to point out two limits of the possible outcomes of the substitution; that is, when the old material cannot survive in the market (pn and a market for the new material cannot be established ( ). In fact, in the latter case fixed costs are very large. For the purpose of this note the most interesting case is the third region where the new technology displaces the old. The second part, in fact, discusses the third region. For that purpose it is necessary to derive the demand curve for the new material.

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The demand curve for the new material, Dn (pn), is derived by subtracting, at each price, the supply of the old material from the corresponding market demand, as, in equilibrium, the total supply of the old and of the new material is equal to the demand for productive service: (6) On combining (4’) with (6), the equilibrium niche and the price for the new material is obtained, satisfying (5): (7) and (8) Meaningful cases are summarized as follows: there exist two positive roots for output, xni (i=1, 2), and correspondingly for price, pni, under the assumption that . For the two positive roots pni, it will suffice to point out that there exist two values of po satisfying the equilibrium equation (5). Generally the long-run demand for the material might be shared between the two technologies in the proportion: (9) where f1 is defined as the long-run degree of substitution (that is, the market share for the new material), and it can, therefore, assume two limiting values, 0 and 1. 3 THE TRANSITION TO THE EQUILIBRIUM NICHE Few innovations are adopted simultaneously. Basically there are two reasons. Firms may expect an increase in demand and may be reluctant to pay the adoption cost before the market has grown sufficiently (product innovation). Firms may expect a decrease in the adoption cost as well as in the uncertainty attached to the decrease of production costs induced by new technology. To determine the path of substitution for the new material, both the dynamics of expansion for the new material and of contraction for the old material are considered. Both effects cannot take place instantaneously. All assumptions still remain on the form of the curves describing the rate of increase of market demand and in the supply of the new material. In particular, users have to acquire information and learn the attributes of the new material before they purchase it. Non-adopters learn by observing the experience of existing adopters. An adequate approach to the demand generating process based on learning is the logistic curve.3 Thus the rate of increase for the market demand is given by:

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(10) where =constant adoption coefficient reflecting the information trans-mitting mechanism. The mechanism specified in (10) is incomplete in that the equilibrium level of demand will depend on the relative price of the new material and this is likely to change during the diffusion process. Particular patterns of price variations correspond to different logistic curves. To take account of this possibility an explanation of price changes is required and this leads to the other second element of the adjustment process: the growth of productive capacity. The supply of material by the new process is limited by the productive capacity, whose rate of increase depends upon the profitability of producing the material. The assumptions made on the supply are as follows: (a) increasing returns to scale prevail in the production of the new material; (b) investment in new capacity occurs as long as the rate of profit exceeds the normal rate of return. The flow of funds to finance investment is generated in fixed proportions by profits generated internally and by external funds; (c) the rate of profit is defined by the ratio of profits to capital invested; that is, the difference between the price of the new material and its long-run unit costs of production over the capital: output ratio. It follows that the rate of increase in the supply of the new material is given by:4 (11) where k=supply diffusion coefficient; that is, the inverse of the amount of profit required to expand capacity by one unit, given that capital funds to finance investment come from internal and external sources. For arbitrary pn, equations (10) and (11) generate different time paths for demand and capacity. In a closed economy market expansion and capacity growth cannot grow inconsistently. To maintain equality between them at all points in time, the price of the material must adjust over time and a situation of equilibrium expansion path is achieved. It follows that: (12) where:

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Equation (12) is a Riccati equation and the general solution is attained in general by integration of a linear differential equation of second order or by some equivalent algorithm (Davis 1962, Chapter 3). But if any particular solution is known, then it is possible to obtain the complete solution by means of quadrature. A particular integral of (12) is a solution of (7),5 then its general solution is (13) where K depends on the level of output of the new material at its innovation date. In this specification the equilibrium path of expansion is also a logistic curve. Of the two solutions of equation (7), only the greater is globally stable. So there exists a unique and globally stable long-run niche xn. The long-run niche is, moreover, independent of both coefficients p and k. The behaviour of the price equation follows by substitution of (12) into (11). Thus the relation between pn(t) and xn(t) along the balanced growth is as follows: (14) From this and from (4), pn(t) and costs fall as the output of the new material along the balanced diffusion path grows. The sign of the difference between price and costs along the equilibrium diffusion path is equal to zero for the two equilibrium quantities. Passing from the lower quantity to the greater one, the sign of this continuous function in that interval must be positive and is preserved along the path. In fact there is no other zero root and the greater root is globally stable. This result follows from the intermediate value theorem (Binmore 1985, p. 88). 4 THE SUBSTITUTION CURVE The substitution curve provides an index of the state of the selection process across competing technologies. And it is the reflection of the process of competition between the old and the new materials as reflected in their relative profitability. To determine the substitution curve for the new technology corresponding to the equilibrium growth paths of output one must decide how market shares are to be measured. Since the materials are physically substitutes, then the market share for the new material at time t can be computed as: (15) It is useful to point out that the physical market share is equivalent in the long run to the measure of the market shares in terms of their long-run relative prices. Differentiating (15) we have: (16)

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where g is the growth rate of output of the old material (go) and the new one (gn), which are varying over the substitution process. Along the diffusion growth path, gn(t) is determined from (12). The solution of (16) follows the stages of separating the variables and by integration. Hence:

(17)

where R is the ratio of the total outputs in the pre- and in the post-innovation long-run positions; that is, a measure of the long-run impact of the new technology upon the market for the productive service. The substitution curve is reduced to a logistic curve, and substitution tends to the long-run level/i. In addition, the rate of diffusion of innovation and of substitution depends on the part in square brackets in (13), since it is the dynamics of the new technology which is driving the process. To infer the effect of parameter changes upon the substitution process, two examples will suffice. An improved process to produce the new material is associated with a higher f1, R and the rate of diffusion, so that any substitution level is reached sooner. Similar implications follow in a higher value for that does increase diffusion substitution to take place. 5 CONCLUSION A relevant aspect of the diffusion process which is not considered concerns the implications of strategic interaction among firms on the diffusion process itself. In order to study the diffusion process one must carefully consider the market structure in which firms operate. For demand-based models relevant results have been obtained. In particular, even with perfect information and identical firms, if the value of adopting a costreducing, capital-embodied process innovation declines with the number of firms which have already adopted it, there will be a diffusion of the new technology rather than simultaneous adoption. This diffusion is due purely to strategic behaviour. It is, moreover, shown that an S-shaped diffusion curve can be predicted when profitability of innovations and strategic interaction across firms are subject to some incomplete information. Firms have the option to conduct experiments and adopt or reject innovation or postpone it. The decision by conducting additional experiments depends whether the accumulated experimental results have

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increased the estimate of profitability above a cut-off level which is chosen so as to achieve a balance between the risk of an incorrect decision and the cost of continued experimentation. The function identifying the cumulative proportions of firms adopting the innovation as in the sketched model nearly matched the logistic curve. The problem of diffusion of innovation seems particularly fruitful since it allows us to exploit jointly the contributions of evolutionary economics and the ‘mainstream’ of industrial economics. NOTES 1 I am indebted to E.Agliardi, C.Beretta, A. D’Agata, G.Fiorentini, J.S.Metcalfe, and A.Vaglio for helpful comments, and to M.Cimoli, G.Colangelo, D.Delli Gatti, M.Morroni and S.Perzolli for valuable conversations on an earlier draft of the chapter. I gratefully acknowledge support from CNR and MURST, Rome. 2 For the appropriateness for the analysis of the process of competition and technological change see Metcalfe (1988, p. 580). 3 Plotting usage of a new technology against time yields a sigmoid curve. The logist curve is just one of a whole class of curves that may be labelled sigmoid. See, for example, Stoneman (1983) and, for a critical approach, Davies (1979). 4 In a formal way the assumptions on the supply curve may be represented as follows: • the new material is produced under increasing returns to scale as in equation (4). • the growth rate of capacity, g(t), is proportional to the rate of profit, r(t): (1a) where n is the fraction of the internally generated profits and is the ratio of external to internal funds invested. • the rate of profit is then defined by: (1b) where v is the capital: output ratio for the new material. Combining equations (1a) and (1b) we have the rate of increase in the supply of the new material. 5 The following proposition is in fact proved (Kreider et al., 1968, p. 64): A constant coefficient Riccati equation has a solution of the form y=m, m a constant, if and only if m is a root of the quadratic equation

BIBLIOGRAPHY Bhattacharya, S., Chatterjee, K. and Samuelson, L. (1986), ‘Sequential Research and the Adoption of Innovations’ in D.J.Morris, P.J.N.Sinclair, M.D.E.Slater and J.S.Vickers

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(eds), Strategic Behaviour and Industrial Competition, Oxford, Clarendon Press, pp. 219–243. Binmore, K.G. (1985), Mathematical Analysis (2nd edn), Cambridge, Cambridge University Press. Cameron, H.M. and Metcalfe, J.S. (1987), ‘On the Economics of Technological Substitutions’ , Technological Forecasting and Social Change, Vol. 32, pp. 147– 162. Davies, S. (1979), The Diffusion of Process Innovation, Cambridge, Cambridge University Press. Davis, H.T. (1962), Introduction to Nonlinear Differential and Integral Equations, New York, Dover. Kreider, D.L., Kuller, R.G. and Ostberg, D.R. (1968), Differential Equations, Reading, Mass., Addison-Wesley. Metcalfe, J.S. (1981), ‘Impulse and Diffusion in the Study of Technological Change’, Futures, Vol. 13, No. 5, pp. 347–357. Reprinted in C.Freeman (ed.) (1984), Long Waves in the World Economy, London, Pinter Publishers, pp. 102–114. Metcalfe, J.S. (1986), ‘Technological Innovation and the Competitive Process’ in P.Hall (ed.), Technology, Innovation and Economic Policy, Oxford, Philip Allan, pp. 35–64. Metcalfe, J.S. (1988), ‘The Diffusion of Innovation: An Interpretative Survey’, in G.Dosi, C.Freeman, R.Nelson, G.Silverberg and L.Soete (eds), Technical Change and Economic Theory, London, Pinter Publishers, pp. 560–589. Metcalfe, J.S. and Gibbons, M. (1987), ‘On the Economics of Structural Change and the Evolution of Technology’ in L.L.Pasinetti and P.Lloyd, Structural Change, Economic Interdependence and World Development: Proceedings of the Seventh World Congress of the International Economic Association, Madrid, Spain. Volume 3: Structural Change and Adjustment in the World Economy, London, Macmillan, pp. 91–102. Reinganum, J.F. (1989), ‘The Timing of Innovations: Research, Development, and Diffusion’ in R.Schmalensee and R.D.Willig (eds), Handbook of Industrial Organization, Volume I, Amsterdam, North-Holland, pp. 849–908. Stoneman, P. (1983), ‘The Economic Analysis of Technological Change’, Oxford, Oxford University Press. Stoneman, P. (1986), ‘Technological Diffusion: The Viewpoint of Economic Theory’, Ricerche Economiche, Vol. XL, No. 4, pp. 585–606. Varian, H. (1984), Microeconomic Analysis (2nd edn), New York, Norton.

8 Technological and organizational innovations as problem-solving activities Massimo Egidi1

1 INTRODUCTION The issue I wish to raise in the present chapter is: what relationship exists between market and organization if we assume that micro-behaviours are innovative activities affected by incomplete knowledge and uncertainty? Although there is broad empirical evidence that technological and organizational innovations are endogenously generated within the economic system, many obstacles still stand in the way of their coherent explanation. The empirical evidence highlights, in fact, certain typical features of innovative processes that the traditional explanation is unable to handle satisfactorily: they take time, at both the creative and diffusion stage; they are path-dependent processes; their outcome is procedurally uncertain (in a sense I will explain in the next pages); they alter the organizational structure within which they are generated. These features can be interpreted by the neoclassical theory, but in a very unsatisfactory way and at the cost of such drastic simplification as to grossly distort the problem. In the present work I shall follow an alternative line of analysis, coherent with the bounded rationality hypothesis,2 in an attempt to show that these particular features of the innovative process can be given a consistent interpretation if one adopts a micro approach based on problem-solving activity. The path-dependent and time-consuming nature of the innovative process can be explained—as I will suggest—if one realizes that innovation is essentially the outcome of a process of learning and discovery, which generally is highly uncertain, complex and costly. This process entails a non-reversible change of the patterns used by individuals and organizations in interpreting the relevant information and determining their courses of action. This view requires a redefinition of what a decision process is; if individuals (and organizations) are assumed to have scarce resources of time and memory, and limited computing capabilities, then the core of the decision-making process becomes the search and learning process by which they acquire the relevant

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information and knowledge. This process is not merely reducible to a process of choice, but, as we will see, requires the use of a very different mental ability in order to assume and synthesize relevant information and to reduce knowledge incompleteness. Moreover, as the purpose of the learning processes that take place within economic organizations is to enable such organizations to pursue pre-established goals more efficiently, I shall briefly address the question of the relationship between organization and technology, on the assumption of the centrality of search in decision-making. Finally I will explore some of the reasons for evolution in organizational shapes, starting with the idea that the generation of new forms of division of labour and new forms of co-ordination among the different divided units is explainable as the outcome of a multi-actor learning process. 2 DECISION AND INNOVATION. CAN INNOVATIVE BEHAVIOUR BE INTERPRETED AS THE OUTCOME OF RATIONAL DECISIONS? The first point that I wish to discuss is the problem of the micro-foundations of the analysis of evolutionary phenomena. If one intends to address seriously the question of how innovation arises and spreads, and in particular understand how technological evolution develops, any attempt to explain the behaviour of the agents involved in this process in terms of optimal choice will be unsuccessful. The decision to innovate cannot be reduced to the optimum choice among known alternatives because, by its nature, it entails conditions of cognitive incompleteness (or, in Heiner’s terms, competence gap3): the innovation may come about only if the relevant information and knowledge are dispersed and incomplete. Otherwise all the economic agents competing on the same market would simultaneously adopt the same procedures and make the same choices. In this case, the innovation would be a transitory process, peripheral to economic analysis. Thus investigation of innovative phenomena would be restricted to an exercise in comparative statics—that is, to a study of an instantaneous transition between two equilibrium states characterized by different states of nature. And this was, in effect, the traditional position until the 1950s; a position which obliged the analyst to treat innovation, technology and organization as exogenous to economic analysis. Today, such a position is regarded as unsatisfactory even by those who adhere to a traditional approach to the problem. Most of the neoclassical literature on innovation, however, still concerns the problem of the adoption and diffusion of innovation only and does so, of course, by framing it in terms of optimal choice.

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Although it is certainly possible to base analysis of innovation on rational choice under conditions of informational asymmetry, such an approach is unsatisfactory because: (a) it gives a substantially exogenous treatment to the innovative process; (b) it assumes that at least one economic agent (who may also be the model builder) is endowed with complete knowledge. The last assumption conflicts with the hypothesis of the rationality of behaviour. In fact, information asymmetries are defined in terms of an agent who knows which is the ‘true model’ and hence which information is relevant. This agent is either one of the actors described by the model or he is the model builder who is supposed to be able to describe the situation without lack of knowledge, while the agents described in his model are attributed partial knowledge of the relevant information. Thus a teacher, i.e. a special agent with full competence in all the relevant situations, is ingenuously presupposed. There is no need to insist on the fact that this description is logically inconsistent, as Muth, Lucas and others have convincingly demonstrated.4 There are only two ways to escape from this contradiction: either one assumes rational expectations, or one assumes a bounded rationality situation in which no one, not even the model builder, can define a priori all the relevant information (as happens, for example, in the game of chess). The hypothesis of rational expectations has been criticized by Arrow on the ground that it conflicts with the nature of the market and of the other economic institutions.5 In fact, the role of the market is to procure for individual agents the information and knowledge necessary to implement their plans in a co-ordinated manner; hence there must not be any actor with access to a complete and accurate picture of all the relevant information and knowledge, since this would nullify the rationale of the market. Let us, therefore, suppose that only incomplete knowledge of what is the relevant information is possible, that a teacher does not exist, and that not all economic agents possess the same knowledge. In this case, the main problem of agents lies in acquiring this knowledge —that is, they have to find ways to pursue pre-established goals and assess their consequences. Seen in these terms, therefore, the core of decision-making is the search and learning process by which actors acquire the information and the knowledge that they need to achieve their objectives. Choice becomes only the final act in this complex process: it is therefore of secondary importance, while the central role belongs to the ability of subjects to frame and solve problems.

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3 INNOVATION AS PROBLEM-SOLVING ACTIVITY It should be pointed out that the above characterization of decision-making as a ‘problem framing’ and ‘problem-solving’ process can be appropriately applied to analysis of innovation processes. Innovative behaviour, therefore, arises where at least one of the three following conditions obtains: (a) incomplete knowledge; (b) close strategic interdependence among the decisions of different economic agents; (c) uncertainty in the Knightian sense. Let us now examine the relations among these three conditions, beginning with a definition of ‘knowledge incompleteness’. Knowledge incompleteness may manifest itself in any situation where at least one of the features of the decision process occurs; that is the economic agent 1 does not possess an exhaustive set of criteria with which to judge whether information is relevant; or 2 is unable to establish a procedure for calculating the consequences of one’s actions; or 3 does not know how to assess these consequences; or, 4 although possessing procedures either for selecting relevant information or for establishing the consequences of one’s actions or for evaluating the consequences, he/she is unable to make the computational effort required by at least one of the previous procedures. Note that in cases 1, 2, 3 above, the agent is assumed to possess ‘inadequate’ procedures, while in the fourth case it is assumed to possess ‘non executable’ procedures: in fact, in cases 1, 2, 3 it is endowed with procedures which are not adequate to permit either 1 the selection of only and all relevant information, or 2 the calculation of all the consequences of action, or 3 their evaluation. On the contrary, in the fourth case the agent is simply unable to execute a procedure in a realistic period of time. Let us now assume strong strategic interdependence and explore the consequences of uncertainty and knowledge incompleteness. Even though the framework within which decisions are taken is perfectly known—that is, every agent knows what information is relevant, knows how to calculate the consequences of his/her own actions and has a system of

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preferences with which to evaluate these consequences, the strong interdependence among different agents induces a high or insurmountable computing effort. In fact, even though the ‘rules of the game’ are perfectly known to actors, the calculation of the consequences of their actions involves a high degree of computational complexity, or else it may be so time-consuming that it becomes impossible to accomplish. This was the situation studied in the pioneering works by Simon; a situation where bounded rationality of the agents takes the form of their inability to perform the necessary computations. This case is only apparently ‘simple’—as the celebrated example of the game of chess effectively demonstrates. In fact, the impossibility of calculating the outcomes of one’s own actions also makes it impossible to assess strategies and therefore to formulate preferences. This induces economic agents to commit systematic errors: they find themselves in situations in which not all the relevant events—in this case the consequences of their actions—can be evaluated. This forces them into evaluations where the systematic lack of relevant information entails systematic error. This is evidenced again by the example of chess, where in a series of matches played by two grandmasters, each loses a certain number of times. Every time a player loses, that player has evidently made a wrong assessment of an opponent’s strategies by. failing to take into consideration the choices that the opponent actually made. Situations of this kind, where the economic agent is unable to make a thorough assessment of the whole set of relevant choices, are characterized by a form of uncertainty similar to that described by Knight. This I shall call procedural uncertainty to avoid confusion with the notion of uncertainty that predominates in economic literature.6 We have thus reached the first conclusion: situations (b) of strong strategic interdependence among decisions, by causing high computational complexity, generate cognitive incompleteness (a) and procedural uncertainty (c). Conversely, as we will see, knowledge incompleteness (a) causes procedural uncertainty (c). In order to discuss the last point, note that the definition of knowledge incompleteness seems to refer to two very different levels of difficulty: the inability to perform the computing effort required by a procedure (either for selecting relevant information, or for establishing the consequences of its actions or for evaluating the consequences), and the inability to build these procedures, which is basically an inability to solve problems. Note that at this last high level of cognitive incompleteness the frame-work within which the decision has to be taken is generally not well-defined. But even in cases when the frame is well-defined, i.e. the subject is faced with a clear and well-posed problem, there is no general algorithm which enables a new solution

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to be found, as we will show below, and therefore the search process can be performed without any certainty about the possibility of achieving a result.7 The two situations are apparently of two entirely different orders of difficulty. The first derives solely from a limited capacity to perform calculations which are, however, well-defined—that is, they are executed according to a known procedure, and the nature of the problem is well-defined and certain (procedures are existing and effective but not executable in a realistic time period). The second derives from the impossibility of giving complete specification to the terms of the problem or from the impossibility of finding a solution. The agent has not been able to formulate the problem in a way that renders it decidable (where one can say whether the problem is solvable or not); or, should the problem be solvable, it has not been able—up to now—to construct a solving procedure. Then the subject is supposed to possess ‘inadequate’ procedures. But as we have seen, the inability to execute a procedure gives rise to procedural uncertainty exactly similar to the inability of building executable procedures: agents, if they want to reduce their uncertainty, must search for a new procedure, which they expect to be executable with reasonable computational effort. Let us recall the chess player situation: a procedure for finding a (the) winning strategy exists (the von Neumann algorithm) but its execution requires the exploration of all the possible chess boards (the states of the game), i.e. requires the full expansion of the game tree, which has a too high (10** 120!!) number of states: the procedure is thus non-executable, and players must try to find a new, executable procedure. As is well known,8 players generally explore a limited portion of future chess boards and simplify the search by the use of ‘heuristics’: it is evident that such a procedure, while undoubtedly intelligent, does not fully reduce uncertainty; in fact it is an inadequate procedure in the previously defined sense because it does not lead to an executable procedure. It is very important to note that we do not have at our disposal a theorem on the existence of an executable procedure for chess. This situation reflects a general incompleteness of the class of formalized problems. Without discussing this topic here, let us remember that for many general classes of formalized problems it can be shown that there is not a solving procedure (algorithm). There are many important examples of unsolvable problems, in a variety of mathematical and logical theories. A striking example comes from the formalized mathematical theories, which I briefly outline here. In formal logic systems certain statements, or strings of symbols, are identified as axioms, and rules of inference are given whereby a new statement can be obtained from old statements. Theorems are new statements obtainable by applying the rules of inference to axioms or to previous statements. By means of this representation, the structure of any formalized theory, with axioms, rules and theorems, is captured. The solvability of a theorem can be

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assessed by formulating the following question: does an algorithm exist to decide whether a given string in a formal theory is a theorem of the theory? The work of Church and Kurt Godel showed that any formal theory that axiomatizes properties of arithmetic…and is not completely trivial (not everything is a theorem) is undecidable’ (Gersting, 1987, pp. 490–491); i.e. the requested algorithm does not exist.9 Of course broad classes of relevant formalized problems are solvable, but important classes are not. This implies that there is not a general algorithm for solving every formalized problem. This property of the class of algorithms allows us to understand that, since it is impossible to solve a problem by means of a general solver algorithm, when we are faced with a new problem that does not belong to a previously solved class of problems, we must generally perform an inductive search process in order to obtain a solution. To put it briefly: ‘That no algorithm exists to solve a large class of problems guarantees jobs for creative thinkers who cannot be replaced by Turing machines’ (Gersting, 1987, pp. 490–491).10 It follows that problem-solving activity cannot be reduced to the application of some known general procedures (deductively) to special cases; it necessarily requires an inductive search on the problems space; a wide literature has been developed in the last ten years that examines how heuristic and inductive search works in the human mind and how it can be artificially simulated.11 I will define innovation as the problem-solving activity performed by an agent (individual or organization) which has to make a decision under the hypothesis of bounded rationality. The agent is supposed to have scarce resources of time and memory during the activity of decision-making, but is supposed to use an intellectual ability— induction—which can be artificially simulated and is undoubtedly more powerful than the ‘classical’ maximizing activity. For the reasons illustrated above, innovation is necessarily a process which requires time, computational effort and intellectual capabilities, and its outcome is procedurally uncertain. Finally, note that as a result of the properties of algorithms previously highlighted the competence of an agent can be defined not only in relation to the latter’s intellectual capabilities but also and mainly to the nature of the problem to be solved: broad classes of problems cannot be solved, whatever the intellectual ability of the agent may be. 4 SATISFICING AND PROBLEM-SOLVING Let us now return to the problem of optimal choice and compare it with the properties of the procedures for problem-solving. Let us summarize the situation. As we have seen, the conditions of knowledge incompleteness imply that before

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making their choices, agents must be able to reduce their lack of knowledge or modify their competence gap in an appropriate manner. Under these conditions the agents—individuals or organizations—are unable to make an optimal choice simply because they are unable to take into account all the relevant information and knowledge. They must search for new procedures, because the procedures they use are not satisfactory for achieving the goals they planned. Because the known procedures are inadequate, agents will try to re-define the problems in such a way as to obtain more effective new solutions. By doing so they perform a highly uncertain search process, by selecting new possibilities in a large space of problems, in such a way as to re-define the original problem by means of new sub-problems. If this process is successfully completed, a new solution can be added to the set of previously known alternative solutions. This implies that for a general problem-solving activity the set of possible solutions of a problem is permanently open; i.e. nobody can be sure that, given a set of solutions for a problem, a new, more effective solution will ever be discovered. It follows that under conditions of uncertainty and incomplete knowledge the choice among alternatives is subject to two constraints. First, a choice is made only when it is possible to discover different alternative solutions to the problem —something that is not guaranteed a priori, given the uncertain nature of the discovery process (often only one solution is found). Second, given the context in which the action is performed, the choice cannot be maximizing in a global sense: it will be obviously the optimal choice among those solutions that have been discovered up to now. As I showed previously, there are obviously classes of solvable problems where it is possible to find the best solution—but this is not the general case. Then, since the search is costly, a global maximizing choice would generally require an amount of time for search and related costs which are not a priori predictable. On these premises the idea of a satisficing behaviour is perfectly consistent. Satisficing does not mean ‘being content with a second best because it is too costly to obtain the information needed to identify the first best’. This would be true only on the ingenuous assumption that a teacher has perfect knowledge of all the possible solutions to a problem, which are assumed to be fully definable in a closed set of alternatives. But, as we saw, from impossibility theorems taken from the theory of algorithms, we generally cannot fully identify such a set and by consequence we cannot identify the first best. Satisficing is therefore the natural effect of a highly uncertain search procedure, whose outcome cannot be previously predicted.

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5 ORGANIZATION, TECHNOLOGY AND LEARNING The problem I wish to address in this final part of the chapter is: can organization and technology be defined on the basis of the problem-solving approach, and what is the relationship between the two? The classical definition of the firm as an organization comes from Coase: ‘Outside the firm, price movements direct production, which is coordinated through a series of exchange transactions on the market. Within a firm, these market transactions are eliminated and in place of the complicated market structure with exchange transactions is substituted the entrepreneur-co-ordinator, who directs the production’. (Coase, 1937, p.3 388) Having previously defined the process of decision-making in a more general and comprehensive way than it has traditionally been, we need to discuss what the term ‘co-ordinating different decisions’ actually means. Since a decision is essentially a problem-solving process, one naturally wonders whether the co-ordination of different decisions will require a multiactor problem-solving process. This multi-actor process actually exists in all institutions, but can be recognized more clearly within firms rather than outside, on markets. Co-ordinating different decisions does not only mean sending signals to independent individuals allowing them to make consistent decisions, as assumed in the perfect competition analysis: we cannot trivially transfer perfect competition analysis to description of firm organization, assuming that the agents are co-ordinated by an entrepreneur who sends consistent plans and projects, while agents themselves are only mechanical executors. The central point is that projecting and planning inside the firm are distributed activities: at the top of the hierarchy plans are formulated in very general terms, not in detail; at any different level of the hierarchy, the execution of plans requires the ability to interpret and adapt these general ideas, and to solve new problems arising from attempts to realize plans; a continuous process of transmission of information and knowledge among agents is then required and their co-ordination is possible only if this organizational learning process takes place. In order to fulfil projects and plans, the organization must therefore be able to define and re-define its internal tasks; i.e. to define the way by which labour is internally divided. We thus arrive at an important point: the outcome of a process of problemsolving performed by a team of individuals that wants to realize a project is the division of labour and the co-ordination among the divided parts.

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Co-ordination is the complementary part of the division of labour which follows the realization of a project; the further the division of labour proceeds, the more the different divided parts require co-ordination and the more information becomes dispersed. In order to clarify my arguments so far, I need to go deeper into the nature of problem-solving activity. The discussion of previous sections has implied that problem-solving activity, although formalizable, cannot be reduced to logical deduction. While a great deal of empirical and theoretical work has been done over the last twenty years to describe how humans solve problems and how a solution can be obtained by means of machines, many contrasting opinions still divide experts in this area.12 Learning, discovery and induction are not considered to be synonyms, and a number of competing explanatory models have been developed. Without trying to discuss the different models of discovery processes, I will mention a ‘classical’ discovery model13 which describes how agents move into the space of problems when they seek a solution, and finally I will use this model to describe some features of organizational evolution. One of the most important ways of formalizing the problem-solving activity is based on the empirical observation that agents generally try to solve difficult problems by reducing them to simpler ones. But when a problem has been decomposed in a set of sub-problems, generally not all of the sub-problems will be immediately solvable and, consequently, they will be in turn decomposed into simpler ones. The decomposition then recursively proceeds until all the ‘relevant’ subproblems have been solved. Note that this procedure ‘by decomposition’ is a conjectural one, because (a) there is not a set of decomposition rules which a priori allows agents to achieve a certain result, and (b) agents can verify the solvability of the original problem only when all the ‘relevant’ sub-problems have been solved. This way of solving problems is characteristic of projecting and of planning activities inside organizations: first a task is drawn up, presupposing its feasibility, then the main task is decomposed into different functional parts. This division proceeds recursively until every part of the project is recognized as feasible. The problem-solving approach is, therefore, very useful for explanation of how teams organize themselves in order to achieve given goals. In understanding and formally describing how the division of labour takes place in organizations I shall therefore assume a general approach based on the recursive division of problems into sub-problems.

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In fact, the internal articulation of organizations can be seen as one of the possible ways by which tasks are decomposed into sub-tasks and entrusted to functional sub-systems of the firm. The organization decentralizes tasks internally, structuring its procedures (routines) into parametrically connected sub-routines14. Note that this process of problem-solving by division into independent sub-problems seems to suggest that the existence of hierarchies in organizations may be intrinsic to the method of solving problems, but it should be emphasized that there is usually more than one way of decomposing a problem, and that therefore there is an equal number of possible hierarchies; moreover, these hierarchies do not coincide with either the hierarchy of roles or of command within the organization, even though they define functional subsystems within the system. One point appears central: the division of problems structures tasks from ‘macro’ to ‘micro’ in the sense that whenever a certain problem is to be solved it is represented as a combination of simpler problems, and so on recursively until a level of ‘minimum detail’ is reached where the problem yields an effectively executable task. This macro-micro feature has many interesting consequences (Egidi, 1989), but I shall stress here only one of them: the possibility of explaining the incompleteness of contracts. Note that when a contract is established between two agents, only very ‘generic’ characteristics are outlined, without entering into the details; the contracts on the labour market are a striking example of this feature. In general, as Coase (1937, p.392) notes: ‘The details of what the supplier is expected to do are not stated in the contract but are decided later by the purchaser.’ It is easy to find actual organizations based on very different ways of defining tasks, as emerges from the difference between the Tayloristic model of organization, which defines even the most specific detail of elementary operations, and the Japanese model which allows notable elasticity for continuous definition and redefinition of sub-tasks within a general framework. In both cases, the recursive nature of the division of problems allows for the incomplete formulation of projects and contracts presupposing, obviously, that human agents will be able to complete them, by resolving problems autonomously. Let us now try to link these ideas with the notion of technology; a useful approach is to define technology as specific knowledge applied to the realization of tools and machines. This definition enables us to distinguish clearly between technology and organization; the former can be studied as problem-solving activity strictly applied to the realization of machines involved in the production process, while the second has been defined as the process of division and coordination of human activities. The process of division and co-ordination of different tasks, either within the firm or among firms, is the result of a multipleagent process of learning and discovery; the procedures (routines) generated by this process are, as we have seen, very incomplete because of the nature itself of

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human knowledge. In special cases it is convenient and possible to give a detailed specification of a routine; in such a case, the procedure can be mechanized, i.e. executed by a physical device, a machine. A machine is, therefore, a physical device which executes one or more programmes, i.e. one or more perfectly detailed procedures. Of course, machines are mostly produced in order to substitute human activities, and this is possible only if such human activities can be completely specified in detailed procedures. Let us add some concluding remarks to the previous discussion. Within the organization the procedures defining the sets of tasks and sub-tasks to be realized are generally co-ordinated by a hierarchical system. This co-ordination concerns not only human activities, which are encoded in open (i.e. neither perfectly specified nor detailed) procedures, but also activities performed by machines, which are perfectly specified. In this case, the programme which allows the construction of a machine is a technology. 6 CONCLUSION Let us recall only some of the foregoing topics, which seem of central interest, and draw a partial conclusion. In the first part of the chapter I have recognized that knowledge incompleteness is a typical feature of human behaviour which arises not only from the subjective characteristics of human reasoning (bounded rationality) but also from the properties of formalized reasoning, i.e. from the general characteristics of computation. Therefore we have recognized that problemsolving activity cannot be performed in a completely routinized way; on the contrary, it must be based on an inductive search process, and consequently we can define innovation as ‘a selective search through large spaces of possibilities’ (Simon, 1989, p. 8). This approach has some important consequences for analysis of human behaviours in organizations, and mainly allows to show that plans and projects within firms are not merely executed by different subjects, but necessarily require a multi-agent process of learning; this feature allows us to re-define the relationship between market and hierarchies. Within organizations, as we have seen, two complementary processes take place so that projects can be fulfilled and given tasks performed: the division of labour and the co-ordination among the different divided parts. These are the two main features of the process of ‘organizational learning’ which any team must implement in order to perform given tasks. In an evolutionary context the two processes are realized in very different proportions by markets and by organizations. Markets mainly co-ordi-nate different activities, while organizations are characterized by the division of activities in different sub-tasks.

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This difference is due to the fact that the internal organization of a firm pursues its goals in at least a partially conscious way: to the extent that they are ‘islands of conscious power’ (Robertson, 1930, p. 85), firms are able to project the forms of co-operation which will allow them to realize their pre-established goals; that is to say, they are able to project their internal organization, and to modify this organization whenever it does not suit their purposes. Firms perform a more complex function than the market—the function of projecting the division of labour—while the market limits itself to co-ordinating the activities of its agents within a structure of the division of labour that has already been established. NOTES 1 This research was supported by a National Research Project of the Italian Ministry of Research. 2 Apart from the ‘classical’ papers by Simon (1976, 1979), in a recent work the same author gives an extremely clear treatment of theoretical developments of the bounded rationality approach (Simon, 1989). 3 See Heiner (1983, 1988). 4 Lucas (1976, 1978), Muth (1960, 1961); see also Lucas (1987). 5 Arrow (1978). 6 This specific definition is provided by Dosi and Egidi (1988). The traditional definition assumed in the General Equilibrium framework is exposed in Arrow (1971) and discussed in Diamond and Rothschild (1978). Further important issues are raised in Arrow (1982). 7 See Simon (1973). 8 For a broad discussion of this topic, see Pearl (1984). 9 See also Church (1936), and for a review of the argument Cutland (1980), Traktenbrot (1963), Hopcroft and Ullman (1979). 10 This result does not imply that one finds a solution, but ‘probably’ a solution exists: on the contrary, it is a formal and very strong statement which asserts that, for a given class of problem, it is impossible for everyone ever to come up with a solution. 11 See for an introductory review Sharples et al. (1989), and for a representative example from the most important research fields, Pearl (1984). 12 See Michalsky et al. (1986) and Rumelhart and McClelland (1988) for a sample of different opinions. 13 ‘Routine’ is here a synonym of ‘not completely specified procedure’; see Nelson (1982), Nelson and Winter (1982), Winter (1982). 14 I will discuss the research process by decomposition of problems in sub-problems. For a technical exposition see Nillson (1971, 1980), Pearl (1984).

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BIBLIOGRAPHY Arrow, K.J. (1971), ‘Economic Welfare and the Allocation of Resources for Invention’, in F.Lamberton (1971), pp. 164–182. Arrow, K.J. (1972), ‘Exposition of the Theory of Choice under Uncertainty’, in C.B.McGuire and R.Radner (eds), pp. 19–55. Arrow, K.J. (1978), ‘The Future and the Present in Economic life’, Economic Inquiry, No. 16, pp. 157–170. Arrow K.J., (1982), ‘Risk Perception in Psychology and Economics’, Economic Inquiry, Vol. XX, pp. 1–9. Arthur, W.B. (1985), ‘Competing Technologies and Lock-in by Historical Events. The Dynamics of Allocation Under Increasing Returns’, Stanford, Stanford University, CEPR Discussion Paper. Church, A. (1936), ‘An Unsolvable Problem of Elementary Number Theory’, American Journal of Mathematics, Vol. 58, pp. 345–363. Coase, R. (1937), ‘The Nature of the Firm’, Economica, Vol. 4, pp. 386–405. Cutland, N.J. (1980), Computability. An Introduction to Recursive Function Theory, Cambridge, Mass., Cambridge University Press. Davies, M. (1958), Computability and Unsolvability, New York, McGraw-Hill. Diamond, P. and Rothschild, M, (eds) (1978), Uncertainty in Economics, Orlando Fla., Academic Press. Dosi, G. (1982), ‘Technological Paradigms and Technological Trajectories. A Suggested Interpretation of the Determinants and Directions of Technical Change’, Research Policy, June, Vol. 11, No. 2, pp. 147–162. Dosi, G. and Egidi, M. (1988), ‘A Bounded Rationality Approach to Uncertainty and Innovation’, in M.Campanella (ed.) Between Rationality and Cognition, Geneva, Meynier. Dosi, G., Freeman, C, Nelson, R., Soete, L. and Silverberg, G. (eds) (1988), Technical Change and Economic Theory, London, Frances Pinter. Egidi, M. (1989), ‘The Firm as Self Governing Organization’, Mimeo, University of Trento. Gersting, J.L. (1987), Mathematical Structures for Computer Science, New York, Freeman and Co. Heiner, R.A. (1983), ‘The Origin of Predictable Behaviours’, American Economic Review, Vol. 73, No. 4, September, pp. 560–595. Heiner, R.A. (1988), ‘Imperfect Decisions, Routinized Behaviour and Inertial Technical Change’, in G.Dosi et. al (1988), pp. 147–169. Hogart, R.A. and Reder, M .W. (1987), Rational Choice, Chicago, University of Chicago Press. Hopcroft, J. and Ullman, J.D. (1979), Introduction to Automata Theory, Languages, and Computation, Reading, Mass., Addison-Wesley. Lamberton, F. (ed.) (1971), Economics of Information and Knowledge, London, Penguin Books. Langley, P. Simon, H.A., Bradshaw, G.L. and Zytkow, J.M. (1987), Scientific Discovery. Computational Explorations of Creative Processes, Cambridge, Mass., MIT Press. Lewis, A.A. (1985), ‘On Effectively Computable Realization of Choice Functions’, Mathematical Social Sciences, Vol. X.

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Lucas, R.E. (1976), ‘Econometric Policy Evaluation: A Critique’, in K.Brunner and A.H.Metzler (eds) The Phillips Curve and Labor Markets, Amsterdam, NorthHolland. Lucas, R.E. (1978), ‘Asset Prices in an Exchange Economy’, Econometrica, Vol. 46, pp. 1429–1446. Lucas, R.E. (1987), ‘Adaptive Behavior and Economic Theory’, in R.A.Hogart and M.W.Reder (1987). McGuire, C.B. and Radner, R. (eds) (1972), Decision and Organization, Amsterdam, North-Holland. Marris, R. and Mueller, D.C. (1980), ‘The Corporation, Competition, and the Invisible Hand’, Journal of Economic Literature, Vol. 18. Metcalfe, S. (1984), ‘Evolution and Economic Change’, in Y.A.Silberston (ed.) Technology and Economic Progress, London, Macmillan, pp. 54–850. Michalski, R.S., Carbonell, J.G. and Mitchell, T.M. (1986), Machine Learning, Palo Alto, Calif., Tioga Publishing Company. Minsky, M. (1967), Computation. Finite and Infinite Machines, Englewood Cliffs, N.J., Prentice-Hall. Muth, J.F. (1960), ‘Optimal Properties of Exponentially Weighted Forecasts’, Journal of the American Statistical Association, Vol. 55, No. 290, pp. 299–306. Muth, J.F. (1961), ‘Rational Expectations and the Theory of Price Movements’, Econometrica, Vol. XXIX, No. 3, pp. 315–335. Nelson, R.R. (1982), ‘The Role of Knowledge in R & D Efficiency’, Quarterly Journal of Economics, Vol. XCVII, pp. 453–470. Nelson, R.R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., The Belknap Press of Harvard University Press. Neumann, J. von and Morgenstern, O. (1953), Theory of Games and Economic Behaviour, Princeton, Princeton University Press. Newell, A. and Simon, H.A. (1972), Human Problem Solving, Englewood Cliffs, N.J., Prentice-Hall. Nillson, N.J. (1971), Problem Solving Methods in Artificial Intelligence, New York, McGraw-Hill. Nillson, N.J.,(1980), Principles of Artificial Intelligence, Palo Alto, Calif., Tioga Publishing Company. Pearl, J. (1984), Heuristics, Reading, Mass., Addison-Wesley. Robertson, D.H. (1930), Control of Industry, London, Nisbet and Co. Rumelhart, D.E. and McClelland, J.L. (1988), Parallel Distributed Processing, Cambridge, Mass., The MIT Press. Sharples, M. et al. (1989), Computers and Thought, Cambridge, Mass., MIT Press. Simon, H.A. (1957), Models of Man, New York, Wiley. Simon, H.A. (1959), ‘Theories of Decision Making in Economics’, American Economic Review, Vol. IL, pp. 253–383. Simon, H.A. (1963), ‘Problem Solving Machines’, International Science and Technology , Vol. 3, No. 36, pp. 48–62. Simon, H.A. (1972), ‘Theories of Bounded Rationality’, in C.B.McGuire and R. Radner (eds) (1972). Simon, H.A. (1973), ‘The Structure of III-Structured Problems’, Artificial Intelligence, Vol. IV, pp. 181–201.

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Simon, H.A. (1976), ‘From Substantive to Procedural Rationality’, in S.Latsis (ed.) Method and Appraisal in Economics, Cambridge, Cambridge University Press. Simon, H.A. (1979), ‘Rational Decision Making in Business Organisations’, American Economic Review, Vol. LXIX, pp. 493–513. Simon, H.A. (1981), The Science of the Artificial (2nd edn), Cambridge, Mass., MIT Press. Simon, H.A. (1989), ‘Scientific Discovery as Problem Solving’, Peano Lecture. Sternberg, R.J. (ed.) (1984), Mechanisms of Cognitive Development, New York, Freeman and Co. Stiglitz, J. (1984), ‘Information and Economic Analysis: A Perspective’, Economic Journal, Conference Papers. Traktenbrot, B.A. (1963), Algorithms and Automatic Computing Machines, Boston, D.C.Heath (English trans. from Russian original). Williamson, O. (1975), Markets and Hierarchies, New York, Free Press. Williamson, O. (1985), The Economic Institutions of Capitalism, New York, Free Press. Winter, S.G. (1982), ‘An Essay on the Theory of Production’, in S.H.Hymans (ed.) Economics and the World Around It, Ann Arbor, Michigan University Press. Winter, S.G. (1987), ‘Adaptive Behaviour and Economic Rationality. Comments on Arrow and Lucas’, in R.A.Hogart and M.W.Reder (1987).

9 Time-saving and innovative processes Paolo Mario Piacentini

1 INTRODUCTION The explicit introduction of indicators of time-duration, which may refer to the whole of a production process or to a component phase of it, can help to narrow the gap between economists’ representation of innovative processes and the technical and organizational features of real production management. ‘Production-time’ and related concepts are potentially powerful tools of representation and could provide a new foundation for a description of production processes.1 Less ambitiously, this chapter proposes a brief investigation of the use of three categories of temporal length in innovation analysis. It only sketches out a formal analysis; a fuller treatment of some points may be found in a previous essay.2 Technical studies and the managerial literature, to which they give rise, often utilize indicators of temporal duration, considering particular phases or activities within a production process (including both phases of direct fabrication and others, such as design, development, etc.). The effects of technical and managerial innovations are being more and more related to efficiency in terms of time-saving, as measured by some of these indicators.3 In this chapter, we shall focus on three ‘duration’ measures: 1 ‘Cycle time’: defined as the time required for the completion of the operation (s) (e.g. machining a piece, assembling a unit, testing a product) forecast for a phase of a wider production process. The phase is conceived here as the unit into which the process may be broken down within a given layout and planning of production.4 2 ‘Switching time’: defined as the interval required for the partial or complete resetting or reconversion of a plant (e.g. change of tools, reprogramming of machines, retraining of staff) when switching products. The extent of an implied product innovation and the related adaptation of the process may

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seem ambiguous unless further hypotheses are introduced; broadly, we may distinguish between a ‘short-run’ or static substitution among products (or models, or other sub-categories) within a given technology allowing multiproduct operation, and a ‘longer-run’ dynamic reconversion entailing a major change of the production line. 3 ‘Lead time’: defined as the interval between the time a firm decides to launch a new product and the time that product actually appears on the market. ‘Lead time’ will thus include all those phases of product development, such as design, specification, prototype testing, and so forth, that precede production itself. As will be discussed below, most of the current process innovations (and the attendant terminology) may be viewed and potentially measured in terms of reduction of one of these time indicators. Considering the production process in a broader sense, so as to include design, development, and other phases, in addition to manufacturing itself, a general category of time-saving may be conceived, which is applicable to most incremental innovations. Only radical innovations which imply incommensurability between the situations ex ante and ex post may fall outside the scope of this category. Analytical concepts that supplement or complement that of efficiency, such as the notion of flexibility of production processes, are shown to be partly reducible to the measurement of timesaving, particularly with reference to ‘switching time’. 2 CYCLE TIME Cycle times refer to component phases of the production process.5 A problem arises in co-ordinating the flow of work in progress among these phases, and its correct solution will be crucial for the efficiency of the process as a whole. Citing relevant sources on this point,6 this section concentrates on cycle time’s applicability as a means of practical evaluation of cost efficiency. Time saving will be shown to play an autonomous role, although it complements conventional measures of factor use and productivity. Considering a very simplified environment, we shall assume that the technical configuration of the phase does not change in the short run and, in particular, we shall assume that this configuration consists of a ‘machine’ M assisted by units of labour (‘men’). The length of the actual utilization of the plant, H, over the period of reference (e.g. hours per week) will also be assumed as given.7 With cycle-time, tc, measured in terms homogeneous to H (that is, in hours and fractions of an hour), and defined as the length of time required for the completion of the operations forecast for this particular phase of the (partial) product, the cost of production per unit/phase may be expressed as follows: (1)

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where R is a ‘rental cost’ of machine M for the period of reference (i.e. R/H is the cost per machine-hour) and w is the cost per man hour (i.e. hourly wage). R, and tc identify three distinct sources of cost reduction that may be related to process innovations in a broader technical and/or organizational sense. If any of these parameters is lowered as a consequence of innovation, while the other two remain constant, we shall have, respectively, pure cases of capital saving, labour saving and time-saving. Mixed forms, implying their combination, may obviously be considered, and will presumably prevail in the real processes. A trivial example of time-saving is offered by ‘time cutting’: cycle time is reduced through more intensive exploitation of machines and men, with all other technical conditions unchanged. A process of ‘learning by doing’ may still be viewed as a case of time-saving. But other cases are less immediate: an improvement in the performance of the machine at given costs and manning requirements (e.g. the increase in a computer’s velocity of calculation) will again result in a reduction of tc.8 Current innovations linked to the introduction of microelectronics in factory and office processes, which conventional analysis by ‘factors’ depicts as being both labour and capital saving,9 may actually turn out to be prevalently timesaving. Certain ‘mixed’ forms of innovation may be particularly significant in current technological trends: automated execution of a phase may be slower (e.g. a single robot requiring more time to complete a set of welding operations that were previously done by a team of workers), but labour saving (reduction of I may more than compensate the increase of tc (and, presumably, of R). The foregoing argument is summarized in Figure 9.1, where a given

Figure 9.1 Time and factor savings as movements along and of the cost curve

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technical configuration is represented by an iso-factor cost curve in a quadrant [C, 1/tc] movements along the curve and of the curve distinguish time and factor savings respectively. 3 SWITCHING TIME Switching time comprises the duration of the not directly productive operations, such as reprogramming, refitting, retraining, etc., which the process requires when products are switched or modified. Again, this section will consider a ‘short-run’ option involving a plant with a potential for multiproduct operation (e.g. a ‘flexible manufacturing system’) that is activated through successions of batches (or ‘lots’) of different products (or product specifications). However, the analytical framework may also apply to cases of plant reconversion in the long run. The diversity among the products will mainly consist of differences within a ‘family’ or a ‘group’ whose common characteristics are particularly suited to joint production.10 Optimal lot-sizing according to cost minimization criteria is a standard tool of production (and inventory) management.11 Considering a product in isolation, let us monetize as a fixed cost (F) the charges deriving from the non-directly productive use of factors for preliminary setting-up of the shop. The optimal lot size will be determined by the combined minimization of setting-up costs F and stocking costs S, assumed, here, to be simply proportional to the lot size (see Figure 9.2). Reduction of ‘switching-time’ (ts) in a new configuration will result in a lower F/L curve, thus implying a lower optimal lot L**. ‘Time-saving’ for ts will enhance cost savings by increasing the productive use of factors (less indirect labour, less ‘down’ time for machine resetting), and allowing savings of working capital in terms of stock reduction. The growing relevance, of such a saving visà-vis general cost performance, is commonly stressed in the managerial literature.12 We now consider an analytic framework which allows a generalized representation of ‘time accounting’ in the case of multiproduct operation. Extending the single product case, the production process can be viewed as the application of j=1, 2,…, m ‘phases’ to i=1, 2,…, n ‘products’; a matrix Tp can be derived, where the element tij gives the (direct) cycle time for application of phase j to product i; where tij=0 phase j is not relevant or applied for production of i. Summing the lines yields total production time for products. Switching times arising within a multiple production programme must be added to direct production times for all phase/product combinations. At each phase j we, therefore, associate a matrix :

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Figure 9.2 Optimal lot sizing

where element t(j)hk gives the switching time on the jth station when production is switched from h to k. Tc matrices need not be symmetrical, since it may be possible that . In general terms, phenomena of time-saving may now be referred to reductions in any element of Tp and Tc’s. From a slightly different view-point, reductions in switching times, in particular, can be seen as increases in the flexibility of the process. The category of flexibility, today much in debate, may thus be treated as a form of time-saving, at least in this restricted sense of ‘range’ flexibility.13

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We are aware that the methodology under consideration affords mainly a static representation of given production processes. The construction of behavioural or choice models and a more precise definition of dynamic trajectories would be a subsequent and far more difficult step in the analysis. We conclude this section, however, by proposing a generalization of the problem of choice of optimal lot. Take a production programme defined as: specifying volumes of production of items 1, 2,…, n to be produced within a planning horizon (e.g. a week). Within given technical constraints (e.g. availability of production capacity, feasibility of joint production), the production programme may be carried through any succession of lots or batches L, so that:

With reference to a particular phase of the process, we consider two ‘extreme’ cases, within which all other options on lot partition and consequent frequencies of resetting operations will fall.14 (a) A pure pull system: production is started, piece by piece, upon activation by demand downstream; in the limiting case, batches will be constituted by single units and final stocks will be zero within the reference period. In this extreme version of a ‘just-in-time’ conception of production, we shall have Li,=1 ; Si=0. (b) A pure push system: the production programme is executed before demand arises. In the limiting case, all the completed items are stocked in warehouses, and sales operations start after the production interval. In these conditions, production of different items will each be scheduled, in sequence, until the completion of planned volume Xi.. Partition in smaller lots would increase switching costs without any saving of stocking costs. We shall, therefore, have Li=Xi, ni=1 for all i. Simplifying again by assuming that stocking costs are proportional to volumes and length of stocking, and further more assuming that production time for lots Li=Xi are equal for all i(t*), we obtain: (2) Given the further simplification according to which switching costs are constant within any particular pair (h, k) of products , we can derive the maximum and minimum values for total switching costs as:

Figure 9.3 gives a rough approximation of the trade-off.15

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Figure 9.3 Switching costs graph

In actual facts, demand forecasts and production decisions interact in many ways, resulting in a great range of optimal strategies of lot planning. In addition, it must be stressed that the distinction between ‘pull’ and ‘push’ systems may become less stringent, when shorter and shorter intervals of reprogramming of production schedules are allowed. Even within a push production (i.e. before demand), a steady reduction of stocking costs may be pursued by shortening the planning interval, which will automatically lower stocks of finished products.16 Again, flexibility of the technology and the savings thus allowed in the more frequent resetting operations now required will be the key factors in determining the feasibility of such an option. 4 LEAD TIME Lead time mainly applies to dynamic analysis of product innovations; essentially, it concerns the interval between the decision to develop a new product or lines of products and the time they actually become available on the market, including phases of design, specification, prototype testing, and so on.

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Such a concept is surely not novel to economic analysis, at both the theoretical and the applied level, although the terminology may differ.17 Time-saving, specifically referred to lead time, will imply a quicker response in the effective supply of an innovative product for which demand is anticipated; profitable advantages will arise from the possibility of reaping the market demand in an early stage of development of the product cycle, characterized presumably by high income and low price elasticities. We again propose a preliminary analytical setting of the problem. Let us now suppose that demand for the new product (or for an improved version of an existing product) is perceived by the firm. To each producer, faced with the decision of implementing development of the new product, we may associate an ex ante schedule of total revenues for the innovation, as a function of time, beginning from the initial decision point. We have drawn the revenue function R(t) as an union of linear segments: such a form should describe the decrease in the instantaneous revenue expected for more remote periods in the future (see Figure 9.4). This may reflect expectations both on the demand side (saturation, etc.) and on the supply side (entry of imitators, etc.). The choice of a ‘broken-line’ function stems from the consideration that factors that determine a reduction of revenue per unit of time are better represented by discrete events, such as the entry of a competitor, the lapsing patent coverage, tariff reductions, etc. The R(t) function will thus be as following (3) In a cost function, on the other hand, costs relative to a gestation period and costs in the operation period should be distinguished. For the sake of simplicity, we represent costs in the gestation period as a fixed cost F, paid in at the initial moment of the innovative decision. F may be considered as a sum directly advanced by a ‘venture capitalist’ for the undertaking. The operation costs, simplified to be linear, will occur after completion of lead time . Cumulated cost function C(t) will thus appear as Figure 9.5. At this point we introduce a crucial hypothesis: the inclination of the revenue function depends exclusively on the moment of time in the future, as it reflects market opportunities and developments that are exogeneous to the firm. The ex post revenue function, R*(t), will ‘start’ from moment and will be constituted by segments parallel to ex ante curve R(t), with coincident points of change in the inclination (t1, t2,…) (see Figure 9.6):

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Figure 9.4 The ex ante revenue function

Figure 9.5 The cumulated cost function

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Figure 9.6 Ex ante and ex post revenue functions

(4)

A longer lead time will thus mean wider gaps between potential (ex ante) and actual (ex post) revenues, since favourable market opportunities in the earlier periods will not be exploited; the value of time-saving in terms of lead time can thus be measured by where considering any R*(t) together with C(t), we finally derive the (expected) pay-off time , where and the optimal period of investment (where ) (Figure 9.7). The analysis outlined above ignores temporal discount factors and the details of cost formation during the gestation period; in reality, F and may be correlated so that a higher investment effort (higher F) could accelerate the realization of the project, lowering . Such a trade-off may be interpreted as a problem of choice between an innovative (higher F and lower) and an imitative strategy (vice versa).

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Figure 9.7 The optimal period of investment

It should be stressed that in this dynamic context, the category of flexibility can again be treated as a particular form of time-saving: more flexible positions will be characterized by shorter lead times when new product developments are undertaken.18 Complementarity between intensity of investment and flexibility, admitted in a static context by other works, could be transposed to a dynamic environment characterized by product innovation.19 These notes certainly do not exhaust the potential impact of ‘time-saving’ categories in connection with analysis of productive processes. We believe, in addition, that from a microeconomic and descriptive base, notions of temporal efficiency could be extended to a sectorial or aggregate level. Fuller formal definition of the categories introduced here and the testing of their relevance to concrete analysis of innovative processes should prove to be an interesting road for research, whose methods and targets remain largely to be explored. NOTES 1 The explicit consideration of the length of use of material and human inputs in conjunction with technology, taken to mean a sequence of actions designed to

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2 3

4

5

6 7

8

9 10

11 12

produce a product, follows the model of the production process proposed by N.Georgescu-Roegen (Georgescu-Roegen, 1970, 1976, 1986). That conceptual framework strand of research to which this chapter belongs seeks mainly to arrive at definitions and categories of economic efficiency which may be compared with the technical parameters of the processes. See Piacentini (1987). The importance of time-saving is underlined most of all in the literature analysing the technical and economic efficiency of new computerized processes in industrial manufacturing, design, etc. (FMS, CAD-CAM, CAPP, etc.). See, inter alia, Ranta and Tchijov (1989) and Stalk (1988). Also among the indicators of efficiency listed in Ayres (1989, p. 50), see those that contemplate temporal parameters. In Scazzieri (1983, p. 599), the ‘atomistic’ unit of the production process is the ‘task’, defined as an ‘operation performed without interruptions, on some particular object. It is not further divisible (at least for the purposes of the analysis in hand) …’ ‘Phase’, as used in this chapter, refers instead to processes of specialization in the use of factors within a technical option. Elementary ‘tasks’ thus may be more or less integrated within a phase. In Adam Smith’s classical example on the manufacture of pins, such a distinction is clearly outlined: the eighteen ‘tasks’ which Smith identified may be included into different specialized phases, according to the degree of division of labour (Smith 1976, p. 6). An ex ante cycle time, referring to optimal conditions from an engineering point of view, may be distinguished from an ex post cycle time, an average operation over an interval of time. In Japanese production accounting practices, cycle time is defined as hours of operation over pieces produced in the interval of reference. This is the focus of the model in Piacentini (1987), in particular pp. 391–393 and 397–399. The degree of utilization of the machine over time is certainly an important factor in determining the unit cost of the (partial) product per phase; we exclude it here only in order to focus on technical conditions in a narrow sense. Having simplified, excluding raw materials from the expression of cost, the latter should be considered as ‘factor cost per phase’. In all these cases, there is evidently an increase of output at a given factor cost. Describing these phenomena as cases of ‘time-saving’ does not necessarily conflict with the literature’s earlier categories of efficiency (e.g. X-efficiency, learning curve, etc.). See Yakushiji (1984) for interesting comparisons of categories of static and dynamic efficiency, as well as the seminal definitions and concepts found in Farrel (1957), Leibenstein (1966), Arrow (1962). That the ‘microelectronics revolution’ implies both capital and labour saving is widely recognized. See, for example, Soete and Dosi (1983). Planning of a multiproduct plant or line is more and more often based on prior analysis of complementarities within a group or ‘family’ of products. For a brief description of the concepts utilized within these ‘group technology’ methods, see Kusiak (1987). Technical descriptions of the principles of optimal lot-sizing are found in Mestoudjian and De Crescenzo (1986), Schonberger (1982, pp. 19–20). According to a Japanese case study of a Hitachi plant the cost of the product in advanced electrical engineering can be broken down roughly as follows: 50 per cent, costs of incorporated materials; 25 per cent, indirect costs (including indirect

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13

14

15

16

17

18 19

labour); 15 per cent, direct labour cost (Kuwabara and Nagasaka 1986). Under these conditions, savings on circulating capital through less ‘work in process’ and ‘waste’ may clearly become more important than marginal savings on direct labour. See Gerwin and Tarondeau (1984) for a taxonomy of flexibility categories. Chapter 2 of Amendola et al. (1990) is dedicated entirely to a survey of theoretical categories and empirical measures of flexibility. The distinction made here between ‘pull’ and ‘push’ systems again draws on studies of Japanese manufacturing systems. Quoting from Schonberger (1982, p. 16): ‘Japanese industry produces small quantities just-in-time; Western industry produces massive quantities just-in-case.’ The examples we develop take two extreme options. A problem of ‘optimal sequencing’ of production batches could be posed if switching costs were made variable, in accordance with different sequences of products. To illustrate convergence between pull and push systems when the planning horizon (and, consequently the possibility of revising the production programme) is reduced, we cite the emblematic case of two of Japan’s leading car producers, Toyota and Nissan. The so-called ‘Toyota production system’ (see Monden, 1981; Schonberger, 1982, in particular the Appendix on the ‘Kanban’ system; Ogawa, 1986) has been presented as a highly advanced embodiment of the ‘pull’ system; Nissan, on the other hand, originally adopted ‘push’ procedures, but has utilized the increased flexibility of plant and logistics to reduce the minimum time required for a change of production schedule (according to a Nissan Co. mimeo, such changes take a minimum of four days). The ‘new-Austrian’ model of ‘Capital and Time’ (Hicks, 1973) contemplates a ‘period of construction’ of capacity for a new process, in which inputs are employed but no output emerges. The model proposed by Amendola and Gaffard (1988, Chapter 4) distinguishes between a process’s ‘construction phase’ and its ‘utilization phase’. On an applied front, ‘lead time’ was investigated in early studies on investment decisions (e.g. Mayer, 1960). Such a notion broadly corresponds to that of ‘alterability’, defined in Stigler’s seminal article (Stigler, 1939, pp. 317–318). For models incorporating a static trade-off between size of investment and flexibility, see Mills (1984) and De Nicole and Piacentini (1988).

BIBLIOGRAPHY Amendola, M. and Gaffard, J.L. (1988), The Innovative Choice, Oxford, Blackwell. Amendola, M., Ingrao, B., Piacentini, P. and Potì, B. (1990), Automazione Flessi-bile: Analisi ed Interpretazione delle Tendenze a Livello Internazionale, Milano, F.Angeli. Arrow, K.J. (1962), ‘The Economic Implications of Learning by Doing’, Review of Economic Studies, June, pp. 153–173. Ayres, R.U. (1989), ‘Technology Forecast for Cim’, Manufacturing Review, March, pp. 43–52.

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Baybarsi, I. (1986), ‘An Efficient Heuristic Method for the Simple Assembly Line Balancing Problem’, International Journal of Production Research, No. 1, pp. 149–166. De Nicole, G. and Piacentini, P. (1988), ‘Scelta d’Impresa e Trade-off fra Costi Fissi e Flessibilità’, Atti della terza riunione scientifica dell’Associazione Italiana Economisti del Lavoro, Arco (Trento), Giugno. Farrel, M.J. (1957), ‘The Measurement of Productive Efficiency’, Journal of Royal Statistical Society (Series A), pp. 253–290. Georgescu-Roegen, N. (1970), ‘The Economics of Production’, American Economic Review, May, pp. 1–9. Georgescu-Roegen, N. (1976), Energy and Economic Myths: Institutional and Analytical Economic Essays, New York and Oxford, Pergamon Press. Georgescu-Roegen, N. (1986), ‘Man and Production’, in M.Baranzini and R. Scazzieri (eds), Foundations of Economics: Structures of Inquiry and Economic Theory, Oxford, Blackwell, pp. 247–280. Gerwin, P.D. and Tarondeau, J.C. (1984), ‘La flexibilité dans les processus de production; le cas de l’automobile’, Revue Française de Gestion, No. 46, pp. 37– 46. Hicks, J.R. (1973), Capital and Time, A Neo-Austrian Theory, London, Oxford University Press. Kusiak, A. (1987), ‘The Generalized Group Technology Concept’, International Journal of Production Research, No. 4, pp. 561–569. Kuwabara, Y. and Nagasaka, K. (1986), ‘Kanseki bumon no kosto asshuku, shitzukòjo ga nerai; Kagi wa database Kochiku to jinzai ikusei’, Nikkei Mechanical, Vol. 4, pp. 52–57. Leibenstein, H. (1966), ‘Allocative Efficiency Versus X-Efficiency’, American Economic Review, June, pp. 392–415. Mayer, T. (1960), ‘Plant and Equipment Lead Times’, Journal of Business, April, pp. 127–132. Mestoudjian, J. and De Crescenzo, J. (1986), La Gestion de Production Assistée par Ordinateur, Vol. I, Paris, Editions de L’Usine Nouvelle. Mills, D.E. (1984), ‘Demand Fluctuations and Endogeneous Firm Flexibility’, Journal of Industrial Economics, Vol. 33, pp. 55–71. Monden, Y. (1981), ‘Adaptable Kanban System Helps Toyota Maintain Just-In-Time Production’, Industrial Engineering, May, pp. 29–46. Nissan Corporation (n.d.), ‘Nissan jid sha, Murayama Koj ni okeru seisan kanri systemu’, Mimeo. Ogawa, E. (1986), ‘Kanban seishin no Kyoryoku ni ugoku. CIM ye no torikumi wa dantaitekini’, Nikkei Mechanical, No. 4, pp. 102–108. Piacentini, P. (1987), ‘Costi ed Efficienza in un Modello di Produzione a Flusso Lineare’, Economia Politica, No. 3, pp. 381–405. Ranta, J. and Tchijov, I. (1989), ‘Economic and Success Factors of Flexible Manufacturing Systems: The Conventional Explanations Revisited’, Mimeo, Laxenberg, liasa. Scazzieri, R. (1983), ‘The Production Process: General Characteristics of Taxonomy’, Rivista Internazionale di Scienze Economiche e Commerciali, July, pp. 597–609. Schonberger, R.J. (1982), Japanese Manufacturing Techniques: Nine Hidden Les-sons in Simplicity, New York, The Free Press.

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Smith, A. ([1776] 1976), An Enquiry into the Nature and Causes of Wealth of Nations , edition 1976, Oxford, Clarendon Press. Soete, L. and Dosi, G. (1983), Technology and Employment in the Electronics Industry, London, F.Pinter. Stalk, G. (1988), ‘Time: The Next Source of Competitive Advantage’, Harvard Business Review, July–August, pp. 41–51. Stigler, G.J. (1939), ‘Production and Distribution in the Short-run’, Journal of Political Economy, Vol. 47, pp. 305–327. Tchijov, I. and Scheinin, R. (1989), ‘Flexible Manufacturing Systems: Current Diffusion and Main Advantages’, Technological Forecasting and Social Change, Vol. 35, pp. 277–293. Yakushiji, T. (1984), ‘Reshuffling Firms for Technology? An Aggregate Time-Series Analysis of B.Klein’s “Dynamic Efficiency”’, in M.Aoki (ed.) The Economic Analysis of the Japanese Firm, Amsterdam, North-Holland.

Part III Applied analyses of technological change

10 Black boxes and variety in the evolution of technologies Pier Paolo Saviotti

1 INTRODUCTION In most industrialized countries life is today very different from just two or three hundred years ago. The activities we perform and the artefacts we use and that surround us, form a landscape entirely different from that of our predecessors. The changes which have created the physical and organizational features of the society we live in have been true qualitative changes, giving rise to something which was not in existence before. Clearly, technological change has played a dominant part in the generation and diffusion of these developments. The new forms of transport, the new materials, the new drugs, the communication technologies which have become part of our everyday lives, owe their origin to changes in technology. Technological innovation has been responsible for the introduction of a variety of products, processes and materials. During the last two hundred years the way in which innovation contributes to the economic system has changed substantially. From a sporadic activity due to individual inventors innovation has become a systematic and quite often large-scale activity. Some industrialized countries spend up to 2–3 per cent of their GDP in R&D, which is one of the most important sources of innovation. In other words, it is not only innovation and technological change which make a growing impact on society but also the institutional arrangements by means of which they are generated have changed enormously. Both the total impact of innovation and the changes in the institutional arrangements for its generation imply that technological change plays a fundamental role in economic development. However, while most economists would agree in principle on the importance of technological innovation as a component of economic development, differences exist concerning the precise role it plays and its theoretical description. Orthodox economics has generally conceptualized technological innovation as being generated exogenously from the economic system and as contributing to increasing production efficiency. On

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the other hand, in Schumpeter’s theory of economic development, which forms the basis for modern evolutionary economics, the role of innovations as agents of change and of disequilibrium is fundamental. The discussion which follows in this chapter is centred around two themes, those of black boxes and of variety, which are considered extremely important for the theoretical understanding of the role of technological innovation in economic development. The first of the themes, that of the black boxes, comes from an expression used by Rosenberg (1982) and is used here as a metaphor for the image economists traditionally had of technology. To describe this metaphor we can begin with the definition of production as a transformation of inputs into outputs used in orthodox economics. The inputs enter the black box and the outputs leave it. The transformation takes place inside the box itself in a way which is not known to the observer of the technology. This image of technology allows one to define a concept of efficiency as the ratio of outputs to inputs, or the quantity of outputs obtained from one unit of inputs. In the course of time technological change leads to an increasing production efficiency and it is consequently possible to increase the quantity of outputs obtained from the same quantity of inputs (see for example, Coombs et al., 1987). Within this image of technology and of production it is not possible to see how the transformation of inputs into outputs takes place at a given time or how this transformation changes in the course of time. In other words, in the traditional representation of technology and of production, it is their effects and not their internal structure which are considered relevant for economic purposes. Thus not only has technology traditionally been considered by economists as exogenous to the economic system, but its development dynamics have been oversimplified by neglecting the interplay of its internal structure with the economic environment. Like all economic models that of production and of technology is a simplification of reality. Like all models it can be expected to be a good approximation in some situations, and a less good one in others. For example, if innovations were sporadic events, due to inventors and innovators who, although they had an economic motivation, did not have the ability or the capacity to monitor economic events adequately, an exogenous representation of technology might be very appropriate. Such a situation might have been more typical at the time of the Industrial Revolution or during part of the nineteenth century. If this were the case it could explain the high degree of consensus amongst economists and historians of the time about the usefulness of technological change as an ingredient of economic growth and the relative lack of interest about the mechanisms by which this contribution was made (Mathias, 1983). Whether or not this was the situation at that time, it has subsequently changed towards a much greater involvement of economic criteria in the planning, design and implementation of innovative activities from their very inception. While research activity aimed at understanding natural mechanisms is still being conducted, the majority of R&D is now carried out in industrial research laboratories (Hounshell and Smith, 1988). Industrial R&D is clearly economically motivated

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in the sense that the choice, timing and implementaton of projects take economic factors into account. In this situation an exclusively exogenous representation of technology has become inadequate. An attempt to ‘endogenize’ technology into the economic system has been made by means of the induced innovation theory (Binswanger, 1974; Binswanger et al., 1978). However, while in this theory technological change was influenced from the very outset by economic factors (i.e. the relative prices of inputs) the technology itself was represented as a combination of inputs (capital and labour). In this sense, therefore, the economic relevance of technology was pushed back to the stage of invention, but the internal structure of the technology and of its changes remained as unexamined as before. A theory of demand that incorporates the multiple wants which complex products can satisify has been developed by Lancaster (1966, 1971). The framework presented in this chapter is related to this theory, but it goes beyond it in analysing the internal structure of technologies, which was completely absent in Lancaster’s theory. The division of characteristics into two types, which will be presented later in this chapter, includes characteristics which are demand related (services) and characteristics which are supply related (technical internal structure). The framework presented here encompasses both the supply and the demand side of technological characteristics. In this chapter an attempt is made to go beyond the previous representation and to open up the black box of technology, providing a conceptual framework which is systematic and generalizable. Such a framework was initially developed by Saviotti and Metcalfe (1984) and was subsequently extended and applied to a number of technologies (Saviotti and Bowman, 1984; Saviotti, 1985, 1986, 1990b). To describe this framework it is useful to begin by discussing the meaning of technology. Naturally, the aim of this discussion is not to provide a comprehensive definition of technology, but to establish some essential points on the basis of which the framework is constructed. To begin with technology is constituted by both knowledge and artefacts. The firm, the institution most commonly responsible for the generation and the implementation of technology, is constituted by a number of individuals, each of whom has some knowledge. The firm as a whole contains an amount of knowledge, which is different from the one of all its individual members. This knowledge, which is very firmspecific (Pavitt et al., 1989) and is based on various forms of learning, determines the nature of the firm’s products. The collective knowledge of the firm is called its knowledge base (Metcalfe and Gibbons, 1989), while the firm’s products represent its revealed technological performance (RTP) (ibid.). This chapter will be predominantly concerned with RTP and not with the firm’s knowledge base.

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Figure 10.1 A product as two sets of characteristics, technical and service, and a pattern of imaging

2 A CHARACTERISTICS REPRESENTATION OF TECHNOLOGY To explain the nature of the framework let us begin with an example. A bird and an aircraft have both similarities and differences. Both of them fly. In a general way we could say that they supply air transport services. However, the ways in which they do this are very different, and the nature of this difference depends on their internal structure. The elementary parts and the internal mechanisms of the aircraft and of the bird are very different. Many other similar examples can be found, such as mechanical and electronic watches, a television based on vacuum tubes or on semiconductors, a nuclear and a thermal power plant, etc. What can be generalized from these and from a large number of other examples is the fact that these different technologies provide similar services to their users, but have a qualitatively different internal structure. Indeed, two technologies are different when their internal structures are different. Therefore, in general we can characterize a technology by means of its internal structure and of the services that it provides to its users. In this sense a technology can be compared to a complex system, having an internal environment, an external environment and a boundary separating them (Simon, 1981; Saviotti, 1986). Second, technologies, and especially product technologies, which will be the object of this chapter, are very complex and cannot be adequately represented by means of one characteristic. They are multicharacteristics products. Two sets of characteristics can then be used to represent the technology, one corresponding to the internal structure and one corresponding to the services. These two sets of characteristics are called technical and service characteristics respectively (Figure 10.1). In terms of the previous comparison between a technology and a complex system, technical characteristics describe the internal structure and service characteristics of the services. The distinction between the internal structure and the outer interactions of a technology is meaningful for a number of reasons. First, the two sets of characteristics are different because it is only the internal structure/ technical

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Figure 10.2 A technological population in a bidimensional X space

characteristics which can be directly changed by designers/engineers. However, what is directly demanded by users of the technology is services. Consequently designers/engineers try to provide wanted services by manipulating technical characteristics. Technical and service characteristics are linked by a pattern of correspondence or imaging: by changing technical characteristics one causes changes in service characteristics. Therefore, there is an intimate relationship between the process of design and the technical/service characteristics imaging. Furthermore, when knowledge of this pattern of mapping is predominantly empirical, then past patterns of correspondence serve as guides for future designs. Patterns of mapping are therefore components of heuristics, routines (Nelson and Winter, 1982), of dominant designs (Abernathy and Utterback, 1975, 1978), of technological guideposts (Sahal, 1981a, 1981b) and of technological paradigms (Dosi, 1982). Further differences between technical and service characteristics are: (a) progress in science is more likely to affect technical than service characteristics; (b) costs are incurred in producing technical characteristics but prices are likely to be more related to service characteristics; and (c) imitation of a technology means imitation of its technical characteristics. As a consequence of this approach each product is represented by a number N of characteristics (leaving aside for the moment the distinction between technical and service characteristics). Consequently each product is a point in an N dimensional characteristics space or X space.

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The concept of population is very important in evolutionary theories. In a population approach it is not only the representative individual and average values which are important, as is the case in the typological approach commonly adopted in orthodox economics, but also the distribution of the properties across the members of the population. The characteristics framework presented here allows a very convenient representation of technological populations. A whole technology will be constituted by a population of relatively similar but nonidentical models. Different product models will therefore occupy different points in X space. The representative points of these models will constitute a cloud of points, which will be the representation of the technological population at time t (Figure 10.2). 3 TECHNOLOGICAL EVOLUTION Qualitative change, giving rise to new forms, has been previously indicated as the most important distinguishing feature of evolutionary theories. In the present case this will mean either the emergence of completely new technologies or a process of progressive internal differentiation and specialization of an existing technology. We can begin here to assume that a radically new technology will be represented by characteristics completely different from those of pre-existing technologies. The emergence of completely new technologies can be represented in an N+M dimensional X space, where N is the number of characteristics of the pre-existing technology and M that of the new one. According to the second mechanism for the generation of qualitative change, a technology which started its evolution by satisfying a relatively uniform set of demands could differentiate to such an extent that its different subsets would fit into separate niches. In this type of differentiation the similarity of the technologies would decrease in the course of time, even though all of them could still be represented by the same characteristics. In this case changes in a technology in the course of time will be reflected in changes in the position and in the shape of the cloud of points which constitutes its representation (Figure 10.3). In this chapter, situations similar to the second case will be analysed. In the previous discussion two different mechanisms according to which qualitative change in technologies can be generated have been presented. These mechanisms have been described without reference to a specific technology. They are likely to be applicable to the evolution of a very large number of technologies. It would be useful to be able to represent large numbers of processes of technological evolution in terms of a much smaller number of elementary mechanisms. These elementary mechanisms would then be like the bricks or the modular elements with which a building is constructed. In what follows some examples of elementary mechanisms are discussed in terms of the twin characteristics framework previously presented.

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Figure 10.3 The change in a technological population between times t1 and t2 in a bidimensional X space

Figure 10.4 Pure substitution in terms of a twin characteristics framework: T1= preexisting technology; T2=new technology

3.1 Elementary mechanisms and X representation

The elementary mechanisms previously mentioned can be more accurately described in terms of the characteristics approach presented in this chapter. 3.1.1 Pure substitution A new technology T2 replaces a pre-existing technology T1. T1 and T2 provide the same quality of services but have different internal structures/ technical characteristics. The advantage of T2 consists of providing the common services at a lower unit cost (Figure 10.4).

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Figure 10.5 Twin characteristics representation of inter-technology specialization: T1=old technology; T2=new technology

Figure 10.6 Twin characteristics representation of inter-technology specialization: T1= old technology; T2 = new technology

3.1.2. Specialization

T1 and T2 provide qualitatively the same services with a different distribution of service levels, or each one of them may provide a common core of services and some other services which are specific to each technology. T1 and T2 may have the same or different internal structures (intra- and inter-technology specialization) (Figures 10.5, and 10.6). 3.1.3 Partial substitution T2 provides the same services provided by T1 and some additional ones. This case is intermediate between 3.1.1 and 3.1.2 both logically and chronologically (Figure 10.7).

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Figure 10.7 Partial substitution in terms of a twin characteristics framework: T1= preexisting technology; T2=new technology

Figure 10.8 Twin characteristics representation of the emergence of a completely new product: T1=pre-existing technology; T2=new technology

3.1.4 Completely new product T2 has a qualitatively different set of both technical and service characteristics with respect to T1 (Figure 10.8). 3.1.5. Fundamental and adaptive variables Fundamental variables/characteristics of a technology are those which led to the introduction of the technology in the first place. Adaptive are those variables which improve the fit of the technology into its environment (Ashby, 1956; Saviotti, 1986). Examples of these different types of variables can be found in the motor car and tractor technologies. In both cases an initial period, during which convergence on a dominant design took place, was followed by another, in which further variables were added, which improved the fit of the technology into its environment. Examples of these changes are the addition of hydraulics and later of a cab to agricultural tractors, of improved suspensions and of lower

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cars to take advantage of the smoother road surfaces which are now commonly encountered in industrialized countries. It is possible to expect that similar patterns could be followed by other technologies. In other words, as a technology ages fundamental variables could become very similar in all models/members of the technological population while adaptive variables could constitute the locus of differentiation of the technology. A possible pattern of evolution would therefore be constituted by a decreasing intra-technology variance of the fundamental variables and by an increasing intra-technology variance of the adaptive variables. It is worth emphasizing that this pattern represents a variant of a life cycle model, such as that of Abernathy and Utterback (1975). According to their model, when the mature phase of the life cycle has been reached after the convergence on a dominant design, the locus of innovation shifts from product to process technology. According to the pattern analysed here the locus of innovation would move from fundamental to adaptive variables, which means between two different types of product characteristics. These changes of the locus of innovation, from product to process and from fundamental to adaptive, could take place simultaneously, so that a mature technology would be characterized by process and adaptive variables innovations: New technology Product innovations Fundamental variables

Mature technology Process innovations Adaptive variables

3.1.6 Radical vs. incremental; species vs. characters characteristics A very fundamental distinction in studies of technological change is that between radical and incremental innovation. In terms of the approach presented in this chapter a radical innovation is one that gives rise to entirely new technical characteristics, which need to be represented by different variables. Depending on the degree of detail with which it is represented, a radical innovation can give rise either to the appearance of entirely new characteristics or to a different value of a dummy variable which indicates an entirely different approach to a common function. For example, power to an aircraft can be supplied either by a piston engine or by a jet engine. The two technologies have qualitatively different internal structures and require different variables to be represented. The transition between the two has evidently been a radical innovation. At a different degree of detail the two technologies could either be represented by two different sets of variables or by two values of the dummy variable engine type. The latter approach is used in this chapter. All the characteristics representing a technology can then be classified as characters (which undergo only quantitative change) or species (which undergo qualitative change). Alternatively, changes in characters are incremental innovations and changes in species characteristics are radical innovations.

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4 VARIETY AND ECONOMIC DEVELOPMENT The second theme of this chapter is related to one of the fundamental mechanisms of economic development anticipated by Schumpeter. According to him innovations, which can be new products, new processes, new markets, new materials and new organizational forms, are the most important factors contributing to economic development (Schumpeter, [1912] 1934, p. 66). Such innovations constitute moments of disequilibrium (ibid. 64) or, as Schumpeter defined them later, of ‘creative destruction’ (Schumpeter, [1943] 1976, pp. 81– 86). The economic growth which would occur in the absence of innovations is, for Schumpeter, ‘overshadowed’ by the fact that development consists primarily in employing existing resources in a different way ([1912] 1934, p. 68). The role of innovations in allowing the process of economic growth to continue in the long term is, therefore, fundamental for Schumpeter. The role of innovations, is on the other hand, very different in the orthodox economics image of technological change. In this image, not only are technology and production black boxes whose internal structure and mechanisms are unknown, but technological change leads only to an increasing efficiency in performing a constant set of activities. The changes which take place over time are thus only quantitative. Economic growth, rather than economic development, is analysed in orthodox economics. The latter, as interpreted by Schumpeter, involves the generation of new forms, or, in other words, of qualitative change. There is consequently in Schumpeter a much-greater emphasis on qualitative change than in an orthodox approach to economic growth. Recently Schumpeter’s ideas have found a considerable degree of favour with students of technological change and with evolutionary economists (see for example Freeman et al., 1982; Nelson and Winter, 1982). In spite of this recent revival of interest for Schumpeter’s ideas, and of their general suitability for the analysis of technological change, a stumbling block to their more general adoption has been constituted by the lack of an analytical framework in Schumpeter’s thought (Hanusch, 1988). To develop such an analytical framework is, therefore, one of the most important lines of research for neoSchumpeterian and evolutionary economists. The following considerations constitute an attempt in this direction. Let us begin by examining the impact of the emergence of innovations on the economic system. To the extent that they are radically new they need to be described by different variables and categories, as it was seen in the previous part of this chapter. Once these types of innovations (new products, new processes, new materials and new organizational forms) have been introduced into the economic system they have made it qualitatively different and have possibly changed its diversity or variety. That variety will change, as a

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consequence of the introduction of any one of these innovations is probable but not certain. If no other product, process, material or organizational form has disappeared, the diversity or variety of the economic system is higher. There can be, however, processes which lead to a decrease in variety, such as the disappearance of an old product, and the resultant outcome or net variety depends on the balance between variety-creating and variety-destroying processes. We can think of this situation by means of a biological analogy. Let us consider the emergence of an innovation a birth and the disappearance of a previous form a death. The resulting or net variety of the economic system will be determined by the balance between births and deaths. In this case, however, we would not be dealing with a population in the proper sense of the term, i.e a collection of individuals having common characteristics, but with a set of qualitatively different entities. A change in the population is here replaced by a change in the number of the surviving types of entities. The likelihood that variety changes during the course of economic development is going to be discussed in what follows, but here we can already point to the importance of the concept of variety in the analysis of economic development. In this sense variety is an aggregate quantity which is influenced by the introduction of innovations into the economic system. It is, therefore, a very good descriptor—both of the processes of economic development presented by Schumpeter and of the processes of technological change which have been studied in particular in the last twenty years. Variety can be used to analyse both these types of processes and their relationship. The considerations presented so far are equally applicable to any of the types of innovations developed by Schumpeter. For simplicity in what follows attention will be focused more often on product innovations, although at this level of generality they are likely to be applicable to the other types as well. As was previously pointed out, one of the main deficiencies of the work by Schumpeter was the lack of an analytical framework. If the concept of variety has to contribute to the construction of such a framework, it has to be defined more rigorously. Given what was previously said, an approach to the problem would simply be to define variety as the number of distinguishable products surviving in the economic system at each time. In this sense variety would have to be the net variety of the system. Let us return then to the definition of variety as the number of distinguishable entities constituting the economic system at any given time. If we imagine to add new products to the system without eliminating any of the old ones, then the variety of the system as defined before will increase. We can expect the differential or marginal impact of the addition of new products to the system to decline as the number of pre-existing products, and therefore the previous variety, increases. In this case it would be more useful to define variety as the logarithm of the number of distinguishable elements of the system, as it is done in information theory (Ashby, 1956):

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Different definitions of variety are then possible. This discussion does not aim at finding an ideal definition of it, but at pointing out that different ones are possible. The two previous definitions are quite suitable for the purposes of this chapter. 4.1 Changes in variety during economic development Based on the previous definitions of variety we can start asking some interesting questions. For example, can we expect a systematic change of variety during the process of economic development? It is not easy to give a definitive answer to this question because there are no reliable data on the number of products, processes, materials, etc. which exist at any time in the economic system. On the other hand, a number of quite impressionistic observations can be made about products, processes, materials and organizational forms which did not exist one or two hundred years ago. If the considered time period seems long, we have to remember that the problem of the role of innovation in economic development, which the concept of variety should help us to analyse, is a long-term one. Examples of these innovations abound: aircraft, computers, motor cars, photographic cameras, radios, television, record players, have all appeared in this century or in the second half of the nineteenth century. In some cases these devices are produced by industries which did not have any precursor and supply functions which are completely new. It is the case, for example, of the electricity and electronics industries, neither of which arose from the evolution of any other industries but which were qualitatively completely different from any industry which preceded them. In some cases their products perform functions which did not exist at all before, such as the transmission of characters, images and sounds (e.g. fax, television, radio, telephone). A crucial point here would be whether and to what extent these products replaced something pre-existing, as the train and the motor car replaced the stage coach, or whether they were completely new and, therefore, represented a net addition to the variety of the system. In the previously mentioned examples it seems clear that the products were radically new, but in general it is not clear that we would be able to distinguish unequivocally a completely new and different product from one which is an incremental improvement of another one. In any case we do not have the data to compute the variety of the system on this basis. Two types of considerations’ can help us in this respect. On the one hand, we can try to assess the changes in the range of products, processes, materials, etc. used in a particular institution—for example a household. On the other hand, we can try to analyse in a general way the potential outcome of the elementary processes previously described. Let us start with the household. Clearly the average household uses many more pieces of

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equipment now than was the case one hundred years ago. Electric and gas cookers, fridges, freezers, washing machines, blenders, toasters, television, video recorders, hi-fi equipment are standard items in most households in industrialized countries. This is not purely a diffusion effect. In other words, it is not the case that these products have always been available and they have only gradually diffused amongst the different social and income groups. Although diffusion effects are obviously very important, if we compare today’s situation to that of one hundred years ago, it is clear that these products did not exist at all. In the meantime it does not seem that the pre-existing products have disappeared. Tables, chairs, beds, etc. are still in use, although in a sometimes quite different form. Consequently, it seems, a relatively safe conclusion that the net variety of the products, processes, materials, etc. used in an average household in the last one hundred years has considerably increased. The second type of consideration is based on elementary processes in technological development. Let us analyse them by means of an example: that of transport technology. Until the end of the eighteenth century there were essentially two forms of transport technology: land based and water based. Each of these had a certain internal differentiation. For example, there were different types of carts and coaches which could be used to carry people and goods, while individuals could also ride on the back of horses, camels, elephants, etc. Likewise there were different types of boats and ships that could transport people and goods over rivers and seas. During the last two hundred years a number of completely new transport technologies have been added to the improved descendants of the old transport technologies. Trains, cars, bicycles, motorcycles, buses, lorries and, later, helicopters have been added to the previous forms of transport. All these new forms coexist and none of the previous ones have been completely displaced, although some of them, e.g. horse riding, have become very marginal. What has been happening in transport technology in the course of history and with increasing speed in the last two hundred years can be described as a process of differentiation and specialization coupled with the emergence of brand new products. During this process the range of services which can be supplied by transport technologies to users has become much wider and the overall market for transport services has grown enormously. Each transport technology addresses the demand of a particular niche in the economic environment. We can then say that the variety of transport technology, defined as the number of its distinguishable products, has constantly increased in the course of economic development. Is transport technology an isolated example or are there others? Although at this stage it cannot be proved that this is a general tendency, there are a number of other examples, such as telecommunications, machine tools and even financial services, in which a similar pattern of differentiation and specialization, accompanied by the emergence of completely new products, applies. In summary, we cannot conclude that this pattern of evolution is common to all technologies, but we can say that a number of technologies are having a very considerable impact on economic and social life and are following this pattern.

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Even if no more than such a limited statement could be made this would justify the importance of the concept of variety. Another point which has to be noted in this context is that other trends could have been in operation at different times. The one outlined above is particularly relevant for the last two hundred years, but different ones could have existed in other historical periods. In addition to the previous reasons we can expect variety to increase in the course of economic development based on other types of reasoning, although none of these constitutes a definitive proof. These reasons will be briefly described in what follows. For a more detailed discussion of them the reader is referred to Saviotti (1990a). In Pasinetti’s (1981) model of economic growth an imbalance can be created in the economy if the rate of growth of demand falls below the rate of productivity growth. If this happens simultaneously for many sectors of the economy and for long periods of time it can limit the utilization of productive resources and, therefore, also lead to unemployment. The long-term continuation of the process of economic growth can be ensured by means of a number of mechanisms, of which the emergence of new products and activities is one of the most important. In other words, there is a very strict relationship between economic growth and structural change. Similarly we can say that an increase in the variety of the system is one of the most important mechanisms by which long-term economic growth is maintained. Studies of non-equilibrium thermodynamics (Prigogine, 1976; Prigogine and Stengers, 1984) and of synergetics (Haken, [1977] 1983) have shown that open systems can undergo transitions to stationary states which become more and more complex and ordered. As the flow of matter into and out of the system increases, the equations describing its behaviour can undergo bifurcations and give rise to a greater number of stationary states. A greater number of more complex stationary states are likely to be associated with a higher variety of the whole system. These considerations, together with the empirical evidence that variety has increased for some subsets of the economic system (e.g. transport, telecommunications), do not constitute a definitive proof that variety increases with economic development, although they make it quite a plausible hypothesis. In what follows attention will be focused more on the implications of this hypothesis than on the demonstration of its validity. 4.2 Informational implications of variety If variety increases in the course of economic development more distinguishable products will be available. This is likely to be an advantage for consumers and users, but the situation could be perceived differently by producers. We can expect that producers, faced with the opportunity to produce a wider range of

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goods (or of services), will take advantage of it but will try to reduce costs. One of the most important types of costs is information costs. The relevance of such a cost can be understood with reference to a firm which, when increasing the number of its products, has correspondingly to increase the quantity of information stored and used. In other words, an increasing variety is likely to lead to increasing information requirements. We can then expect that the firm will tend to reduce all costs, including information costs, while increasing the number of its products. There are two general ways to achieve this. First, firms can redesign their organizational structures and procesess in such a way that they need less information to produce the same variety of products. Second, they can increase the quantity of information proportionally to the variety of products but use more efficient technologies to process information. The latter solution is the one adopted by using information technology, which allows the processing of large quantities of information at low cost. In reality the two approaches are used together, but they can be separated conceptually. The rest of this chapter will be exclusively concerned with the first type of approach. It is useful at this point to structure the concept of variety. By calling the number of distinguishable products ‘output variety’ (Va) and the number of operations, pieces of equipment, etc., used to produce them ‘process variety’ (Vp), we can say that firms will tend to increase output variety while limiting process information requirements. Naturally, one of the ways to do this is to limit the variety of the process—but there may be other ways, depending on the relationship between variety and information. To discuss this problem let us introduce the concept of entropy. In a very general sense entropy can be considered a measure of the disorder or randomness of a given system. If we consider a set of objects of different colours (e.g. balls on a billiard table or cars of different colours in a car park) we associate a greater randomness with a configuration in which all the objects of the same colour are mixed at random than with one in which the objects of the same colour are all together, for example in a corner. We can then expect that a configuration on a billiard table (using, say, an equal number of red balls and white balls) with all the red balls in a corner and the white balls in another corner will have a lower entropy than one with the balls of the two colours distributed uniformly over the table. The relationship between entropy and disorder/randomness can be understood if we imagine to perform the following experiment. Let us take all the balls of the two colours and throw them on the table, then observe their position and repeat the whole experiment all over a large number of times. We expect that most of the times the outcome of our experiment will be a configuration in which the red and white balls are mixed at random. It is not impossible that in one of our experiments we obtain an ordered configuration with all the red balls in a corner and the white balls in another one, but we do not expect this outcome to occur very often. In other words, a random configuration of the system is more probable than an ordered one. We have then established two

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relationships—one between entropy and disorder and the other between disorder and probability: entropy proportional to disorder; probability proportional to disorder. From these two relationships we can infer that entropy is proportional to probability. If we indicate entropy with S and the probability of finding the system in a given state with W, we can understand the well-known formula introduced by Boltzmann: (1) where K is a constant. The previous considerations apply when the microstates of the system are independent and equiprobable. When this is the case formula (1) can be rewritten as: (2) where pi is the probability of an individual microstate. When the previous assumptions of independence and equiprobability are not satisfied, formula (2) is no longer valid. To understand what happens in this case let us perform a modified version of our experiment by covering all the red balls with glue. We can expect that they will stop nearer the position at which they land on the billiard table than the white balls, and that they will tend to stick to one another more than they stick to white balls. Very often the outcome of our experiment will be given by configurations in which the white and red balls are not distributed uniformly over the billiard table. In other words, individual configurations are not equiprobable any more. In these conditions formulas (1) and (2) do not apply any more and they have to be replaced by: (3) where pi is the probability of an individual configuration of the system. Formula (3) has been introduced by Shannon and Weaver (1949) to deal with the information content of messages. In view of this application it is understandable that formula (3) has to take into account the effect of non-equiprobable configurations. If we imagine that messages are constituted by words, that are constituted by letters, we can realize that different configurations of letters are not equiprobable. According to Shannon, S is a measure of the information required to describe the system. If we have a system constituted by N elements we need more information to describe the position of all the elements when they are in random configurations than when they are ordered. Any degree of order establishes relationships between the elements of the system which allow us to ‘deduce’ the position of at least part of the elements of the system when we know the position of others. Therefore, we can consider the entropy S as a measure of the information content of the system. Now we have to relate information to variety. To do that let us return to the definition of variety as the logarithm of the number of distinguishable elements

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of the system. If the elements are distinguishable but also independent and a priori equiprobable, the probability pi of encountering each one of them is: (4) or (5) Consequently the variety of the system can be expressed as: (6) This is clearly equivalent, except for the constant K, to formula (2) for entropy. Under these conditions, therefore, variety differs from entropy only for a constant term. The entropy of the system falls below that of formula (2) by introducing interdependence of its elements and non-equiprobability of its microstates (Gatlin, 1972). In other words, the entropy of the system has its maximum value when its elements are independent and its microstates are equiprobable, and it falls below the maximum when constraint and non-equiprobability are introduced. On the other hand, variety does not change unless the number of distinguishable elements of the system changes. Therefore, entropy can have a maximum value equal to variety (except for a constant term) and falls below variety whenever constraint and non-equiprobability are introduced. Since entropy measures the information content of a given system, variety is equal to its maximum information content. The information content of a system of a given variety can be reduced below its maximum by introducing constraint and non equiprobability of its microstates. If we are considering a social system this can be obtained by means of different types of organizational arrangements. We can now proceed to examine how producers can increase their output variety without correspondingly increasing the quantity of information required and therefore the information costs. 5 INFORMATION AND ORGANIZATIONAL CHANGES One of the ways of reducing the information requirements of a process consists of changing the organization of the process itself. This amounts to changing the patterns of information storage and flow, as it will become clear by analysing some examples.

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5.1 Division of labour We can describe a process as the combination of a number of workers and a number of operations. Each operation consists of the transformation and combination of materials by applying energy to them. In general the number of workers and that of operations in a process will not coincide, but, for simplicity, we can start by considering a process in which the number of workers and of operations are both equal to n. The process can be organized with different degrees of division of labour. On the one hand, there is the case in which each worker performs all the operations in a sequence and ends up with the finished product. On the other hand, there is the case in which each worker can only perform repeatedly one operation and then pass the outcome to the following worker in an assembly line situation. All other possible degrees of divison of labour are included between these two extremes. We can expect that in the organizational form without division of labour, each worker will have to store information about all the operations which constitute the process. Furthermore, in that same organizational arrangement, management will need a greater quantity of information to ‘know’ the state of the process at each given time. This depends on the uncertainty of the process, which reaches a maximum in this case. In general the greater the uncertainty of a system the greater is the quantity of information required to specify the ‘location’ and time changes of all its components. If we move from the state without division of labour to that with the maximum possible division of labour, in which each worker can only perform one operation, then both the information that has to be stored by each worker and that required by management to ‘know’ the process decrease. The latter result is due to the decrease in uncertainty of the process. The state with the maximum possible division of labour is characterized by the minimum uncertainty, because the location of each worker is known at all times. Consequently, the information requirements of a given process will fall if we gradually increase the degree of division of labour of the process itself. This is, therefore, an organizational change which can be used to decrease the information costs required to produce a unit of output variety. The description of the process has so far been purely qualitative. It is possible to calculate the relative entropies of the two extreme cases and, therefore, their information requirements. The detailed calculations are not described here. Also, for a more detailed analysis the reader should refer to other articles (Saviotti, 1988, 1990a). In this section only the general principles of the calculations will be described intuitively. We can now return to the previous two states of the system, the one without any division of labour and the one with the maximum division of labour, and calculate their entropies. These two states can have many configurations in each

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of which the workers are coupled in a given way with the operations of the productive process. Each of these different configurations can be called a microstate of the system. The whole productive process can be represented as a sequence of configurations or microstates. If we imagine to take a time average of the different configurations of the system, we will obtain a so-called macrostate. Given what was previously said, there are many more random or disordered micro-states, or, equivalently, the probability of finding the system in a disordered microstate is higher than that of finding it in an ordered microstate. We can, therefore, expect that a macrostate will resemble a disordered microstate. We can imagine to generate the different microstates of the system by extracting the workers and the operations from two different urns and by coupling them. A microstate will be generated after we have extracted all the workers and all the operations from the two urns and we have coupled them. Other microstates can be obtained by returning the workers and the operations to the urns and by repeating the sequence of draws and of couplings. The essential difference between the two states, which is responsible for their different entropy values and, therefore, for their different information contents, is that the microstates are equiprobable only in the first case, when the workers can perform all the operations. In this case, since any worker can be coupled in the same way with any operation, all the microstates of the system are equiprobable. In the second case, in which each worker is highly specialized, all the microstates of the system but one have probability zero. The only state with a non-zero probability is the one in which each worker is coupled with the operations in which he/she is specialized. The entropy of the two states of the system can now be calculated, with the following results: entropy without division of labour=log2n; entropy with maximum division of labour=0. We then see that not only the entropy has decreased, as we expected, with increasing division of labour, but that it has actually fallen to zero. Naturally this does not mean that zero information is required to know the system, but that entropy has fallen to its minimum value. On the other hand log n is the maximum value that the entropy can have for the given system. Thus the state with the maximum entropy of the system corresponds to the minimum degree of division of labour and the state with the minimum entropy to the maximum division of labour. In general the entropy and, for what we said before, the information content of an organizational system decrease with increasing degree of division of labour. We can also note that the maximum value of entropy, log2 n, is equal to the variety of the system. This happens when the elements of the system are independent, or there is no constraint amongst them. When there is some

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constraint amongst them, a fixed relationship between their behaviour is introduced and the number of degrees of freedom of the system decrease. A lower number of degrees of freedom implies that the system is characterized by a lower uncertainty and, therefore, that less information is required to describe it. When constraints are introduced amongst the elements of the system the entropy starts to decrease and to differ from variety. The transformation between the state of the system in which there is no division of labour and each worker performs all the operations in a sequence, and the state in which each worker is so specialized that he/ she can perform only one operation, is an example of organizational change. We have seen that this type of organizational change leads to a change in the information content of the productive process and that an increasing degree of division of labour decreases the information requirements of the process. Therefore, division of labour, in addition to its other properties, is an information-saving device. 5.2 Hierarchical organizations Another type of organizational change is the transition from an organization without internal boundaries to one with departmental boundaries; for example, a multifunctional organization. The change in entropy and, therefore, in information content, of this change can be studied in a way similar to that of the previous case. Again, we can generate the microstates corresponding to the state without internal boundaries and to the state with departments, and calculate the entropies for both (for the calculations see Saviotti, 1988, 1990a). The result of these calculations is that the entropy of the state with departments is always lower than that of the state without internal boundaries. Another case which can be considered a subset of this is the transition from the multifunctional to the multidivisonal firm. Again, it is possible to generate the corresponding microstates of the two organizational structures and then to calculate their entropy (see Saviotti, 1990a). The result is that the entropy of the multidivisional firm is lower than that of the multi-functional firm provided that the total number of employees does not change and that the number of employees in each sub-unit of the multi-divisional firm is lower than that in the multifunctional firm. This is always going to be true if the total number of employees does not change. 5.3 Variety, information and processes of technological development Recent studies on innovation have uncovered some patterns of development that are followed by several technologies. These patterns are characterized by the presence of discontinuities, constituted by sharp qualitative changes, followed by

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periods during which only gradual, quantitative modifications are introduced into the technology. Concepts such as dominant designs (Abernathy and Utterback, 1975), technological paradigms (Dosi, 1982), technological regimes and natural trajectories (Nelson and Winter, 1977), technological guideposts (Sahal, 1981a, 1981b) all describe patterns of technological development constituted by a sequence of periods of discontinuities followed by periods of gradual change. That such patterns emerge is partly explained by the increasing returns to adoption (Arthur, 1983, 1988, 1989). In this case a technology increases its advantage with respect to other competing technologies every time it is adopted. These patterns of technological development have important implications for variety. If during the course of the evolution of a technology the initial multiplicity of designs is replaced by one dominant design, clearly the output variety of the technology falls. This either contradicts the initial statement of this chapter, that variety is generally likely to increase in processes of economic and technological development, or it needs to be explained. When a dominant design/paradigm/regime is achieved within a given technology, this leads to common practices followed by all the firms using the technology itself. The convergence on common practices, procedures and techniques narrows the range of information required to know the processes constituting the technology. Furthermore, scale economies in the production of common parts become possible. Therefore, the convergence on dominant designs which takes place during the evolution of a technology decreases both the information and material costs of a process, leading to a greater efficiency. The problem then arises whether these variety-decreasing and efficiencyenhancing trends are compatible with an overall increase in the variety of the system. The answer to this question can be formulated by hypothesizing that both variety-enhancing and efficiency-increasing processes are important components of economic development. Niether of these two types of processes by itself would be enough to explain economic development as we know it. If all the processes were efficiency-increasing the system would not change its variety. As we have already seen in Pasinetti’s model (1981) the possibility that demand and productivity can grow at different rates could lead to an underutilization of resources. On the other hand the new products/ processes etc. which create the increased variety need resources to be developed. These resources can only be generated by increasing efficiency in existing processes. The efficiencyincreasing and variety-enhancing trends are, therefore, complementary rather than competing processes. Economic development needs both of them to continue. It is, therefore, possible that a variety-increasing trend at the highest level of aggregation is compatible and indeed requires efficiency-increasing/varietyreducing processes at a lower level of aggregation. Naturally this question cannot be considered settled here. Better conceptual and empirical analyses are required. However, the considerations in this chapter are enough to establish the importance of these trends as fundamental components of economic development.

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5.4 Routines Nelson and Winter (1982) carried out a sophisticated analysis of routines and of their importance for firms and other organizations. They have defined routines as regular and predictable patterns of the behaviour of firms (ibid., p. 14). A slightly more specific definition was given by the author of this chapter (Saviotti, 1990a) as a constant pattern of internal activities of firms and organizations in response to environmental stimuli occurring within a predetermined range. In other words, a standard pattern of behaviour is associated with any external stimulus falling within a given range. Naturally, routines have to be sufficiently elastic to be able to cope with a reasonably wide range of external stimuli, but they are never going to cope with an infinite range of stimuli. When environmental stimuli fall outside their predetermined range, routines break down and have to be replaced by new ones. Nelson and Winter (1982) consider a number of possible functions of routines; for example, to be the memory of organizations and to function as a truce amongst different groups. A different function can be hypothesized in this context. Routines can be considered similar to heuristics, which can be defined as a device that contributes to the reduction in the average search to solution. In a similar way routines, by sticking to a predetermined behaviour in response to a set of environmental stimuli, reduce the search required to ‘process’ the stimulus. One of the additional functions of routines is that of information-saving devices. A final consideration about routines is that they, like the patterns of technological development already discussed, can be considered a sequence of discontinuities followed by periods of gradual quantitative development. In this respect routines, paradigms, trajectories, regimes, dominant designs can all be considered examples of the self-regulation of complex systems, which try to achieve some degree of invariance with respect to environmental stimuli (Saviotti, 1986). 6 VARIETY AND ELEMENTARY MECHANISMS IN TECHNOLOGICAL EVOLUTION Processes of technological evolution can be of an extremely wide diversity. It would be quite useful to represent this diversity in terms of a few elementary mechanisms. Some examples of these have been described before in terms of the twin characteristics approach (see 3.1). For the purposes of the present discussion it is important to point out that elementary mechanisms have different implications for variety. For example, pure

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Table 10.1 Examples of different elementary mechanisms and their contribution to output variety Vq. P2=new product, P1=existing product Process

Example

P2 completely substitutes P1

P2=digital watch supplying time and date P1=mechanical watch supplying time and date P2 cheaper P2=digital watch supplying time, date, stopwatch

P2 almost identical to P1 except few new technical and service characteristics P1=digital watch supplying time and date P2 new internal structure, some services similar to P1, some new services

P2 new internal structure, new services

0

>0

small P2=digital watch supplying time, date, stopwatch

>0

P1=mechanical watch supplying time, date Personal computers, 1976–1979

larger >0 maximum

substitution, by replacing an old with a new technology, does not alter the number of distinguishable elements of the system and, therefore, it does not change variety. On the other hand, the emergence of a completely new product represents a net addition to the system and makes the highest possible contribution to variety. Partial substitution and specialization are intermediate cases. They increase variety, but not as much as the emergence of a completely new product (Table 10.1). Any combination of elementary mechanisms which does not contain only pure substitution will lead to a net increase in the variety of the system. 7 COMPETITION The generation of new products, processes, materials and organizational forms is one of the most important factors leading to economic develpment. However, not all the products which are proposed succeed. Successful ones have to pass a process of selection, which in economic terms generally means to survive competition with products of other firms. There are different types of competition. The most common one is perfect competition. In this type firms are price-takers, there is free entry which establishes a position of normal profitability, and all firms produce a homogeneous and equal product (McNulty, 1968). A very different type of

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competition was that described by Schumpeter, in which entrepreneurs compete by being first at introducing new products. This type of competition has recently been called innovation competition by Metcalfe and Gibbons (1989). This type of competition is driven by differences between firms, which has as its outcome continuous changes in the relative economic performance of firms (Metcalfe and Gibbons, 1989). For the purpose of the present discussion the most important differentiating feature amongst different types of competition is product homogeneity or, conversely, product diversity. We can imagine that real life competition will be somewhere in-between perfect and innovation competition. The closer it is to the extreme of perfect competition, the more homogeneous and similar the products of different firms will be; the closer it is to innovation competition, the more diverse and dissimilar the products of different firms will be. As it was explained before, product models can be represented by points in characteristics space. In the same space technological populations will be represented by clouds of points. A change in the mechanism of competition during the evolution of a technology, for example, in the direction of perfect competition, will be represented by a growing similarity of the cloud/population and, therefore, by a growing density of the same cloud (see Figure 10.9 for an example) (Saviotti, 1990b). Using the concepts presented in this chapter it is possible to map changes in the nature of competition. It is also possible to hypothesize that changes in competition could take place systematically during the evolution of technologies. Thus, if a multiplicity of product design is present at the beginning of its evolution, a technology could be characterized by innovation competition in its early phases. As the technology matures and then converges on a dominant design some competitors are driven out while the surviving ones would converge on a dominant design and on common practices. Competition could then move in the direction of perfect competition. Such hypothesis has not yet been tested in general, but some examples of this pattern have been found in the development of motor cars and of aircraft technologies (Saviotti, 1985, 1990b). 8 CONCLUSION This chapter has been mainly based on two themes: the need to open the black box of technology and the role of variety in economic and technological development. The two themes are related, in that the first one starts by introducing a representation of individual technologies, or more appropriately, of technological models, which can then be used to describe elementary mechanisms of technological evolution and aggregated into populations of models. Variety is an even more aggregate concept, which can be used to describe the final outcome of innovation and of technological change at the level of aggregation of the whole economic system. These two themes can be seen in the context of the

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Figure 10.9 Density change in a technological population between times t1 and t2 in a bidimensional X space

construction of evolutionary theories, which give a central place to the analysis of qualitative change and of its role in the dynamic processes of economic development. In this context it is argued that technological change, one of the main sources of qualitative change, needs to be analysed more thoroughly than economists have traditionally done. It is not only technological change which needs to be considered endogenous to the economic system; attention must also be paid to the internal structure of the technologies whose economic aspects are studied. This is done by means of a twin characteristics representation of a technology, one set of characteristics describing the internal structure of the technology and the other the services performed for its users. By means of this representation one can describe elementary mechanisms of technological evolution and map individual technological models in characteristics space. Furthermore, the sets of similar models by different producers constitute technological populations. These populations are mapped in characteristics space by the clouds of points which represent individual models. Schumpeter’s theory of economic development is the starting point for the second theme. The role of innovations, defined as new products, processes, materials, markets and organizational forms is considered essential in the longterm process of economic development. The concept of variety is proposed as a basis on which an analytical treatment of Schumpeter’s theories can be developed. Variety is here defined as the number of distinguishable elements in a system. It is argued that in general variety is likely to increase during economic development, although not in every process and at all levels of aggregation.

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An increase in variety will have as a consequence an increase in information requirements and, therefore, in information costs. We can then expect that producers, while trying to produce a greater output variety will try to reduce all their costs, and information costs in particular. This can be done either by means of organizational changes or by using more efficient technologies to process the same quantity of information. The relationship between organizational changes, variety and information is analysed by means of the concept of entropy. It is demonstrated that organizational changes, like division of labour and the introduction of hierarchical structures in organizations, reduce information costs. In a similar way it is argued that routines have the function of information savers. Concepts such as dominant designs, technological paradigms, natural trajectories, etc. point towards a reduction of the variety of the system at given levels of aggregation. This reduction in variety contributes to increase the efficiency of technologies during their life cycle. Such variety-reducing, efficiency-increasing processes are complementary with respect to varietyenhancing processes. Efficiency-increasing processes free resources that can then be used to generate the new products, processes, etc. which lead to an increased variety. A number of descriptive concepts have been introduced in the discussion of the two themes. Both the twin characteristics approach and the concept of variety are useful descriptors of processes of technological change at different levels of aggregation. These concepts relate to the observables of a theory of technological change, such as new products or the number of distinguishable elements of the economic system. Descriptive concepts define the basic observables of a theory, but the theory itself must be something more than a description. Thus, for example, the elementary mechanisms of technological evolution are important building blocks which can be applied to the analysis of the development of many technologies. Also, a number of generalizations have been proposed, such as the transition from fundamental to adaptive variables during the evolution of technologies, the changes in variety and in information requirements during the course of economic development, the changes in information requirements due to organizational change, and the change in the nature of competition during the evolution of technologies. These generalizations are in principle testable and form a basis for the development of a theory of technological evolution which is compatible with economic theories. BIBLIOGRAPHY Abernathy, W.J. and Utterback, J.M. (1975), ‘A Dynamic Model of Process and Product Innovation’, Omega, Vol. 3, No. 6, pp. 639–656. Abernathy, W.J. and Utterback, J.M. (1978), ‘Patterns of Industrial Innovation’, Technology Review, June/July, pp. 41–47.

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Arthur, W.B. (1983), ‘Competing Technologies and Lock-in by Historical Events: The Dynamics of Allocation under Increasing Returns’, International Institute for Applied Systems Analysis, Paper WP-83–90, Laxenburg, Austria. Arthur, W.B. (1988), ‘Competing Technologies: An Overview’, in G.Dosi et al. (eds), Technical Change and Economic Theory, London, Pinter. Arthur, W.B. (1989), ‘Competing Technologies, Increasing Returns, and Lock-in by Historical Events’, Economic Journal, Vol. 99, pp. 116–131. Ashby, W.R. (1956), An Introduction to Cybernetics, London, Methuen. Binswanger, H.P. (1974), ‘The Measurement of Technical Change Biases With Many Factors of Production’, American Economic Review, Vol. 64, pp. 964– 976. Binswanger, H.P. et. al. (1978), Induced Innovation: Technology, Institutions and Development, Baltimore, Md., Johns Hopkins University Press. Coombs, R., Saviotti, P. and Walsh, V. (1987), Economics and Technological Change, London, Macmillan. Dosi, G. (1982), ‘Technological Paradigms and Technological Trajectories: A Suggested Interpretation of the Determinants and Directions of Technical Change’, Research Policy, Vol. 11, pp. 147–162 Freeman, C., Clark, J. and Soete, L. (1982), Unemployment and Technical Innovation: A Study of Long Waves and Economic Development, London, Pinter. Gatlin, L.L. (1972), Information Theory and the Living System, New York, Columbia University Press. Haken, H. ([1977] 1983), Synergetics: An Introduction, Heidelberg, Springer-Verlag. Hanusch, H. (1988), ‘Introduction’, in H.Hanusch (ed.) Evolutionary Economics, Cambridge, Cambridge University Press. Hounshell, D.A. and Smith, A.K. (1988), Science and Corporate Strategy, Cambridge, Cambridge University Press. Lancaster, K. (1966), ‘A New Approach to Consumer Theory’, Journal of Political Economy, Vol. 14, pp. 133–156. Lancaster, K. (1971), Consumer Demand: A New Approach, New York, Columbia University Press. McNulty, P.J. (1968), ‘Economic Theory and the Meaning of Competition’, Quarterly Journal of Economics, Vol. 82, pp. 649–656. Mathias, P. (1983), ‘The Machine: Icon of Economic Growth’, in S.Macdonald, D.McL. Lamberton and T.D.Mandeville, The Trouble with Technology, London, Pinter. Maynard Smith, J. (1974) Models in Ecology, Cambridge, Cambridge University Press. Metcalfe, J.S. and Gibbons, M. (1987), ‘Technology, Variety and Organisation’, Mimeo, University of Manchester. Metcalfe, J.S. and Gibbons M. (1989), ‘Technology, Variety and Organisation: A Systematic Perspective on the Competitive Process’, Research on Technological Innovation, Management and Policy, Vol. 4, pp. 153–193. Nelson, R. and Winter, S. (1977), ‘In Search of Useful Theory of Innovation’, Research Policy, Vol. 6, pp. 36–76. Nelson, R. and Winter, S. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., Harvard University Press. Newell, A., Shaw, J.C. and Simon, H.A. (1962), ‘The Process of Creative Thinking’, in H.E. Gruber, G.Terrell and M.Wertheimer (eds), Contemporary Approaches to Creative Thinking, New York, Atherton Press. Pasinetti, L.L. (1981), Structural Change and Economic Growth, London, Macmillan.

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Pavitt, K., Robson, M. and Townsend, J. (1989), ‘Technological Accumulation, Diversification and Organisation in UK Companies, 1945–1983’, Management Science , Vol. 35, pp. 81–99. Prigogine, I. (1976), ‘Order Through Fluctuations in Self-Organisation and Social Systems’, in: E.Jantsch and C.H.Waddington, Evolution and Consciousness: Human Systems in Transition, New York, Addison-Wesley. Prigogine, I. and Stengers, I. (1984), Order out of Chaos, London, Fontana. Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, Cambridge, Cambridge University Press. Sahal, D. (1981a), ‘Alternative Conceptions of Technology’, Research Policy, Vol. 10, pp. 2–24. Sahal, D. (1981b), Patterns of Technological Innovation, Reading, Mass., Addison Wesley. Saviotti, P.P. (1985), ‘An Approach to the Measurement of Technology Based on the Hedonic Price Method and on Related Methods’, Technological Forecasting and Social Change, Vol. 27, pp. 309–334. Saviotti, P.P. (1986), ‘Systems Theory and Technological Change’, Futures, Vol. 18, No. 6, pp. 773–786. Saviotti, P.P. (1988), ‘Information, Variety and Entropy in Technoeconomic Development’, Research Policy, Vol. 17, pp. 89–103. Saviotti, P.P. (1990a), ‘The Role of Variety in Economic and Tehnological Development’, in P.P.Saviotti and J.S.Metcalfe (eds), Evolutionary Theories of Economic and Technological Change: Present State and Future Prospects, London, Harwood Publishers. Saviotti, P.P. (1990b), ‘A Characteristics Approach to Technological Evolution and Competition’, Mimeo, University of Manchester. Saviotti, P.P. and Metcalfe, J.S. (1984), ‘A Theoretical Approach to the Construction of Technological Output Indicators’, Research Policy, Vol. 13, pp. 141–151. Saviotti, P.P. and Bowman, A. (1984), ‘Indicators of Output of Technology’, in M. Gibbons, P.Gummett and B.M.Udgaonkar (eds), Science and Technology Policy in the 1980s and Beyond, London, Longman. Schumpeter, J. ([1912] 1934), The Theory of Economic Development, Cambridge, Mass., Harvard University Press. Schumpeter, J. ([1943] 1976), Capitalism, Socialism and Democracy, New York, George Allen and Unwin. Shannon, C.E. and Weaver, W. (1949), The Mathematical Theory of Communication, Urbana, University of Illinois Press. Simon, H.A. (1981), The Sciences of the Artificial, Cambridge, Mass., MIT Press.

11 Human resources constraints in technological innovation processes Theoretical considerations and empirical evidence Riccardo Leoni1

1 INTRODUCTION In the literature which deals with the phenomenon of technical innovations, the relationship between technical progress and human resources has been amply investigated in terms of the impact of the former on the latter. This chapter will set forth some empirical results which demonstrate the existence of a relationship in the opposite direction, thus strengthening the idea of a bi-univocal relationship between technology and the market of human resources. The data, some of which have already appeared in Leoni (1989b), are the result of a recent survey on a sample of over two hundred firms in a highly industrialized area.2 Before looking at these results, a review of some of the approaches which appear in theoretical literature is required; in this way, it will be possible to verify coherences and discrepancies between dominant interpretations and the data at our disposal. In our interpretation, both the origin of job vacancies and the role of constraint of human resources at the initial stages of the creation of new production options can be explained by a modified version of Amendola and Gaffard’s model (1988). 2 APPROACHES TO CONSTRAINTS IN INNOVATIVE ACTIVITIES: A FEW THEORETICAL CONSIDERATIONS When considering innovation processes (both of product and process), the financial aspect is the most frequently examined. The usual source of reference is Spence (1979), who emphasized the financial constraints encountered by the supply of new investment goods. On this subject, the recent contribution of Fazzari et al. (1988) is quite interesting. This work demonstrates how the existence of asymmetric information between firm managers, on the one hand, and creditors (banks and even new potential shareholders) on the other, generates

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the so-called ‘lemons premium’ which is responsible for costs of investment being higher than the opportunity costs of internal funds. If this is true, the liquidity constraint will be stricter for an innovative firm than for a mature and wellestablished one. Mainstream literature, as well as the most recent contributions, devotes little attention to the constraints arising from qualified human resources. Our framework of reference conforms to some novel interpretations of innovation processes, which focus both on the firm as specific cognitive capital and on learning as a dynamic factor par excellence. Other factors are also taken into account; namely, the organization of production, the variety of firms, the role of institutions in structuring the innovating activity at the industry level.3 According to this approach, the cognitive capital of the firm would be supplied by a series of cognitive sources, both within and outside the firm. These sources would include R&D expenditures, design, learning by doing and learning by using and, finally, forms of co-operation with other firms and research organizations. It should be noted that the idea that firms can also acquire technical innovations by resorting to the market presupposes that specific innovations are ‘marketable’ and that the market itself is a source of subjects (firms and human resources) capable of meeting the demand of the purchasing firm. According to this approach technology can exhaustively explain the qualitative adjustments of the workforce employed. Such adjustments take place in the learning-by-doing and/or learning-by-using versions and other institutionalized and non-institutionalized forms. If this were really the case, then the vast changes which are generally involved, as much in the structural organization and capital stock as in the composition of the skills, would not generate mismatches between demand and supply of labour. Even if mismatches were to result, the categories of learning and retraining would be capable of confining the length of the unemployment spell of those whose professional qualifications have become temporarily obsolete within acceptable social limits. However, empirical evidence suggests that occupational mismatches are one of the main factors of long spells of unemployment. The view of production and of the technological modifications implicit in such a form of adjustment can also be questioned on a more theoretical level. According to Amendola and Gaffard the analytical ingredients of this adjustment are the following: 1 a technological advance that is assumed to be fully defined and developed at its first appearance, and which generally consists in a new production process embodied in durable capital equipment (a new ‘technique’); 2 a given productive structure into which the innovation must be absorbed and; 3 a given point of arrival of the process, represented by a productive capacity fully shaped according to the new technique (Amendola and Gaffard, 1988, pp. 1–2).

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The techniques are fully specified in terms of technical coefficients and can be considered as given; i.e. they can be freely appropriated provided that inputs are available according to the quantity and the quality required. As far as labour input is concerned the category of learning would fulfil the market requirement. We can see how such ingredients identify not so much a process of generation of innovation as a process of diffusion of innovation, whose speed would be a function of learning. Following Amendola (1988), in an ex ante view of the technology connected with the productive fact, innovation is no longer a rational choice between known alternatives, nor the consequent absorption of a given technological result into a prevailing productive structure; rather, it is a process aiming at the creation of new productive options. Because of the creative and explorative behaviours which characterize and differentiate them from other inputs, human resources play the key role in such a process. This behaviour is expressed, according to Nelson and Winter (1982), in the research for new routines; i.e. for new ways of solving problems of production. The features of such a presentation of the process are analogous to those described in the path-dependency models (cf. David, 1988; Cantalupi, 1988). Decisions are sequential and casual and are not always instantly reversible (and/ or without costs); as initial conditions play an important role and cannot be ignored, the result of the process cannot be foreseen, that is to say ‘contemporaneously at the application of the input’ (Amendola, 1988, p. 54). Hence a dynamic equation describing a process of production of new routines should include a stochastic component which, at the beginning of the process, is quantitatively more important than the deterministic component. Consequently, in endogenous processes of technological creation, the hypothesis of constant learning coefficients is no longer pertinent, even if innovation is characterized by an incremental modification. The appearance, on the production scene, of new technological and scientific concepts and principles, fruit of the creative and explorative behaviour mentioned above, is at the origin of new technological paradigms or regimes (discussed, respectively, by Dosi, 1983, and Nelson and Winter, 1977). In these contexts the hypothesis of constancy of the coefficients of learning becomes completely incongruous and the category of learning itself (in both the doing and using versions) is greatly reduced. The situation may prove to be less serious in the case of process innovation, which entails an absorption of new technology produced elsewhere and very probably requiring, in usage (as compared with the professional skills acquired in preceding processes), less accentuated upgrading in problem-solving activities than is required both in the generation and generalization processes of innovation. Once the new scientific and technical principles have been established, they tend, initially, to be internalized by the productive units which have generated them (for example through patents). Then, to the extent that the basic principles can increase the ‘technological content’ and the performance of a large number of

216 HUMAN RESOURCES CONSTRAINTS IN INNOVATION

products and processes, they are gradually transferred into the curricula. This creates a new labour demand4 for those who, possessing these curricula, are suitable for exploring ways of transferring new technical and scientific principles into production. The time gap which occurs between the affirmation of the new technical and scientific principles and their incorporation in the curricula depends, in a first approximation, on the distance between the existing and the new technical and scientific principles; in addition social and cultural institutions play an important role. Perez (1983) underlines how institutions and attitudes settled on preceding forms of technological and social development may impede both the generation and diffusion of new technology. This sort of inertia of the socio-cultural structures and institutions is inversely related to the degree of education of the population. The gap in question gives rise to mismatch-induced job vacancies. The unsuitable quality of the existing workforce imposes a constraint on those firms interested in the further generation of technological innovation (i.e. to make an innovative choice, according to Amendola and Gaffard’s model). The latter is, at times, just the generalization of the ‘cluster’ of new technical and scientific principles to the whole production range. This is what Freeman et al. (1982) have interpreted as complementarities between different technological developments and interdependencies between industries. Following the principle of low ex post substitutability of production techniques (of Johansenian origin), we believe that new technological knowledge is more easily assimilated by new cohorts than by those who are already established in production. For the latter it seems reasonable to assume a degree of trainability inversely correlated to the age of the cohort. In conclusion, at the appearance of new clusters of technical and scientific principles, the consolidated skills and abilities of the workforce become a constraint in the initial stages of the process of creation of new production options. The next section analyses the data at our disposal. 3 PRODUCT INNOVATION: THE RESULTS OF OUR INQUIRY A number of aspects concerning the innovation of products have been investigated. Special attention is paid to research and development, and to the perception of human resources constraints connected with this activity.

RICCARDO LEONI 217

3.1 The forms of research and development activity The activity of researching and designing product innovation can take place either within or outside an establishment. In the latter case, for those production units belonging to a company with several factories (that is to say, multilocational companies), the activity in question can be located in other establishments belonging to the mother-company. This form, which we shall call internal/external, is distinct from the form which is fully internal to the factory and from the external form (i.e. one acquired from firms independently of the interviewee). The locality of research activity can be interpreted either in terms of industrial organization or in terms of territory; in this chapter, aspects of localization will not be taken into consideration. Table 11.1 indicates that out of the 173 factories which innovated products between 1982 and 1987, 146 (that is 84.4 per cent) did so through internal ability. The percentage does not change (84.8 per cent) in a survey of monolocational firms (Table 11.2). The distribution by technological group5 and by the size of the firm shows a greater endowment of these ‘abilities’ in high-tech and mediumsize firms. It is necessary to add to the internal ability the quota acquired outside the establishment, both from other factories belonging to the mother-company (internal/external form) and from other formally independent companies; combinations of the two forms are also possible. Moreover, the external contribution can either be in addition to or can substitute the internal form. From a separate analysis of the establishments belonging to multilocational firms, it is clear that the acquisition of research activity from other establishments is decidedly limited, equal to 33.3 per cent of cases, of which about 25 per cent of the establishments used both their own abilities and those of other establishments of the same firm contemporaneously. Research commissioned both outside the establishment and in the firm amounts to about 40 per cent of cases (cf. Table 11.1) and is evenly distributed between technological groups and size classes. Table 11.1 Distribution of establishments according to the source of R&D of product innovations realized within the period 1982–1987. Absolute values and percentages (multiple answers admitted) Within establishment 84.4% Within firm* 30.8% External both to establishment and firm

Yes

No

Valid answers

146 15.6% 12 69.2% 68

27 100% 27 100% 105

173 39 173

218 HUMAN RESOURCES CONSTRAINTS IN INNOVATION

Yes

No

Valid answers

39.3% 60.7% 100% * Relatively to establishments belonging to multilocalized firms Table 11.2 Presence of R&D capacity within establishments Presence

Firms

Total valid answers

Monolocalized

Multilocalized

Yes 84.8% No 15.2% Total valid answers 76.7%

112 85.0% 20 15.0% 132 23.3%

34 84.9% 6 15.1% 40 100%

146 26 172

Percentage distribution of positive answers* Presence

Technological groups

A

B

Yes 95.2 No 4.8 Total valid answers 100 15.9 22.0 * Limited to mololocalized firms

Sizeclasses

C

10–49

50–99

100–499

89.7 10.3 100 62.1

80.5 19.5 100 28.8

81.6 18.4 100 36.4

83.3 16.7 100 31.8

500 90.5 9.5 100 3.0

75.0 25.0 100

Table 1 1.3 Relationship between R&D capacity within establishments and capacity acquired in the market Research and design Yes

No

Acquired in the market 39.0%

Yes 40.7% No 61.0%

Total valid answers 100.0%

100.0%

Within establishment

Total valid answers

57 39.3% 89 59.3% 146 100.0%

11

68

16 60.7% 27

105 173

Of the 68 factories which resorted to the acquisition of external research, about 84 per cent did so to integrate internal ability. So, external research substituted internal research in only 16 per cent of cases (Table 11.3). These results tend to back up the thesis put forward by Cohen and Levinthal (1989), according to which the acquisition of external knowledge requires, at

RICCARDO LEONI 219

least, internal ability and organizational structures for its reception and absorption. The ability to innovate products can assume a different form within the establishment: the autonomous behaviour which materializes can reasonably be taken as a good indicator of the strategy of the company; the company wishes, structurally, to retain a position in which advantages can be gained over the competition6. It has already been noted that of the 206 firms interviewed, 173 (84 per cent) are involved in some form of product innovation. Of these, 146 (84.4 per cent) claim to have used internal (or internal/external) research ability. Yet, of these, only 112 (76.7 per cent or rather 64.7 per cent of the total number of innovating firms) possess an autonomous research department. This structure is evenly distributed between technological groups and size classes, with the exception of those firms which belong to the low technology group or to the class with over 500 employees, where the numbers are lower (Table 11.4). The average number of those employed in activities inherent to processes of product innovation (Table 11.5) totally confirms the expectations, especially in the technological groups. By size classes, the results concerning large firms are coherent with those concerning the existence of a research department. The result for the small firms may give rise to some doubt: it might, however, be justified by the multifunctional role of the employees. The findings support the idea (cf. Sahal, 1981, Chapter 6) that the technological innovation of output depends on the availability of skills operating directly in research and development activity. In fact, transfusions of know-how from one organization to another appear to be somewhat infrequent. In particular, since innovation appears to be mainly related to the existence of wellorganized efforts within the units of production interviewed, the outcome backs up the thesis which sustains the existence of difficulty in transferring external know-how. 3.2 The processes of upgrading and the effects of qualified human resources Adjustments in the quality of human resources made necessary by the processes of product innovation can be carried out by the firms in two different ways: through courses and retraining both within and outside the production units, or by recruiting in the market those with the necessary qualifications and dismissing those without these qualifications. The first way was chosen by 102 production units, that is 64.7 per cent of the innovating firms, or about 50 per cent of the whole sample (cf. Table 11.6, column 6, row 4). The results of the high-technology and medium-size firms are much higher than the average values of the sample. The quota of personnel involved in one or more of the various processes of professional training set up

220 HUMAN RESOURCES CONSTRAINTS IN INNOVATION

by the firms between 1984 and 1987 was 23 per cent; this figure rises with the technological sophistication of products and with firm size. Of the various forms of training, high-tech firms tend to prefer internal training, hence indirectly supporting the idea of job-specific, or at least firm-specific qualifications, which Piore-Doeringer’s thesis tends to ascribe to the role of technology. Table 11.4 Relationship between R&D capacity within establishment and presence of autonomous research department (absolute values in terms of establishments) Capacity within establishment Technological group Total firms A Yes

No

Autonomous department Yes 108 – 73.0% No 40 20 Total 148 20 100%

B

C

Total Yes No

Total

Yes

No

Total Yes No Total

108 25 89.3% 60 3 168 28 100%

25 77.1% 5 30 100%

27

–

8 35

1 1

27 56 65.9% 9 29 36 85 100%

– 2 2

–

56

17 17

46 102

Size classes Autonomous department Yes 21 70.0% No 9 Total 30 100%

10–49

50–99

–

–

7 7

21 27 58.7% 16 19 37 7 100%

7 46

100–499

27 43 81.1% 26 10 53 53 100%

– 4 4

500

43 14 93.3% 14 1 57 15 100%

–

14

2 2

3 17

Table 11.5 Employees in design, development and research activities Number of establishments

Technological groups: A 26 B 29 C 58 Total 113

Total employees in R&D

Average number of employees in R&D per establishment

Percentage employees in R&D on total employees

326 345 313 984

12.5 11.9 5.4 8.7

5.10 3.68 1.57 2.76

RICCARDO LEONI 221

Number of establishments

Size classes 10–49 27 50–99 29 100–499 49 500 8

Total employees in R&D

Average number of employees in R&D per establishment

Percentage employees in R&D on total employees

62 94 421 407

2.3 3.2 8.6 50.9

3.44 2.07 3.03 2.64

Out of the whole sample, the quota of employees involved in training processes organized by the firms and made necessary by product innovation is 10.9 per cent (final column). The percentage is much higher in firms concerned with the technological complexity and sophistication of the products. Recourse to market recruiting of individuals with required professional qualifications is not always satisfactory. In any case, both methods were incapable of answering the needs of the production units interviewed: from the data collected, it emerges that, both at the time of the inquiry and in the past, firms have suffered from the shortage of qualified personnel available on the market and have been forced to restrict attempts at innovation. More precisely, 30 per cent of the firms interviewed restricted or delayed innovation processes because of the scarcity of available personnel. This constraint proves greater for high-tech firms than for those which produce low-tech goods (the per centages are, respectively, 47 per cent and 23 per cent), and for medium to large firms rather than for small firms (respectively 33– 45 per cent and 20 per cent). 4 JOB VACANCY RATIO OF TECHNICAL OCCUPATIONS The role of the technical personnel is not only confined to product innovation, but also affects other aspects of the firm’s activity as the sample firms have also carried out innovations of equipment, using new and more sophisticated machinery.7 It is difficult to quantify the role played by human resources in the accomplishment of single processes, especially in those firms where there is no rigid delimitation of the personnel functions.

222 HUMAN RESOURCES CONSTRAINTS IN INNOVATION

Table 11.6 Establishments engaged during tire period 1984-1987 in some process of ‘training’ of their employees, by technological groups, employment size, type of processes. Absolute value (av) and percentages (%) Tec Size Classes hnic al gro ups

Total Internal External establis cources courses hment involved

Training labour contract s

Investm We We ents in igh igh invididu ted ted * † als per Pe cen rce tag nta e ge em em plo plo yee yee s s on on tra tra ini ini ng ng

%

av

%

av

%

av

%

av

%

av

%

A

25

15

49

57 .7 59 .3 58 .7

17

C

53 .8 59 .3 45 .7

15

28

57 .7 45 .4 30 .4

14

B

83 .3 45 .4 30 .4 49 .5

65 .4 77 .8 49 .0

38 .2 27 .2 14 .8

10– 49

Tot al est abli sh. inv olv ed Int ern al cou rse s 33. 3 Ext ern al cou

50 – 99

10 0– 49 9

5 00

av

19

32

46

5

6

10

23

3

30 .3 5

52 .3 16

60 .0 26

4

10 2

16 21

42

16 21

42 .0

51

51 .5

16 27

21 24

32 .2 10 .1 4. 6

RICCARDO LEONI 223

Tec Size Classes hnic al gro ups

10– 49

rse s 27. 8 Tra ini nglab our con trac ts 55. 6 Inv est me nts in ind ivi dua ls 42. 1 We igh ted *

50 – 99

10 0– 49 9

5 00

av

48 .5 10

60 .5 20

80 .0 27

1

60 .6 8

62 .8 17

20 .0 33

4

53 .1 23 .7

71 .7 13 .8

80 .0 19 .9

30 .1

Total Internal External establis cources courses hment involved

Training labour contract s

Investm We We ents in igh igh invididu ted ted * † als per Pe cen rce tag nta e ge em em plo plo yee yee s s on on tra tra ini ini ng ng

%

%

av

%

av

58

58 .6

62

60 .8

av

%

av

%

av

%

23 .0

224 HUMAN RESOURCES CONSTRAINTS IN INNOVATION

Tec Size Classes hnic al gro ups

10– 49

per cen tag e em plo yee s in trai nin g We igh ted

50 – 99

10 0– 49 9

5 00

av

8. 2

7. 0

11 .8

12 .1

Total Internal External establis cources courses hment involved

Training labour contract s

Investm We We ents in igh igh invididu ted ted * † als per Pe cen rce tag nta e ge em em plo plo yee yee s s on on tra tra ini ini ng ng

%

%

%

av

%

av

%

av

av

av

†

per cen tag e em olo yee s * †

On total employees of establishments engaged in some form of ‘training’ On total employees of all establishments included in each cell

%

10 .9

RICCARDO LEONI 225

Table 11.7 Question: Have you limited or delayed your product innovation due to shortage of qualified human resources? Answers

Number Technological groups establishmen ts

A

B

c

Size classes

20–49 50–99 100– 499

Yes 62 15 16 (30.2) (46.9) (40.0) (23.3) No 143 17 24 (69.8) (53.1) (60.0) (76.7) Total 205 32 40 establish. (100) (100) (100) (100) Note: Figures in parentheses are percentages

500

31 12 (19.7) (34.8) 102 49 (80.3) (65.2) 133 61

23 22 5 (32.8) (45.5) 43 45 6 (67.2) (54.5) 66 67 11

(100)

(100)

(100)

(100)

Quantification has proved particularly difficult in those firms which, although involved in the processes of product innovation, do not possess an autonomous research department. We limit the quantification to the per centage of technicians8 firms would have liked to have, in all, over the number on the books. This is certainly an indirect and approximate measure of the rationing suffered by firms in the processes of creation of product innovation. However, if this measurement is seen alongside that of firms limiting attempts to innovate products (cf. Table 11.7), then it may be possible to confirm or disprove the role played by the shortage of qualified human resources. The rate of job vacancy in the technician category (defined and definable in different ways) was just under 20 per cent (cf. Table 11.8) for all the firms interviewed. The average datum is the fruit of a rate of over 30 per cent registered in the firms in group A (high-tech), of a rate of 14 per cent registered in the firms in group B (medium-tech) and 15 per cent in group C (low-tech). In terms of size, small production units suffer more from this constraint than medium-large units. The reasons might reasonably arise from the fact that larger firms attract qualified technicians more easily for economic and structural reasons —that is, higher salaries and better chances of promotion. 5 CONCLUSION The empirical results set forth support and verify the lines of argument developed in the first part of this chapter. In particular the data tend to emphasize the importance of qualified human resources in relation to three aspects of innovating activity. That is:

226 HUMAN RESOURCES CONSTRAINTS IN INNOVATION

1 to the possibility of carrying out the product innovation within the production unit, in that the transfusion of know-how from outside, being very restricted, is difficult to put into action: 2 to the amount of product innovation which is possible in a given production unit; 3 to the speed at which technological innovation is diffused. Table 11.8 Percentage of ‘technicians’ employed in firms in 1981 and in 1987; job vacancies registered for technicians at the interview Employed in 1981

Employed in 1987

Desired in 1987

Sampl Standa Sampl Standa Sampl e mean rd e mean rd e mean mean deviati on

Technological groups: A 13.8 14.6 B 16.5 21.1 C 7.6 8.7 Total 10.4 13.5 Size classes: 10–49 11.1 15.4 50–99 7.9 6.5 100– 9.9 12.9 499 500 24.2 26.0

1987

Standa rd deviati on Job On vacanc total ies (%)

Techni cians emplo yed On total emplo yees (%)

18.4 18.3 9.3 12.5

15.0 19.9 9.2 13.6

21.9 21.0 10.6 14.4

16.8 21.3 10.2 15.0

30.2 13.5 15.3 19.3

5.7 1.8 0.05 2.1

18.9 13.4 0.3 11.0

13.8 9.6 12.5

15.4 6.6 14.2

15.7 10.8 15.3

16.6 7.1 16.8

29.9 12.6 21.6

1.9 1.1 2.2

6.2 9.1 10.0

22.7

23.8

24.2

23.1

18.6

2.4

12.9

The persistence of the rationing of human resources might induce firms— especially those which operate in areas where the institutions react more slowly— to reduce the number of their productive niches to that compatible with available human resources, thus generating a technical process of despecialization of the area.

RICCARDO LEONI 227

NOTES 1 I would like to thank A.Cristini and M.Morroni for helpful suggestions and comments, and exonerate them from any remaining shortcoming in the chapter. Financial support from the Italian Ministry of Education is acknowledged. 2 The province of Bergamo, in Lombardy. 3 On these single aspects cf. Malerba and Orsenigo (1989), and bibliography. 4 This coincides with the firm’s decision to make—according to Amendola and Gaffard’s model—an innovative choice. See Amendola and Gaffard (1988, p. 43). 5 The classification of factories according to technological contents is made with references to output. The classification and criteria used are explained in Leoni (1989a). 6 On the concept of competitive advantage cf. Porter (1985). 7 In the process innovation, out of the 199 production units interviewed, 192 said that they had renewed their machinery between 1982 and 1987. Of these, 172 said that they had adopted more sophisticated machinery in their production processes, 20 had renewed their production capacity using the same type of machinery as that which was previously installed, and 14 had renewed their machinery installing both types. The average per centage of renewal of machinery was 36.6 per cent. For further details on this subject see Leoni (1989b, Table 3.7, Chapter 3). 8 The hypothesis of complementarity between the figure of the technician and the one of the skilled worker in the three technological groups is verified in Leoni (1988). On the basis of the 1981 Industry Census data, the coefficient of complementarity between the two figures proved equal to 10.6 per cent, after checking by size and by group. See Leoni (1988, p. 30).

BIBLIOGRAPHY Amendola, M. (1988), ‘Visione ex-post e Visione ex-ante nelle Teorie della Produzione’, in S.Zamagni (ed.). Amendola, M. and Gaffard, J.L. (1988), The Innovative Choice, Oxford, Basil Blackwell. Cantalupi, M. (1988), ‘Path-dependency, Stilizzazioni Tecnologiche e Teoria della Produzione’, in S.Zamagni (ed.). Cohen, W.M. and Levinthal, D.A. (1989), ‘Innovation and Learning: The Two Faces of R & D. Implications for the Analysis of R & D Investment’, Economic Journal, Vol. 99, September, pp. 569–596. David, P. (1988), ‘Path-Dependence: Putting the Past into the Future’, Stanford University, Technical Report 533. Dosi, G. (1983), ‘Technological Paradigms and Technological Trajectories. A Suggested Interpretation of the Determinants and Directions of Technical Change’, in C.Freeman (ed.) Long Waves in the World Economy, London, Butterworths. Fazzari, S.M., Hubbard, R.G. and Petersen, B.C. (1988), ‘Financing Constraints and Corporate Investment’, Brookings Papers on Economic Activity, No. 1, pp. 141–195. Freeman, C., Clark, J. and Soete, L. (1982), Unemployment and Technical Innovation, London, Frances Pinter. Leoni, R. (1988), ‘Innovazioni Tecnologiche, Occupazione e Skills in un Mercato Razionato’, Discussion Paper no. 8, University of Bergamo.

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Leoni, R. (1989a), ‘Sulla Definizione di Industrie High-tech Tramite Indicatori di Input’, L’Industria, No. 2, pp. 247–276. Leoni, R. (1989b), Innovazioni Tecnologiche, Posti di Lavoro e Risorse Umane. Un ‘Case Study’: Bergamo Verso il Duemila, Milan, F.Angeli. Malerba, F. and Orsenigo, L. (1989), ‘Teoria Evolutiva e Innovazione Industriale: Risultati Empirici degli Anni Ottanta’, Paper submitted at the meeting of the Società degli Economisti, Rome, 26–27 October. Nelson, R.R. and Winter, S. (1977), ‘In Search of Useful Theory of Innovation’, Research Policy, No. 6, pp. 36–76. Nelson, R.R. and Winter, S. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., The Belknap Press. Perez, C. (1983), ‘Structural Change and the Assimilation of the New Technologies in the Economic and Social System’, Futures, Vol. 15, No. 4, pp. 357–375. Porter, M.E. (1985), Competitive Advantage, New York, Free Press. Sahal, D. (1981), Patterns of Technological Innovation, London, Addison-Wesley. Spence, A.M. (1979), ‘Investment Strategy and Growth in a New Market’, Bell Journal of Economics, Vol. 10, No. 1, pp. 1–19. Zamagni, S. (ed.) (1988), Le Teorie Economiche delta Produzione, Bologna, II Mulino.

12 Firms, uncertainty and innovation policy Some spatial considerations in an evolutionary framework Mario Agostino Maggioni

1 INTRODUCTION In the economics of technological change the spatial factor has often been unjustly neglected, even within the debate arising from an evolutionary approach (see Dosi et al., 1988). Therefore, in most models1 space is defined by means of political-geographic categories (nations) or reduced to a mere physical support of production activities (land, capital, labour). Such interpretation appears to be surpassed by some characteristics of the present technological paradigm (information-communication) and contradicted by the increasing economic importance of local systems of production where new informative and organizational relations are experimented. Therefore the scenario described by traditional inter-countries, interindustries, inter-firms comparisons is incomplete and has to be integrated by a theoretical and empirical space-based perspective. On the basis of these findings some proposals for a policy for real support to the innovative activity of firms will be suggested. With this purpose in mind I intend to further analyse the relationship between the firm and the public authorities, between the firm and the environment and between the firm and the innovation in a dynamic framework driven by technological change. In order to understand these relationships, some explanatory remarks regarding the reasons and the types of public innovative policies should be provided. 2 THE REASONS FOR INNOVATION POLICY The reasons for public intervention in innovation matters have been widely debated in the literature.2 The seminal work by Arrow (1962) highlights the frequent divergence between firms and local or central authorities on the determination of the optimum amount of resources to devote to R&D activities. Public intervention is

230 FIRMS, UNCERTAINTY AND INNOVATION POLICY

therefore seen as a remedy for this market failure.3 Such reasoning underlies the rationale for a public regulation system over patents. This system appears to be the only possible compromise between the following requirements: that there be a reward for the innovator, whilst at the same time ensuring a quick spread of the innovation to society. Arrow points to the fact that public authorities are in a better position to consider numerous possible phenomena of externalities (positive or negative) associated with the development of a new technology. An alternative approach (somewhat more pragmatic) (see Freeman, 1982) underlines the advantages of a general co-ordination of the innovative process, which cannot be attained by private institutions alone. In this perspective public intervention is able to cope with the pervasiveness of the innovative process, together with the heterogeneity of the numerous agents involved. In this way the presence of increasing returns in the process of research (learning to learn), production (learning by doing), and use (learning by using) of an innovation would not run the risk of creating monopolies. In addition this approach highlights the role of technological change as a strategic factor in the process of the development of an economic system and in the identification of a comparative advantage in international markets. 3 WHAT ARE THE APPROPRIATE POLICIES FOR INNOVATION What is left unconsidered by standard economic theory is the need to ensure that such public intervention can work effectively within this context.5 For simply proving that the market cannot assure the optimum allocation of resources for R&D does not ensure the success of government intervention in the same area. Before sanctioning intervention it is necessary to verify that the target can really be achieved and then to analyse the most suitable method to be implemented given the situation in hand. The political actions of public authorities in this and other fields may in fact develop according to the different types of intervention which exist in a continuum between the following two extremes: the centralized economic planning approach on the one hand and a total laissez-faire approach on the other. In many scientific and innovative interventions however, public action can be seen to have been limited just to the two extreme alternatives: the complete management of the whole process (as in ‘Manhattan Project’) or the support in a vaguely defined ‘innovative activity of firms’, through an indiscriminate supply of financial resources. These two opposite views both imply a unique partial concept of the innovative process.6 Such a conception is supported by a partial rereading of Schumpeterian theory, according to which innovation is seen as a sporadic and unforeseeable event, arising from a complex and mysterious mechanism: the invention. Invention here is defined as the product of a ‘black box’ where the individual genius or the secret activity of a limited group of

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researchers, inside an industrial laboratory, assumes the role of a catalyst of an immense and casual list of ingredients. The evolutionary approach takes into account the spread of innovative industrial districts, science parks (SP), tecnopolies, and introduces into the economic theory more realistic variables which underline the historic and pathdependence characteristics of the innovative process. In this way the role of external economies of agglomeration, increasing returns and, in general, the relationships and interactions amongst people, firms and institutions are emphasized. According to this approach public authorities have to be aware of the ‘spontaneous’ strategies adopted by firms (individually or collectively) when facing uncertainty and innovation. For it is only in this way that it is possible to avoid distortions and wasteful duplication of activities. 4 INNOVATION AND THE FIRM Firms’ innovative activities are hindered by the high transaction costs, arising from the following characteristics of innovation: indivisibility, inappropriability, uncertainty. The indivisibility of knowledge (which is fundamental to the process of innovation), by its very nature results in the indivisibility of the innovative process and causes problems regarding the minimum efficient scale of ‘plants’ (laboratories or R&D divisions of a firm). Furthermore, as it results in the noncontinuity of the production function, the application of the usual maximization procedure, to determine the quantity to produce, is more difficult. Inappropriability means that the innovator is unable to enjoy exclusively the gains from the innovation. From this perspective innovation acquires some of the typical characteristics of a public good—namely, it is difficult to distinguish between individual and collective consumption: difficulties arise in determining the optimum allocation of resources for production; and the determination of a fair and efficient tax to finance such a production is more difficult. Uncertainty, according to the most general definition, arises whenever a decision can lead to more than one possible consequence (Hammond, 1988). According to Knight (1921) it is possible to distinguish between risk and uncertainty by using objective or subjective criteria to assign a probability to different outcomes.7 In the case of the innovative process, however, a more complex and difficult phenomenon has to be faced. A phenomenon which has been defined by Dosi (1988, p. 1123) as: ‘strong uncertainty…whereby the list of possible events is unknown and one does not know either the consequences of particular actions for any given event’. Such an uncertainty, which is typical of the so called pre-paradigmatic phases of innovative process, concerns both the existence, the qualitative characteristics of the results of research and the choice of the most suitable scientific-technological principles, together with the

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procedures for problem-solving, which are fundamental to every process of innovation. The following analysis, therefore, concentrates on the consequences arising from the presence of uncertainty. For, in contrast to the description provided by standard neoclassical theory (according to which a market is defined as an efficient mechanism which regulates, through prices, any transaction occurring between rational and perfectly informed agents), firms operate in conditions of uncertainty, which arise from a structural lack of information and the inability to judge and control the external environment. The main sources of uncertainty which the firm faces have been described by Camagni as being due to the: information gap: linked to the complexity, the width and the cost of the information collection activity; assessment gap: linked to the difficulty of inspecting ex-ante the qualitative, mainly hidden, characteristics of inputs, components, production factors, technical equipments; competence gap: linked to the firm’s limited ability in processing and understanding available information; competence-decision gap: linked to the impossibility of accurately assessing the outcomes of alternative actions, even in the presence of full and free information on past events; control gap: linked to the firm’s impossibility to control the dynamic interaction among the independent decisions of many different actors (such as public institutions, local authorities, universities). (Camagni 1989, p. 5) In order to reduce this uncertainty to some acceptable levels (enabling the firm to operate within the economic system) each agent becomes involved in at least one of the following activities: research, screening and signalling, transcoding, selection, control. 5 THE FIRM'S RESPONSES TO UNCERTAINTY The aforementioned activities can be implemented by a firm through its traditional internal procedures and departments (technology monitoring, quality control and certification, R&D, definition of decision routines and management styles and its hierarchical structure). Alternatively the firm can involve other agents according to either an environmental or an interactive-structural strategy. The environmental aspect highlights the concept of ‘industrial atmosphere’ (see Marshall, 1923, 1949) and recalls the concept of internalization of the market (see Coase, 1937; Williamson, 1975, 1989). According to this perspective, the driving force behind technological change shifts from firms to

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collective space-based entities such as the local environment, the integrated area, the industrial district, or the milieu.8 The interactive-structural aspect (see Håkansson, 1987, 1989; Vaccà, 1986; Di Bernardo et al., 1986) on the other hand, focuses on the relationships between actors, activities and resources both inside and outside the local area. 5.1 The local environment It is generally accepted that the pioneering work in the analysis of industrial production and economic development of specific areas, has been provided by Alfred Marshall. In observing the economic and industrial development of some towns and local areas (Sheffield, Lancashire, Solingen) he concludes that, in contrast to the established industrial paradigm (Fordism), there is an alternative method of development based on small and middle-sized firms. The success of such an industrial structure is due to techniques employed in some sectors (i.e. textiles), where large scale is not a necessary condition for specialization. In such an ‘industrial district’ (Marshall, 1923), the role of external economies of localization becomes enormously important, given the cost reductions that arise in: (a) the production (due to the harmonization and increasing interdependence of production processes); (b) the transportation (by drastically reducing the average distance between suppliers and customers); (c) the employment of skilled workers (easily available in large number); (d) the supply of specific training (partially replaced by informal learning based on experience and by sharing an homogeneous industrial culture);9 (e) the spread of information (in a limited community the technological progress is easily spread and technological knowledge becomes public property). Marshall summarizes all these phenomena with the expression ‘industrial atmosphere’: ‘a privilege or spin-off which is freely available to any member of the club (the industrial district) and not available at all to the outsiders’ (Reisman, 1986, p. 86). The industrial atmosphere is a comparative advantage owned by industrial districts because of the stratification and historical consolidation of cultural, social and productive links. The relevance of these spatial aggregations amongst firms is also illustrated, although from a different perspective focusing on transaction costs, in the works of Coase (1937) and Williamson (1975). Building on their approach, Bianchi (1989) assigns to the following different inter-firm relationships (market, quasimarket, quasi-hierarchy, hierarchy) equivalent sums of co-ordination and

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Figure 12.1 The local environment Source: Camagni (1989, p. 8)

information costs.10 An industrial district, in contrast to the above, would be represented here as a situation of a quasimarket, whose main objective is the reduction of information costs without leading to a consequent increase in coordination costs. Therefore, the industrial atmosphere could be interpreted as a sunk cost which creates a barrier to entry for those firms outside the district. The aforementioned definition of Camagni, however, cannot be classified within the two previous approaches. He describes, with the aid of a diagram (see Figure 12.1), the ‘milieu’ as an intermediate agent, acting between the firm and its external environment, that manages the problems of innovation. The search function describes any activity which, through the informal exchange of information, ensures: (a) the diffusion of behavioural models and successful decisions concerning both the market and technology; (b) the economic and technological monitoring of the external environment;

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(c) the selection of more reliable informative channels through a continuous testing process by trial and error. The screening-signalling function includes two different but interdependent activities: 1 The first activity defines the selection and evaluation of the signals originating from the market and the discovery of the ‘hidden’ qualitative characteristics of the required inputs (both capital and intermediate goods). 2 The second activity is devoted to the external promotion of the image and ‘reputation’ of the area, supplying a sort of qualitative and indirect certification of products. The milieu in this context can be compared to a transmitting-receiving aerial for communications between the firm and its external economic environment. The transcoding function defines a collective learning process, caused by a higher degree of inter-area skilled labour mobility, by the technical relationship of customers and their suppliers, by imitation processes and ‘reverse engineering’. In this way the external incentives can be quickly translated, adopted and adapted successfully to the peculiarities of the local structure. From an ‘evolutionary’ perspective this function appears to be the main objective of R&D, for according to Camagni: Utilising codified information, both freely available and costly, and merging it with tacit and informal information, transcoding activities convert a chaotic and un-ordered ‘information’ flow into a firm-specific ‘knowledge’ and possibly potential business idea at the disposal of the managerial decision making. (Camagni, 1989, p. 5) The selection function describes the collective process of defining ‘management styles’ and procedures of the firm established through the solidarity of employers’ associations and the implementation of complementary innovative processes. The control function is the most recognizable feature of the local environment: the social and cultural cohesion of the area is such that it affects the competitive behaviour of rival firms (through the respect of a strict ‘moral’ code) and involves the local public authorities as well. The transformer function implies a widening of the function of transcoding. Thus it is not only the case that information and specific technological knowledge are decoded and adapted to the specific features of the area, but also the qualities of human capital arising from the labour market and the output of the educational system are organically integrated and matched with the local culture.

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In all the previous functions it is the physical proximity of economic agents which constitutes the fundamental assumption. The importance of this physical proximity arises because of: (a) the relative outer impermeability of these structures as far as the mobility of skilled labour is concerned; (b) the impossibility of re-establishing the industrial atmosphere,11 in a different place and at a different time; (c) the nature of the collective subject of the environment, due to the blending of historical, cultural, psychological and political values (see Bagnasco, 1977). This associative strategy, however, does have a number of shortcomings: 1 A complex structure is more suitable to transmit and spread information, incentives and news than to implement quickly a common strategy of action. 2 The local environment experiences a rapid decline of its industrial structure as a result of the two following factors, one internal and the other external. The first one concerns the exponential increase of rental costs together with an over-concentration of new ‘imitative’ firms in the successful district because of ‘bandwagon’ effects. The second one points to the fragility of the local structure when faced with a sharp change in international markets or the revolution of technological paradigms. Further restrictions are highlighted if one examines a dynamic scenario. In such a framework the milieu has not only to gather qualitative and quantitative resources that are sufficient enough to cope with the unforeseen and irreversible environmental shocks, but it is also required to manage such a process, so as to become the engine of change. Phillips (1980) shows that ‘organizational persistence’ phenomena— which hinder the dynamic process of change—are present in every kind of co-ordinate structure. A high degree of externalization of these structures may cause even further inefficiency and organizational inertia at a time when a quick response and a higher degree of internal consistency are required to cope with a change in market conditions. Teece (1986) underlines the risk linked to an unlimited decentralization of production. This process can result in the deterioration of specific skills essential to assure the long-run competitive advantage of the firm. When the milieu is facing sharp changes in technology, the firm is no longer able to adapt the same intangible assets (industrial atmosphere) to a new product/ process (Bianchi, 1989). For the industrial atmosphere becomes an obstacle to change, a barrier to exit from a sector which is in crisis. In light of these problems, the concept of the milieu appears more suitable for the management of inter-firm relationships during periods of incremental

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innovation and along predefined technological trajectories, whose evolutions are controlled and governed inside the system-area (Dosi, 1982). For it has been shown that, when a new paradigm arises, the industrial district often cannot react, and a process of decline will inexorably set in, leading to: (a) a declining area marked by an increasing and irreversible loss of competitiveness both in domestic and international markets; (b) a quasi-hierarchical structure, where the production network is structured as the relationship between sub-contractors and local leaders. 5.2 The network The concept of network was originally used in the analysis of electrical circuits. The network here is defined, according to Lovitch and Rosati (1979), as ‘the set of branches which link two or more junctions’. This definition, taken from a physics handbook, appeals because of its simplicity and adaptability, enabling it to be embedded in an economic context. Over recent years a composite literature concerning the network model has emerged from microeconomics and organizational theory debates. I will first examine some of the most interesting interpretations of the network concept to verify whether they are consistent in themselves, and analyse their explanatory power concerning technological change. 5.2.7 The theoretical analysis Two recent contributions in this area have been made by Vaccà and Håkansson. Vaccà focuses on the organizational features of the network, whilst Håkansson focuses on the specific characteristics of technological and innovative dynamics. Vaccà (1986) embodies the network model—seen as a response to the increasing complexity of the economic reality (world-wide partnership, rapid technological change, re-definition of the role of supply and demand) —into the wider analysis of externalities management. According to Vaccà there are three main methods to cope with externalities, these being the transaction, the system and the network. The transaction describes a relationship between two agents (firms), defined by a legal contract, which establishes the rules for the exchange of goods and services between the two parties. The system is a set of relationships established and enforced through routines. The network is a dynamic set of interdependent relationships, the distinguishing feature of which is a specific language. An assessment of each of these three methods within a dynamic technological framework characterized by discontinuity, plurality and interdependence

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highlights some problems associated with the transaction and system methods of coping with externalities. The transaction, because of its own legal nature, emphasizes the bilateral and contractual aspect of a relationship in an atomistic framework. For this reason it cannot simultaneously manage several informal relationships which are continuously changing. The transaction is in fact a simplification of an economic reality: the pure exchange economy, in which the transfer of goods and services is the only available means to forge relationships between agents. The system highlights the leader role which is adopted by a central decisionmaking body. Therefore it cannot be used in the context of a multicentred environment which is not organized by a rigid hierarchical structure. Furthermore, because of its inner criterion which is driven by a stabilizing logic,12 the system cannot allow for a dynamic situation characterized by discontinuous transformations. The system, however, seems to be a more appropriate means of maintaining and stabilizing the pattern of existing relationships and the heritage of the past, as opposed to being regarded as the potential engine for future relationships which have not yet been tested. The network represents an ‘open and multicentred structure which allows several innovative experiments and promotes a variety of contributions and of different capabilities to bear risk’ (Vaccà, 1986, p. 15). In my opinion, however, it is more important to highlight a further factor which supports the use of the network structure: the network’s dynamism, which is the capacity of evolving through time. The transaction, on the other hand, which is defined as a set of rules (which themselves are anticipated responses to future problems), is timeless and static, whilst the system is essentially conservative and is directed towards self reproduction via homeostatic mechanisms. It is the network only which introduces an evolutionary principle which can modify the complex set of interagent relationships. Within a framework of increasing technological and industrial interdependence, languages become the key factor of a network. Through them the firm accesses in real time all the information and the knowledge present in the external environment. The actual technological paradigm stresses the role of languages as an efficient way to solve the following problems: (a) the multicentred nature of the innovative process and the consequent need to aggregate several diverse agents with different ability levels on specific projects; (b) the high degree of specialization of resources which must be acquired from the outside environment (in many high-tech sectors the producer places only a flexible technological capability at the intermediate user’s disposal, and it

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Figure 12.2 The network Source: Håkansson (1987, p. 17)

is the intermediate user who then decides on the specific characteristics of his product); (c) the increasing codification and the standardization of know-how required for a swift transmission and transferability of technological knowledge among firms, as well as an increasing integration of the production cycle and a wider diffusion of flexible manufacturing systems. Håkansson’s approach, on the other hand, considers the role of the network structure within a technological framework from a structural perspective. Håkansson (1989, p. 15) defines innovation as a process ‘in the form of a technical exchange between different actors such as individuals or companies’. The focus here is shifted from the behaviour of the single agent towards the concept of technical exchange (mutual transfer of information, knowledge, and technical experience), the key role being played by the relationships between different actors. According to Håkansson, the innovation has to be considered as being the outcome of the network of activities and resources between the actors involved (see Figure 12.2).

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Håkansson defines as actors those who perform activities and/or control resources in a specific situation. Actors can be individuals, firms, or groups of firms. Every single actor can be defined according to three dimensions: the activities performed, the resources controlled and their knowledge of other activities, resources and actors belonging to the network. The main aim of each actor is to increase the control of the network. Activities, on the other hand, are defined as the combination, development, exchange (transaction activity) or creation (transformation activity) of resources using available resources. These activities are performed by actors. Activities are often grouped into chains (sectors and filières) and they themselves lead to relationships between actors. They may represent stages in cycles in which each activity is repeated according to interdependent routines. This actor-activity relationship is characterized by dynamic economies of learning in such a way that the longer the actor performs an activity the more efficient he becomes. Resources are physical (machinery, raw materials), financial (money, bank credits, shares and equities) and human endowments (labour, knowledge and relationships). Transformation and transaction resources are mutually dependent. The use and value of a specific transformation resource is dependent on the way in which it is combined with transaction resources, and vice versa. The only constraint on the possible number of combinations of different resources is given by human creativity. The cohesion of this kind of structure is neither a result of a hierarchical dependence from a central authority nor the outcome of the utility maximization procedures performed by rational agents within the market. More specifically, the network designates the set of interdependent relationships between the actors in the innovative process. Moreover, it represents an efficient device for managing two interrelated activities which are essential to the process of technological innovation, these being the creation of new knowledge and the subsequent implementation of the original project through the gathering and co-ordination of resources. The network, on the one hand, assures the interaction among different disciplines and makes the exchange of information among the members of the scientific community easier. On the other hand, the network facilitates the management of the required amount of other goods, processes and services in the external environment by co-ordinating the actions of several heterogeneous actors. 5.2.2 A factual-theoretical analysis Recently, scholars have used the network model to explain the increase of ‘cooperative agreement’, ‘technological joint-venture’ and ‘strategic partnerships’ (alternative strategies to both market relationships and hierarchical acquisition) in the behaviour of the most innovative firms. This literature (Mariti and Smiley,

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1982; Momigliano and Balcet, 1982; Mariotti and Migliarese, 1984) emphasizes the advantages which arise from the adoption of network relations in order to: (a) reduce the risk of contractual failures or asymmetry of information; (b) support the exchange of innovative resources such as know-how (which is often intangible and hard to patent); (c) promote the ‘speed economies’ (Teece, 1986) in the exploitation of a specific resource and in the strategic relocation in a certain market. Furthermore, such co-operative structures of firms seem to allow for the exploitation of competitive advantages in the management of the process of technological change (Colombo, 1989) by solving the following problems: (a) the growth and indivisibility of R&D costs; (b) the increase in risks linked to the innovative processes; (c) the rapid redundancy of existing knowledge and production techniques; (d) the increasing complexity of the products/processes; (e) the birth of new ‘hybrid’ sciences due to the increasing interaction between technologies, which were once separate and distinct entities;13 (f) the importance of dynamic economies of learning; (g) the dispersion of the sources of technical progress. The network is naturally designed for dealing with transition periods because of the following features: (a) the ability to involve a large number of subjects (whilst maintaining their autonomy); (b) the flexibility of the structure and its evolutionary capability; (c) the possibility of diversifying the risk of innovation; (d) the emphasis on the strategic role played by languages in inter-agent relationships. However, there are a number of drawbacks when using this model; these being: (a) the univocal stress on structural features arising from neglect of the spatial factor; (b) the exclusive focus on the technical and quantitative aspects of the relationships (such as the number of linked subjects and the quantity of information exchanged) at the expense of the quality of such relationships. This process may degenerate into an ‘optimization syndrome’, when the efficiency of the network is separated from the effectiveness of communications.

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6 THE NECESSITY FOR AN INTEGRATION BETWEEN THE NETWORK AND THE ENVIRONMENT The arguments set out above have led me to formulate the following hypothesis, which is capable of blending and highlighting both the spatial element (i.e. the environment as a set of social relations) and the structural element (i.e. the network as an ordering criterion). The network organizes internal relationships according to dynamic criteria and establishes privileged relationships outside the local area. In this way the network and the environment can be described as two complementary and mutually enforcing models of innovative local dynamics. The simultaneous presence of the network and the local environment produces an integrated and open structure, able to understand and quickly respond to exogenous technological changes. The network drives the local environment along the leading technological trajectory whilst, at the same time, ensures the adoption of a new paradigm. The resulting structure, based on the coexistence of these two principles, could, however, run the risk of a genetic weakness given that: (a) the exclusive emphasis on the local environment leads to the suppression of the communications with the external environment and the death by entropy of the structure (caused by the birth of new but weak firms which exploit the advantages of the area without contributing anything new); (b) the hegemony of the network causes a progressive decline of the area. For the network exploits local resources (raw materials and skilled labour force) without pursuing a development strategy for the area. The response to an exogenous shock will be to disconnect a part of the network and shift the exploitation of resources to a more profitable area within the network. The relationship between the network and the environment should, therefore, be structured according to a systemic and holistic framework in which each component is essential to the functioning and the understanding of the whole model. 6.1 The neural model First of all a graphic representation of the interdependencies among the agents within a local economic system is given, following the inferences that can be drawn from the previous section. Figure 12.3 highlights the functions carried out by the local environment strictly connected to the external environment through a network structure of ‘privileged channels’.

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Figure 12.3 The ENN (economic neural network)

The single firm here can, therefore, enjoy external economies of localization without being totally constrained by the characteristics of the milieu. Further analysis reveals an analogy between the aforementioned structure and the morphology of the neural cell (Figure 12.4). In fact the neuron consists of the nucleus and of two separate sets of branches: a local one (the dendrites) and the external one (the axon), both with very sensitive points of contact (the synapses). The dendrites and the axon have complementary functions and are strictly necessary for the functioning of the cell. Therefore the neural model describes a holistic and systemic relationship between spatial (milieu) and structural (network) elements within an evolutionary framework. As this model provides a more realistic description of a single firm, it can be implemented in order to analyse the interactions among several firms. For this reason the analogy has been extended from the structure of a single neuron to the functioning of a complex neural network. To this end the recommendations, developed independently in two different fields of science (computer science and organizational theory), have been utilized. The possibility of transferring some

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Figure 12.4 The neural cell Source: Noback (1981, p. 52)

functions of the neural network into an economic framework will be examined in section 7, and the results will be applied to innovation policy. 6.2 Computer science Recently a substantive amount of research has been devoted to the realization of hardware and software systems modelled on the structure and physiology of the brain cell. Neurocomputers (NC) are information processing systems that are designed to emulate the organization and pattern recognition process of the human brain. NC recognize patterns and attempt to learn by example in a manner similar to human learning. They model the inter-connectedness of biological networks in which a stimulus gives rise to a complex spreading and feedback of neural excitation. Synaptic connections pass on signals from one neuron to another, creating a pattern that generates meaning. In contrast to conventional digital computers, NC appear to succeed at tasks, such as recognizing sounds and visual images, that involve highly complex patterns of data. (Nolan et al, 1989, p. 10) Neural networks (NN) are composed of many interconnected processing elements that operate in parallel.14 They work in a very similar way to how we think the neurons in the human brain encode information. Instead of programming a NN, you ‘teach’ it to give acceptable answers. You input known information, assign weighted values to the connections within the architecture, and run the network (which adjusts those weights by using several criteria) over and over until the output is satisfactorily accurate. A weighted matrix of interconnections allows NN to learn and remember. As a result of the way they work, even when you enter new information that is not stored in the network, they can still provide adequate responses…[NN are the best devices] to resolve issues that do not have straightforward

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Figure 12.5 Hierarchy vs. network in the nine agents’ case Source: Based on Nolan et al., (1988, p. 7)

black-and-white, yes-or-no answers. When they work correctly, NN provide some major benefits, such as the ability to take fuzzy, incomplete, chaotic data and produce approximate results. (Obermeier and Barron, 1989, pp. 217–18) Thus the neural network is a system capable of learning and correcting its own mistakes. It is formed by a large number of parallel processors that are capable of producing valuable results even from incomplete or chaotic data. As the field of innovation is comparable in its complexity and structure to the issues in computer science, in which NCs are used, the neural model should be able to cope with technological dynamics.

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6.3 Organizational theory In organizational theory neural models have recently been used to analyse the efficiency and effectiveness of the management of relationships. In order to illustrate the application of neural models to organizational theory, an example taken from Nolan et al., (1988) will be used. In this example two extreme organizational structures are considered: hierarchy and pure network (Figure 12.5). In the first structure each individual has only one direct superior but every superior can control several subordinates; in the second structure any node has direct communication with all the others. The most significant difference between these two structures regards the number of direct relationships that, in the case of a nine-nodes hierarchy is aaa In a hierarchical organization different levels of hierarchy communicate mainly through a single line (chain of commands). As the number of nodes increases, the accuracy of the information transmitted decreases. A high risk of distortion exists in an organization where a message must pass several stages before arriving at its final destination.15 The same problem does not occur in the network. However, an increase in the number of nodes corresponds to an exponential increase in the number of communication channels, causing high costs and chaos. These problems can be solved by superimposing a network structure on to a hierarchical organization as an intermediate stage in the progression towards a neural structure (see Figure 12.6). In this way it is possible to remove some of the rigidities and inefficiencies associated with the hierarchical organization, whilst implementing simultaneously the ordering criterion of the network structure. The resulting system reacts intelligently to different situations and learns from past experiences and previous mistakes. In an electronic model (Baum, 1988) this outcome is obtained by an intermediate level of ‘neurons’ linked according to ‘winner-takes-all’ circuits.16 This last point highlights the overlapping fields of interest that exist between computer science and organizational theory. A similar process of evolution for both firms and computers has taken place since the 1960s: from uniqueness to plurality, from large to small size, from hierarchy to co-ordination, from rigidity to flexibility and from quantity to quality. In less than thirty years the transition from multinationals to strategic alliances, and from mainframes to PC-networks and NCs has been completed.

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7 ECONOMIC NEURAL NETWORK (ENN) IN AN EVOLUTIONARY FRAMEWORK What is left to be explained is how the specific features of a biologic structure like the neural network can be used as an analytical tool in understanding small firms’ innovative behaviour and strategies. Let us summarize these features as follows: plurality of very simple interconnected units, high degree of parallel processing, fast communication, self-organizing learning procedures, fault tolerance. An ENN can, therefore, be similarly defined as follows: • a plurality of small firms (simple units of decision and action) involved in very similar and interconnected activities; • a high degree of parallel procedures (successful behaviour of innovative firms is rapidly replicated within the network by ‘swarms’ of imitators); • a high speed of parallel procedures and reaction to external stimuli; • a self-organized learning procedure based on the ex post observation of the gap existing between the desired and the actual performances. (The correction procedure works by adjusting the ‘relative weights’ of the network, i.e. by modifying the degree of influence, alliance, collaboration and hierarchy existing among the nodes.); • a high degree of fault tolerance. Because of its structure, an ENN can survive both large exogenous (i.e. sharp changes in technological and market conditions) and endogenous (i.e. single-firm bankruptcy) shocks. The evolutionary approach highlights some specific characteristics of ENN that cannot be incorporated in a standard neoclassical framework, these being: 1 Path dependence: history matters and shapes the present set of options available to the economic agents. The actual state of an ENN is strictly dependent on its previous structure and on the outcome of the last decision process performed. 2 Non ergodicity: small events are not averaged away but they may determine (by lock-in mechanisms) the final outcome of a process.The specialization of an ENN in a specific production may start ‘by chance’, but it is soon selfsustained by an increasing number of complex links of technological and productive interdependencies. 3 Self-organization: the resulting order of an evolutionary process is an unintended outcome of the interactions of different factors, usually a selection mechanism applied to an heterogeneous population. If the outcome of the decision routines adopted by the agents within the ENN is far from the desired one (does not satisfy the profitability requirements), new relative weights on the connections are set out.

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Figure 12.6 The evolution of organizations

4 Bounded rationality: the single firm has a limited capacity of collecting and processing information. Its behaviour can, therefore, be described in terms

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of decisions routines and local process of search.Two main consequences arise when dealing with ENNs. First, there is a causal relationships between diversity and interdependence of the nodes in a network (it is impossible to conceive an ENN made of a plurality of Marshallian representative agents). The second consequence is the trade-off between static and dynamic efficiency (to be dynamically efficient in a continuously changing environment an ENN must be redundant and therefore statically inefficient). 8 PUBLIC POLICIES FOR ENNS Relevant effects arise when applying this model to the management of local systems of innovations both for private and public agents. In such a perspective, the concept of economic policy in itself is modified. Public authorities are thus responsible only for the project and the design of the network’s architecture (i.e. the general structure of relations and communications among the agents within the local system of innovation). ‘Structural’ or ‘real’ interventions for the whole area are preferred to financial interventions in favour of the single agent. In particular there are three specific targets of such a policy: the entry (admission) and exit (exclusion) mechanism, the selective inner process of innovators, and the dynamics of learning and knowledge spreading within the network (Bianchi and Bellini, 1991). From these principles at least two guideposts for public decision-makers can be stated. Public interventions should help the ENN in maintaining a correct balance between two opposite behaviours: the complete separation from the outer environment and the total lack of entry rule. Two risks are in fact likely to occur: self-exclusion from market and technological dynamics or exposure to an increasing number of free-riders that (by exploiting the transaction cost advantages of the network) destroy the reciprocal trust of the agents. Public policy should establish (through the creation of ‘collective agents’) an environment favourable to innovative behaviour. In such an environment basic technical knowledge, specialized skills, labour division routines and incremental innovations should be managed as ‘club goods’. 9 SCIENCE PARKS AS ENNS Over recent years, continuous increases in the number of science parks (SPs) have been observed without any corresponding progress in the theoretical analysis of their existence. Previous works (Maggioni, 1989, 1990) illustrate that some features of SPs can be legitimately interpreted as an attempt to provide a network structure to the

250 FIRMS, UNCERTAINTY AND INNOVATION POLICY

interactions between heterogeneous agents in a local area. SPs could therefore provide a means of applying the ENN model to innovation policy. The official definition of SP taken from the introduction of the IASP (International Science Park Association) Directory supports this hypothesis: A science park is a property based initiative which has formal and operational links with a University or other Higher Educational Institution or major Centre of Research; is designed to encourage the formation and growth of knowledge-based business and other organizations normally resident on-site; has a management function which is actively engaged in the tranfer of technology and business skill to the organizations on-site. (IASP, 1988) Following the previous definition, the main features of a SP arise from the interdependence between a network structure (links and transfer) and a spatialfactor (property based) under the management supervision. The concept of the network has been closely linked to the existence of SPs from the very beginning. The first British and American experiences of SPs were centred on a so-called liaison office, aimed at creating, developing and supporting delicate relationships between different agents within the park (universities, firms, public authorities). This function was later Table 12.1 Actors, activities and resources of a science park Actors

University

Firm

Public authorities

Activities

Basic research Training Consulting

Applied research Development Specific services Technical and organizational consulting On-job training Development laboratories Managerial capital Technological knowhow Knowledge of the market

Local economic and social development Infrastructures (roads, buildings, transportation communication)

Resources Scientific equipment Laboratories Human and intellectual capital

Financial resources Laws Fiscal policy Public investments

Source: Maggioni (1989, p. 323)

formalized so that the SP model is often defined as a triangular network among universities/research centres, public authorities and firms. By utilizing the scheme proposed by Håkansson it is possible to highlight the different and complementary roles played by actors, activities and resources within an SP (see Table 12.1). In such a framework the success of any policy depends

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solely on the willingness of the agent to co-operate. As shown by the well-known prisoners’ dilemma, often co-operation yields better results. The problem is, therefore, the enforcement of this co-operation through a system of incentives and rules. The spatial point of view can be introduced here by considering the SP as an intermediate environment between the firm and the outer ‘economic space’, able to facilitate the management of innovation through the following functions: 1 Search: SP is designed to support scientific and technological research by fostering informal exchange of information, co-operative agreement and sharing of basic equipment. 2 Screening of external inputs occurs via stable informal relationships between researchers within the SP. At the same time a quality signalling activity is ensured by a preventive selection of members. 3 Transcoding: SPs ensure the advantages of internal mobility of highly skilled personnel and provide an update of technological and marketing know-how through privileged external connections. 4 Selection of routines, managerial styles and complementary production processes is made easier in an SP by the location in a restricted area of a large number of new high-tech firms, whose management often shares similar academic curricula. 5 Control function is two-sided: on the one hand it avoids the entry of firms which do not share the aim of the SP; on the other hand it ensures the involvement of all the agents who are relevant to the innovation process, admitting them as founder-members. Therefore the SP is structured as a synergie combination of the network and the local environment able to exploit the advantages of these two structures while avoiding their drawbacks. By comparing Figures 12.3 and 12.7 the analogy between ENN and SP receives further support. 10 CONCLUSION Holland shows that some characteristics of the global economy cannot be explained within an orthodox economic framework and therefore suggests the following provocative interpretation: the global economy is an example, par excellence, of an adaptive non linear network (=ANN). Other ANNs are the central nervous system, ecologies, immune systems, the developmental stages of multi-celled organism and the processes of evolutionary genetics. ANNs provide for a substantial extension of traditional economics. The building blocks of

252 FIRMS, UNCERTAINTY AND INNOVATION POLICY

Figure 12.7 The science park Source: Baldini (1984, p. 35)

traditional economics are fixed rational agents that operate in a static, statistically predictable environment. In contrast ANNs allow for intensive non linear interactions among large numbers of changing agents. These

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interactions are characterized by limited rationality, adaptation (learning) and increasing returns. (Holland, 1988, p. 118) The ENN model has been developed from the empirical observation of the main strategies (network and environment) chosen by firms when dealing with innovation. The analogy of this model with a biological structure (the neuron) and its evolutionary features have been highlighted throughout the chapter. The ENN shows bounded rationality, self-organizing learning processes and agglomeration economies, all features as required in Holland’s previous quotation. Further theoretical research should be devoted to analytical refinements and a proper formalization of the ENN model, whilst future empirical studies should concentrate on creating a coherent set of comparable data regarding SPs all around the world. NOTES 1 Some exceptions being Arthur (1986) and Momigliano (1986). 2 For an exhaustive exposition see Dasgupta and Stoneman (1987). 3 ‘The previous discussion leads to the conclusion that for optimal allocation to invention it would be necessary for the government or some other agency not governed by profit-and-loss criteria to finance research and invention’ (Arrow, 1962, p. 621). 4 Such as the ecological, social and institutional consequences of the Industrial Revolution. 5 See some criticisms by Demsetz (1969) on Arrow (1962). 6 Håkan Håkansson (1987, p. 3) ironically defines this conception of the process for the advancement of knowledge and of technological change, with the term ‘Newton syndrome’, referring to the traditional and legendary version of the discovery of the law of gravity by the English scientist. 7 Roulette involves risk, whilst horse-race betting represents a situation of uncertainty. 8 According to the original definition of the Gremi approach (see Aydalot, 1986; Aydalot and Keeble, 1988). 9 ‘Where large masses of people are working at the same kind of trade they educate one another. The skill and the taste required for their work are in the air and children breathe them as they grow up’ Marshall (1881, p. 53). 10 See the following table:

254 FIRMS, UNCERTAINTY AND INNOVATION POLICY

Inform ation costs Coordinat ion costs

Market

Quasimarket

Hierar chy

+−+

Quasihierarc hy −+−

+++ −−−

−+−

+−+

+++

−−−

Source: Blanchi (1989, p. 51) 11 ‘Sheffield and Solingen have acquired industrial atmosphere of their own; which yields gratis to the manufacturers of cutlery great advantages, that are not easily to be had elsewhere: and an atmosphere cannot be moved’ (Marshall, 1923, p. 284). 12 Compare the concept of ‘homeostasis’ as defined in the biological field by Von Bertalanffy (1968). 13 Consider, for example, data network and electronic mail as products of the interaction between computer science and telecommunication technology. 14 ‘Parallel processing computers architectures come closer to the way the human brain works than do the computers commonly in use today. In the evolution of computer application and languages, FORTRAN was designed to apply the computer to the analytical task; later, COBOL was designed to apply the computer to file oriented processing task. Parallel processing is a programming approach in which the problem to be solved is broken into constituent parts that are then solved simultaneously, or in parallel, before being recombined to produce the desired answer’ (Nolan et al., 1989, p. 12). 15 Assuming that the accuracy of the interpersonal transmission of a complex message is 70 per cent, after six passages only (0.70)6=0.1176 of the original message will reach its original destination. This process is generally referred to as ‘filtering problem’. 16 ‘The winner-takes-all circuit is an important concept in neural nets, both artificial and biological. If a set of neurons are connected into a winner-takes-all circuit, the one with the greatest external input turns on and suppresses all the rest’ (Baum, 1988, p. 39).

BIBLIOGRAPHY Antonelli, G. (ed.) (1984), Innovazioni Tecnologiche e Struttura Produttiva: la Posizione dell’Italia, Bologna, Il Mulino. Anderson, P.H., Arrow, K.J. and Pines, D. (eds) (1988), The Economy as an Evolving Complex System, London, Addison-Wesley. Arrow, K.J. (1962), ‘Economic Welfare and the Allocation of Resources for Invention’, in NBER, The Rate and Direction of Inventive Activity, Princeton, Princeton University Press, pp. 609–625.

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Arthur, W.B. (1986), Industry Location and the Importance of History, CEPR Paper 84, Stanford University. Aydalot, P. (1986), Milieux innovateurs Europe, Paris, Gremi. Aydalot, P. and Keeble, D. (1988), High Technology Industry and Innovative Environments. The European Experience, London, Routledge. Bagnasco, A. (1977), Tre Italie: la problematica territoriale dello sviluppo italiano, Bologna, Il Mulino. Baldini, V. (1984), ‘Parchi scientifici per 1’innovazione’, L’lmpresa, No. 1, pp. 29– 38. Baum, E.B. (1988), ‘Neural Nets for Economists’, in P.Anderson, K.J.Arrow and D.Pines (eds), The Economy as an Evolving Complex System, London, Addison-Wesley, pp. 33–48. Beccattini, G. (1987), Mercato e forze locali: il distretto industriale, Bologna, Il Mulino. Beccattini, G. (1989), Modelli locali di sviluppo, Bologna, Il Mulino. Bianchi, P. (1989), ‘Concorrenza dinamica, distretti industriali e interventi locali’, in F.Gobbo (ed.) Distretti e sistemi produttivi alla soglia degli anni ’90, Milano, CNR— Franco Angeli, pp. 47–60. Bianchi, P. and Bellini, N. (1991), ‘Public Policies for Local Network of Innovators’, Research Policy, No. 5, pp. 487–498. Brusco, S. and Sabel, C. (1981), ‘Artisan Production and Economic Growth’, in F. Wilkinson (ed.), The Dynamics of Labour Market Segmentation, London, London Academic Press, pp. 34–42. Camagni, R.P. (1989), ‘Space, Networks and Technical Change: An Evolutionary Approach’, in GREMI—Table Ronde Internationale, Barcelona, 28–29 March, ‘Milieux Innovateurs et Reseaux Transnationaux. Vers une Nouvelle Théorie du Developpement Spatial’, Mimeo. Capitani, G. (1983), ‘I fattori territoriali nello sviluppo della piccola e media impresa nelle aree periferiche: una definizione del quadro concettuale’, in AA. VV., I fattori territoriali nello sviluppo delle piccole e medie imprese, Milano, IRER—Franco Angeli. Coase, R.H. (1937), ‘The Nature of the Firm’, Economica, Vol. 4, pp. 386–405. Colombo, M.G. (1989), ‘Accordi fra imprese: un quadro concettuale di riferimento’, in AA. VV., Il settore dell’automazione industriale in lombardia. Strategie di cooperazione fra imprese produttrici, Milano, IRER—Franco Angeli, pp. 51–75. Dasgupta, P. and Stiglitz, J. (1980a), ‘Industrial Structure and the Nature of Innovative Activity’, The Economic Journal, Vol. 90, pp. 266–293. Dasgupta, P. and Stiglitz, J. (1980b), ‘Uncertainty: Industrial Structure and the Speed of R&D’, Bell Journal of Economics, Vol. 11, pp. 1–28. Dasgupta, P. and Stoneman, P. (1987), Economic Policy and Technological Performance, Oxford, Oxford University Press. Demsetz, H. (1969), ‘Information and Efficiency: Another Viewpoint’, Journal of Law and Economics, No. 12, pp. 1–22. Di Bernardo, B.Rullani, E. and Vaccà, S. (1986), ‘Cambiamento Tecnologico ed Economia di Impresa’, Economia e Politica Industriale, Vol. XIII, No. 50, pp. 79–124. Dosi, G. (1982), ‘Technological Paradigms and Technological Trajectories: A Suggested Interpretation of the Determinants and Directions of Technical Change’, Research Policy, Vol. 11, pp. 147–162.

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Dosi, G. (1988), ‘Sources, Procedures and Microeconomic Effects of Innovation’, Journal of Economic Literature, Vol. XXVI, pp. 1120–1171. Dosi, G. et al., (1988), Technical Change and Economic Theory, London, Frances Pinter. Farhat, N.H. (1989), ‘Optoelectronic Neural Networks and Learning Machines’, Circuits and Devices Magazine, pp. 32–41. Freeman, C. (1982), The Economics of Innovation, London, Frances Pinter. Freeman, C. and Perez, C. (1988), ‘Structural Crises of Adjustment: Business Cycles and Investment Behaviour’, in G. Dosi et al, Technical Change and Economic Theory, London, Frances Pinter. Georgescu-Roegen, N. (1973), Analisi Economica e Processo Economico, Firenze, Sansoni. Gibb, J.M. (1985), Science Parks and Innovation Centres: Their Economic and Social Impact, Proceedings of the conference on ‘Science Parks and Innovation Centres’ , Berlin, 13–15 February, 1985. GREMI (1989), ‘Milieux Innovateurs et Reseaux Transnationaux. Vers une nouvelle Théorie du Developpement Spatial’, Table ronde internationale, Barcelona, 28–29 March, Mimeo. Håkansson, H. (1987), Industrial Technological Development. A Network Approach, London, Croom Helm. Håkansson, H. (1989), Corporate Technological Behaviour. Co-operation and Networks, London, Routledge. Holland, J. (1988), ‘The Global Economy as an Adaptive Process’, in P.Anderson, K.J.Arrow and D.Pines (eds), The Economy as an Evolving Complex System, London, Addison-Wesley, pp. 95–104. Hammond, P.J. (1988), ‘Uncertainty’, in J.Eatwell, M.Milgate and P.Newman, The New Palgrave: A Dictionary of Economics, London, Macmillan. IASP (1988), International Association of Science Parks. European Directory of Science Parks and Incubators, Lyon. Kay, N. (1988), ‘The R&D Function: Corporate Strategy and Structure’, in G.Dosi et al., Technical Change and Economic Theory, London, Frances Pinter. Knight, F.H. (1921), Risk, Uncertainty and Profit, Chicago, University of Chicago Press. Lovitch, L. and Rosati, F. (1979), Fisica Generale II, Milano, Casa Editrice Ambrosiana. McClelland, J.L. and Rumelhart, D.E. (1986), Parallel Distributed Processing. Explorations in the Microstructure of Cognition, Vols 1,2,3, Cambridge Mass., MIT Press. Maggioni, M.A. (1989), ‘La dimensione territoriale delle politiche per l’innovazione: lo science park e le trasformazioni della città’, in AISRE, Atti della X Conferenza di Scienze Regionali, Rome 27–30 December, pp. 299–323. Maggioni, M.A. (1990), ‘Sostegno reale all’innovazione: lo strumento degli “science parks”. Un confronto internazionale per 1’analisi della realtà italiana’, Dynamis— Quaderni, IDSE, No. 10/90. Mariotti, S. and Migliarese, P. (1984), ‘Organizzazione industriale e rapporti fra imprese in un settore ad elevato tasso innovative’, L’lndustria, Vol. V, No. 1, pp. 71–110. Mariti, P. and Smiley, R.H. (1982), ‘Accordi di collaborazione fra imprese in una economia industriale moderna’, Economia e Politica Industriale, No. 33, anno IX, pp. 79–108. Marshall, A. (1881), The Economics of Industry, London, Macmillan. Marshall, A. (1923), Industry and Trade, London, Macmillan. Marshall, A. (1949), Principles of Economics, London, Macmillan.

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Momigliano, F. (1986), ‘Revisione di modelli interpretativi delle determinanti ed effetti dell’attività innovativa, della aggregazione spaziale dei centri di ReS e della diffusione intra-industriale e territoriale delle innovazioni tecnologiche’, in R.Camagni, R.Cappellin, and G.Garofoli (a cura di), Cambiamento Tecnologico e Diffusione Territoriale. Scenari Regionali di Risposta alla Crisi, Milano, AISRE— Franco Angeli, pp. 19–57. Momigliano, F. and Balcet, G. (1982), ‘Nuove forme di investimento internazionale e teoria del coinvolgimento estero dell’impresa’, Economia e Politica Industrial, Vol. IX, No. 36, pp. 35–68. Nelson, R.R. (1987), Understanding Technical Change as an Evolutionary Process, Amsterdam, Elsevier. Nelson, R.R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Cambridge, Mass., Harvard University Press. Noback, C.R. (1981), The Human Nervous System, New York, McGraw-Hill. Nolan, R.L. Pollock, A.J. and Ware, J.P. (1988), ‘Creating the 21st Century Organization, Stage by Stage, Vol. 8, No. 4, pp. 1–11. Nolan, R.L. Pollock, A.J. and Ware, J.P. (1989), ‘Toward the Design of Network Organizations’, Stage by Stage, Vol. 9, No. 1, pp. 1–12. Obermeier, K.K. and Barren, J.J. (1989), ‘Neural Networks: Time to get Fired Up’, Byte, Vol. 14, No. 8, pp. 217–224. Pacini, M. (1986), ‘Introduzione’, in C.Antonelli, L’attività innovativa in un distretto tecnologico, Torino, Edizioni della Fondazione Agnelli. Phillips, A. (1980), ‘Organizational Factors in R&D and Technological Change: Market Failure Considerations’, in D.Sahal, Research, Development and Technological Innovation, Lexington, Mass., Lexington Books, pp. 105–125. Ratti, R., Sen, L. and Bramanti, A. (1989), ‘PME, Synergies locales et cycles spatiaux d’innovation’, in GREMI-Table ronde internationale, Barcelona, 28–29 March , ‘Milieux Innovateurs et Reseaux Transnationaux. Vers une Nouvelle Théorie du Developpement Spatial’, Mimeo. Reisman, D. (1986), The Economics of Alfred Marshall, London, Macmillan. Rosenberg, N. (1976), Perspectives on Technology, Cambridge, Cambridge University Press. Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, Cambridge, Cambridge University Press. Sahal, D. (1981), Patterns of Technological Innovation, London, Addison-Wesley. Teece, D.J. (1986), ‘Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy’, Research Policy, Vol. 15, pp. 89–106. Vaccà, S. (1986), ‘L’economia delle relazioni tra imprese: dall’espansione dimensionale all sviluppo per reti esterne’, Economia e Politico Industriale, Vol. XIII, No. 51, pp. 3–42. Von Bertalanffy, L. (1968), General System Theory, New York, George Braziller. Williamson, O.E. (1975), Markets and Hierarchies: Analysis and Antitrust Implications, New York, Free Press. Williamson, O.E. (1989), ‘Transaction Cost Economics’, in R.Schmalensee and R. Willig (eds), Handbook of Industrial Organization, Amsterdam, Elsevier.

13 Patenting abroad and international technology transfer Antonio Sassu and Raffaele Paci1

1 INTRODUCTION In the last twenty years foreign technological activity has been continuously increasing in all developed western countries, though at different rates. In its various articulations—patenting, R&D, co-operative agreements—it represents perhaps the most distinctive element of the internationalization process that is typical of big multinationals’ modern strategy. In spite of the relevance of the phenomenon and the huge literature, our knowledge is very limited, especially on foreign patenting and R&D. This chapter will try to fill some gaps in this field of research, through an analysis of the characteristics and the determinants of foreign patenting activity. More precisely, the aim of the research is to inquire into three questions: 1 Which countries patent abroad? 2 What is the direction (countries and sectors) of patents flows? 3 Which factors affect the propensity to patent abroad? The problems raised are very complex, so we do not pretend to provide a conclusive solution. At this stage, our aim is to throw light on some evidence, reading into the statistical data on the most industrialized countries foreign patenting. The result, as we shall see, is threefold. First of all, evidence shows that foreign patenting activity is distributed among few countries. Only a small group of countries with a high technological capacity and a high market demand exchange and attract great technology flows. Second, foreign patenting depends mainly on market demand—as is shown even with an econometric analysis—but also on the technological capabilities and complementary assets of the host country. These are particularly important in some sectors which are increasingly becoming internationalized. Third, as a corollary of the first two results, a methodological comment shows some fallacies in using foreign patenting activity as an indicator of the technological capacity of the country of origin.

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The chapter is divided into five sections. After the introduction, in section 2 we briefly discuss some general theoretical remarks on foreign patenting activity. In section 3 the phenomenon of international patenting is examined in detail for six major industrialized countries. In section 4 we present an econometric investigation into the determinants of patenting abroad for the Italian industry. Finally, in section 5 a methodological comment on the use of foreign patenting is raised. 2 GENERAL ASPECTS ON FOREIGN PATENTING ACTIVITY Foreign patenting, as we said, is an important constituent of the internationalization process of firms’ technological activity. It seems appropriate to put forward some considerations on the factors determining this process, before getting into the analysis of data we have collected. To this aim we can remember that recent literature on multinational firms’ strategy has given relevant contributions to the knowledge of foreign technological activity, where foreign patenting is framed. In particular, two approaches are useful to explain the phenomenon that is our concern. We refer to the macroeconomic approach and to the technological accumulation approach. The macroeconomic approach presents several versions, but we shall deal mainly with Dunning’s (1988) and Ozawa’s (1985) contributions. According to Dunning it is possible to say that the level and the structure of the internationalization of firms’ technological activity depend on three factors: 1 the measure of the possessed ownership advantages; 2 the incentives to internalize market transactions; 3 the technological endowment of countries that are producers and hosts of technology. Let’s look at some details which explain this statement. Firms normally dispose of peculiar resources that are used as means of monopoly in order to increase their demand and to gain profits in domestic and foreign markets. We refer to tacit and uncodified knowledge, to marketing and trade facilities, besides patents and know-how. The exploitation of these resources, at a certain degree and in case of extension of market, is much more profitable if firms internalize some transactions than if they leave them to the market. A typical example, which has recently drawn scholars’ attention, is represented by direct foreign production which sometimes becomes more profitable than a strategy of exports. In this situation, advantages resulting from the use of those resources are greater when resources are run directly in ownership conditions, as happens in direct production. When internalization of market is not profitable, firms prefer the decentralization of technological

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activity, mainly patenting or licensing abroad, without their direct involvement— that is, without direct investment or production. The extent of the internationalization of the technological activity (in this case, of patenting activity), then, is determined by the other two factors: the extent of the ownership advantages (in other words, the size of foreign markets) and the endowment of resources, in particular the technological ones, of the host country. As technological resources endowment is essentially affected by the achieved level of economic growth, it is possible to maintain that international diffusion of patents depends, to a great extent, on the relative degree of growth of the country-producer and a host of technological activity. This sentence will be clearer when we have examined the contents of the second approach: the one on technological accumulation. This approach begins by pointing out that technical change is a cumulative (Rosenberg, 1982) and local (Stiglitz, 1987) phenomenon consisting of continuous improvements introduced in the existing technology, thanks to learning and new knowledge. Thus every firm has its own pattern of technical progress that is the outcome of the interdependence between its innovative activity and the feedbacks generating from the productive activity and the market. As their size grows, firms may find it profitable to transfer abroad some of their technological activities, so that in different environmental conditions they can try new adjustment processes and create a better innovative performance. At the first stage, technological activity to transfer can only be patenting, which represents a firm’s strategy to protect the market, or even licensing; at the second stage, mainly with production decentralization, it may consist of R&D activity, joint ventures, know-how and personnel exchange. Several reasons are put forward to explain this process. Innovative activity—it is maintained—is geographically differentiated because of the different technological environment (different opportunity, institutions and market conditions) that is peculiar to each economic system. As we have already noticed above, firms wishing to enter or to keep the markets may find it profitable to decentralize part of their technological activity, in order to have a better access to complementary patterns of technological change. In addition, they can decide to decentralize their activity, even adopting a strategy of technological diversification. In this way it is possible to say that, in order to increase their innovative capacity, firms can be forced to widen technological activity abroad, either patenting or investing in R&D (Cantwell and Hodson, 1990). Moreover, we have to recall that, although technological activity is in some way locationally differentiated, there are strong complementarities among national technological systems (partly because of technological trajectories). Therefore, the conclusion that is possible to draw is that, though nothing is automatic in patents flows, geographical dispersion of patenting and, in general, of technological activity, shows some regularities. According to the approach we are referring to, factors determining the extent and direction of patents flows are to be found in demand-side characteristics (the degree of product adaptation

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required by local markets) and supply-side characteristics of locations (Cantwell 1989). It is possible to understand, then, how firms, once they get to a certain size, have strong incentives to operate, starting from patenting, in those technological systems where the demand is high and the main competitors act. This is not very different from what Dunning says: that production and the diffusion of technological activity depend upon the level of growth achieved by countries. One country with a marked patent activity abroad and a decentralized R&D activity is one where big multinationals will be present. Patents and R&D flows in and out of a country are, at least in highly science-based sectors, strongly related. All these elements bring us to conclude that technological transfer abroad, in particular foreign patenting, depends mainly on demand conditions. Empirical results obtained by Dunning (1988), Cantwell and Hodson (1990), and Berlin and Wyatt (1988) bear out this opinion. In particular, Berlin and Wyatt hold that patents are the multinationals’ most important tool to achieve technological advanlages, now the main source of comparative advanlages. Elsewhere we reached the same conclusion, pointing out the reasons of a firm’s strategy lo patent abroad (Sassu and Paci, 1990). Both approaches, however, enphasize the importance of the factors relating to supply conditions as affecting the extent and the direction of the international diffusion of patents flows. With these considerations, we can move to examine, first, where patents flow is directed and the factors that are conducive to a particular direction; second, the processes that determine the selection of countries and sectors in foreign patenting activity; third, what factors determine the propensity to patent abroad. We shall investigate these problems in the following two sections. 3 THE PATENTING ACTIVITY ABROAD It is useful to start our analysis by making some preliminary remarks on patenting activity in the world and on the data we are going lo use. The first point to be stressed is that patenting activity is concentrated in a few industrialized countries.2 If we consider the number of domestic patents as an indicator of the ‘production of technology’ in one country, it turns out that patents granted by the six major industrialized countries (the United Slates, Japan, Germany, France, Great Britain and Italy) represent more than 90 per cent of the creation of technology in the OECD countries.3 A high concenlralion within few countries also characterizes the dissemination of technology abroad: OECD countries represent 95 per cent of world external patents and, among these, the six major counlries cover more than 80 per cent of the external patenting; in other words eight out of ten patents internationally transferred come from the six major countries. Within this group, the United Slates holds the leadership as producer of international technology flow (29.8 per cent of OECD

262 PATENTING ABROAD

external patenting) followed by Germany (18.7 per cent), Japan (14.8 per cent), Great Britain (7.5 per cent), France (7.3 per cent) and Italy (3.3 per cent).4 The distribution of the international technology flows appears to be less concentrated if we look at the country of destination of external patenting: it shows that only 48 per cent of OECD external patenting is directed towards the six major countries. The most relevant destination market is represented by the United States (10.8 per cent) followed by Great Britain (9.2 per cent), Germany (8.2 per cent), France (8.2 per cent), Japan (5.9 per cent) and Italy (5.6 per cent). To sum up the above general observations, it can be argued that, while production and dissemination of technology are highly concentrated in a few industrialized countries, there is a growing role of small and medium-sized countries as receivers of innovations. The statistical data on the technology flows we analyse in detail refer to the six major industrialized countries. We can thus say we have a good representation of the whole international distribution of technology we want to study. To this end we make use of the database on international patenting activity we have organized which collects data from the International Patent Documentation Center (INPADOC) of Vienna.5 Our statistical series refer to patents granted arranged by application years. The use of patents granted requires a lot of care in making international comparisons due to the considerable differences in granting procedures among countries.6 However, we have preferred this kind of series because several countries do not report the economic classification of patents application, in this way making a sectorial analysis impossible. As far as European countries are concerned our database also includes, from 1978 onwards, patents granted by the European Patent Office. The international flows of patents among the six major industrialized countries (see Table 13.1) confirm the general findings previously presented.7 It is worth pointing out four relevant results on international patenting that emerged over the period 1972–1984: 1 The six countries considered cover more than 80 per cent of total patenting in each publication country.8 2 The United States appears to be the greatest producer and disseminator of technology, followed by Germany. Italy appears to be the last one of the group. 3 Domestic patenting usually holds the majority share of national patenting; however, foreign patenting, especially coming from Germany and the United States, plays an increasing role. 4 The direction and strength of external patenting varies from country to country: i.e. Japan holds almost 40 per cent of foreign patenting in the the United States and only 6 per cent in Italy; Great Britain holds 14 per cent of foreign patenting in the United States and 7 per cent in Germany.

ANTONIO SASSU AND RAFFAELE PACI 263

The second step of our investigation is to analyse to what extent national economies are receiving a technology flow from foreign countries in the various industrial sectors. In Table 13.2 we have thus calculated the contribution of domestic patents to total patents for eighteen manufacturing Table 13.1 Patents granted by publication, and application countries, 1972–1984 Application countries Italy* Italy Germ any Franc e Great Britai n Japan

Publication countries Germany

France

Great Britain

United States

No.

%

No.

%

No.

%

No.

%

No.

%

45, 485 47, 558 19, 021 15, 564

20.5

4, 540 137, 659 18, 925 13, 000

1.4

8, 688 72, 376 107, 826 24, 707

2.3

9, 335 79, 804 29, 073 112, 986

2.0

8, 508 71, 109 25, 148 33, 769

1.0

10, 276 45, 542

21.4 8.6 7.0

4.6

41, 561 50, 802

42.3 5.8 4.0

12.8

35, 333 78, 607

18.9 28.2 6.5

9.2

52, 430 112, 108

17.5 6.4 24.8

11.5

95, 060 595, 689

8.5 3.0 4.0

11.3

Unite 20.5 15.6 20.5 24.6 70.9 d States Total 221, 100. 325, 100. 382, 100. 456, 100. 840, 100. patent 926 0 009 0 819 0 067 0 276 0 s grante d *1972–1981 Source: Based on International Patent Documentation Centre Note: The total figures also include patents granted to other applicants (for example, the Italy column adds up to 183,466 patents; that is, 82.7 per cent of the total) Table 13.2 Foreign patents as a percentage of total patents by manufacturing sectors, 1972–1974 and 1982–1984 Sectors

Publication countries

Italy

Germany

France

Great Britain United States

72–74

79–81 72–74 82–84 72–74 82–84 72–74 82–84 72–74 82–84

Extractio n of minerals Producti on and processi

82.9

96.2

18.3

25.6

73.1

82.9

53.3

61.8

57.1

50.8

88.5

90.6

52.1

71.9

76.8

84.5

73.5

88.8

31.3

43.8

264 PATENTING ABROAD

Sectors

Publication countries

Italy 72–74 ng of metal Manuf. of nonmetallic mineral products Chemica ls and manmade fibres Metal products Mechani cal engineeri ng Office and dataprocessi ng machiner y Electrica l and electroni c equipme nt Motor vehicles Instrume nts engineeri ng Food, drink and tobacco Textiles Leather

Germany

France

Great Britain United States

79–81 72–74 82–84 72–74 82–84 72–74 82–84 72–74 82–84

80.1

67.4

49.7

70.6

67.6

74.0

63.2

80.1

22.4

33.6

91.3

93.1

59.5

91.1

81.5

87.1

77.5

90.0

26.0

32.6

76.3

80.5

43.5

61.5

58.6

68.2

64.0

74.2

19.7

28.1

75.3

76.9

44.4

59.5

65.8

72.7

68.4

79.6

25.4

36.2

89.2

89.1

62.4

77.6

76.3

83.2

81.0

85.5

23.6

35.5

85.6

88.5

45.9

73.2

70.9

76.0

73.6

83.6

25.9

39.0

77.2

77.5

42.4

61.8

63.0

74.1

67.6

78.9

26.3

47.3

80.3

85.3

52.6

70.2

68.0

74.1

72.3

79.7

24.9

38.9

77.1

77.3

57.3

78.2

70.0

80.3

63.0

83.7

18.8

26.5

80.3 67.3

86.1 81.0

51.6 23.8

68.0 66.7

71.7 45.5

78.2 60.2

63.6 46.4

83.5 83.8

29.5 14.6

42.4 21.8

ANTONIO SASSU AND RAFFAELE PACI 265

Sectors Italy 72–74

Publication countries Germany

France

Great Britain United States

79–81 72–74 82–84 72–74 82–84 72–74 82–84 72–74 82–84

Clothing 64.0 67.2 52.1 74.7 59.5 68.8 68.7 and footwear Timber 49.8 64.2 24.7 50.7 48.3 57.4 55.6 and wooden furniture Paper 82.0 89.6 59.2 75.3 66.8 78.4 65.6 products, printing, publishin g Rubber 81.3 79.1 46.8 63.4 76.3 77.4 67.6 and plastics Other 62.9 65.7 48.0 62.2 43.5 50.4 54.8 manufact uring industrie s Total 80.2 83.2 48.4 70.3 68.9 75.8 70.8 Source: Based on International Patent Documentation Centre data

79.6

21.2

30.0

65.2

15.9

24.9

83.1

24.9

38.3

79.5

23.0

32.4

61.7

15.1

21.8

81.6

24.3

35.2

sectors. A further breakdown into the two periods 1972–1974 and 1982– 1984 sheds some light on the variations of domestic and foreign patenting over time. First, there is a considerable differentiation among industrial sectors in the relevance of domestic patenting. In general, traditional sectors (timber and wooden furnitures, metallic and non-metallic products, leather) show the highest coverage of domestic patenting; conversely, science-based and scale-intensive sectors (chemicals, processing of metals, office and data processing equipment) are characterized by a relatively higher presence of foreign patenting. Second, it can be noticed that there is a strong growth in foreign patenting that reflects the internationalization process of the economic activities.9 To understand the phenomenon better and to try to explain the points raised up to now we need a more detailed investigation into the pattern and the direction of international technology flows. We shall proceed by looking at the following concise index. Let’s define the propensity to patent PP of country k in the foreign country j, in sector i and time t as: (1) where P is the number of patents granted arranged by application year; needless to say, kjP is the number of patents granted to residents of country

266 PATENTING ABROAD

Table 13.3 Italy: propensity to patent abroad by manufacturing sectors, 1973–1984 (percentage values) Sectors

Foreign countries

Germany

France Great Britain United States

Extraction of minerals 0.0 14.3 Production and processing of metals 13.6 22.4 Manuf. of non-metallic mineral products 3.6 7.3 Chemicals and man-made fibres 25.7 50.6 Metal products 5.3 9.5 Mechanical engineering 8.8 15.8 Office and data-processing machinery 2.9 4.0 Electrical and electronic equipment 10.3 17.5 Motor vehicles 5.0 10.8 Instruments engineering 7.9 13.9 Food, drink and tobacco 7.5 9.1 Textiles 6.4 17.9 Leather 1.4 1.4 Clothing and footwear 3.9 9.2 Timber and wooden furniture 3.1 5.9 Paper products, printing, publishing 4.0 8.1 Rubber and plastics 7.4 19.1 Other manufacturing industries 1.8 3.8 Total 8.3 15.5 Source: Based on International Patent Documentation Centre data

21.0 23.2 4.9 51.9 11.7 19.9 5.0 22.4 16.8 8.6 17.5 7.2 8.6 4.7 8.4 22.8 3.9 3.9 17.6

14.3 19.1 4.1 45.5 10.1 18.2 5.7 17.5 7.8 17.8 8.3 9.4 4.3 15.0 4.8 9.6 21.8 5.4 15.9

k in country j, while kjP are patents granted to residents in their home country.10 Tables 13.3–7 represent the propensity to patent abroad of Italy, Germany, France, Great Britain and the United States for the period 1973–84 and for eighteen manufacturing industries. In evaluating the results it should be remembered that the index we have calculated is based on the ratio of patents granted by different countries and, therefore, its value is affected by differences in national patenting legislations. For example, Germany shows a higher propensity to patent abroad, since its national granting system is very severe and thus the value of the index denominator is relatively low. However, the index is still valuable because it allows sectorial comparisons within the same country, these not being affected by different legislations.11 The picture of international technology flows that results from the tables is very complex. As a first tentative classification we can say that the propensity to patent abroad varies in relation to three kinds of elements: sector-specific, country-specific and market-specific.

ANTONIO SASSU AND RAFFAELE PACI 267

A sector-specific pattern is followed by chemicals and, to a smaller extent, the processing of metals and rubber which both show a high Table 13.4 Germany: propensity to patent abroad by manufacturing sectors, 1973–1984 (percentage values) Sectors

Foreign countries

France

Great Britain United States Italy*

Extraction of minerals 21.9 30.8 Production and processing of metals 36.8 41.7 Manuf. of non-metallic mineral products 43.3 41.9 Chemicals and man-made fibres 125.1 139.1 Metal products 42.3 46.3 Mechanical engineering 43.3 55.1 Office and data-processing machinery 43.8 44.0 Electrical and electronic equipment 35.8 41.2 Motor vehicles 55.6 55.7 Instruments engineering 41.1 49.6 Food, drink and tobacco 44.6 46.1 Textiles 48.9 47.5 Leather 48.2 50.6 Clothing and footwear 41.5 45.8 Timber and wooden furniture 33.7 29.7 Paper products, printing, publishing 50.8 44.4 Rubber and plastics 54.1 60.1 Other manufacturing industries 26.1 28.4 Total 50.2 57.5 *1973–1981 Source: Based on International Patent Documentation Centre data

23.2 46.6 31.4 124.5 36.6 51.0 47.5 34.4 49.9 49.4 44.8 48.8 45.8 61.7 21.1 30.0 72.8 44.0 52.3

2.7 37.2 35.1 83.6 35.3 38.3 39.8 30.4 48.4 28.0 37.2 47.2 53.1 41.3 28.2 44.5 51.8 34.2 40.7

Table 13.5 France: propensity to patent abroad by manufacturing sectors, 1973–1984 (percentage values) Sectors

Foreign countries

Germany

Great Britain United States Italy*

Extraction of minerals Production and processing of metals Manuf. of non-metallic mineral products Chemicals and man-made fibres Metal products Mechanical engineering Office and data-processing machinery

20.6 24.7 20.8 24.0 14.0 15.1 18.9

37.4 28.6 25.8 39.4 20.8 23.8 29.9

25.2 28.6 18.3 35.2 14.2 21.4 38.3

8.1 25.3 24.3 25.3 17.5 20.6 19.4

268 PATENTING ABROAD

Sectors

Foreign countries

Germany

Great Britain United States Italy*

Electrical and electronic equipment 23.7 33.1 Motor vehicles 18.2 25.2 Instruments engineering 17.2 25.0 Food, drink and tobacco 17.8 22.3 Textiles 17.1 25.1 Leather 7.5 12.5 Clothing and footwear 9.6 13.6 Timber and wooden furniture 6.6 11.0 Paper products, printing, publishing 13.0 13.6 Rubber and plastics 16.7 30.2 Other manufacturing industries 6.9 6.3 Total 17.9 26.6 *1973–1981 Source: Based on International Patent Documentation Centre data

28.1 19.8 24.5 19.7 18.9 13.1 19.1 7.4 10.9 33.2 13.7 23.5

22.0 27.6 16.1 23.0 22.6 17.6 16.3 11.5 13.6 25.6 11.3 20.9

propensity to be patented abroad regardless of the country of origin and of destination. This fact is strictly related to the structural characteristics of the sectors: strong economies of scale in production and research, oligopolistic world-wide market structure, high degree of internationalization in production activities.12 In particular these sectors are characterized by a knowledge base produced, along a paradigm, in every country of the group by the same kind of scientific structure and facilities. As the knowledge in these sectors can be easily transferred, it is convenient for a firm to patent in countries where knowledge is similar in order to enjoy technological synergies. Opposing arguments hold for those sectors that always show a low propensity to patent abroad: metal products and timber and wooden furniture. In these sectors the knowledge growth is mainly tacit, local and specific and it can hardly be transferred. Moreover, they are sectors characterized by small firms with lower economies of scale. A country-specific behaviour means that the sectorial propensity to patent abroad varies crucially according to the country of origin and regardless of the country of destination of the technology flow. For example, the United States displays a high propensity to disseminate patents in textiles, office and dataprocessing equipment, paper products in all the Table 13.6 Great Britain: propensity to patent abroad by manufacturing sectors, 1973– 1984 (percentage values) Sectors

Foreign countries

Germany

France

United States

Italy*

Extraction of minerals

11.8

5.4

24.9

4.1

ANTONIO SASSU AND RAFFAELE PACI 269

Sectors

Foreign countries

Germany

France

United States

Production and processing of metals 15.3 22.3 Manuf. of non-metallic mineral products 15.5 26.5 Chemicals and man-made fibres 18.9 37.7 Metal products 7.1 12.2 Mechanical engineering 8.9 15.3 Office and data-processing machinery 10.6 16.2 Electrical and electronic equipment 11.5 17.6 Motor vehicles 11.9 25.9 Instruments engineering 10.5 15.8 Food, drink and tobacco 20.0 28.9 Textiles 13.8 26.8 Leather 3.0 3.0 Clothing and footwear 3.6 6.3 Timber and wooden furniture 4.4 7.7 Paper products, printing, publishing 14.9 23.0 Rubber and plastics 9.4 22.9 Other manufacturing industries 2.5 4.3 Total 11.2 19.8 *1973–1981 Source: Based on International Patent Documentation Centre data

Italy* 32.7 28.4 51.5 16.8 24.1 39.9 29.9 26.7 29.1 37.1 19.1 13.1 17.2 12.4 24.8 36.7 13.2 29.5

20.1 20.2 21.9 10.3 12.7 8.3 13.3 28.1 9.9 28.4 21.5 4.7 6.4 5.0 19.4 19.3 5.5 14.9

recipient countries considered; Germany in motor vehicles; Great Britain in food products; Italy in electrical products and mechanical engineering. In this case the international flows of patents are related, to a certain extent, to the market structure of the country of origin (i.e. presence of multinational firms) and to its specific technological patterns. By market-specific factors we mean the characteristics of the recipient country that affect the sectorial propensity to patent abroad. For instance, all countries show a relatively higher propensity to patent in Italy in the motor vehicles industry and, conversely, a relatively low propensity in office and dataprocessing equipment. In general, the explanation for the market-specific pattern can be found in the distinctive feature of the destination country: the dimension of the market with the consequent interest for a commercial penetration, the institutional environment, the presence of competitors with strong technological capability. This last element seems very important in affecting multinational firms’ choices on international activities localization. As Cantwell has pointed out: once they have achieved a sufficient level of technological strength in their own right, firms are particularly keen to produce in the areas from

270 PATENTING ABROAD

Table 13.7 United States: propensity to patent abroad by manufacturing sectors, 1973–1984 (percentages values) Sectors

Foreign countries

Germany

France

Great Britain

Italy*

Extraction of minerals Production and processing of metals Manuf. of nonmetallic mineral products Chemicals and manmade fibres Metal products Mechanica l engineerin g Office and dataprocessing machinery Electrical and electronic equipment Motor vehicles Instrument s engineerin g Food, drink and tobacco Textiles Leather

5.1

3.7

14.3

10.7

10.7

16.6

21.2

14.5

13.6

17.4

20.1

11.6

11.2

18.4

23.4

11.9

5.9

8.4

14.2

7.4

6.7

10.0

17.0

8.0

17.1

22.3

28.1

13.7

10.5

14.2

20.7

10.0

5.6

8.3

12.8

9.8

9.0

12.3

18.9

7.8

11.9

14.8

21.1

13.1

14.4 2.4

19.8 2.5

23.7 4.5

16.7 3.2

ANTONIO SASSU AND RAFFAELE PACI 271

Sectors

Foreign countries

Germany

France

Great Britain

Italy*

Clothing 4.0 6.7 11.2 and footwear Timber 2.2 3.1 6.7 and wooden furniture Paper 15.4 17.1 22.3 products, printing, publishing Rubber 6.1 9.9 13.9 and plastics Other 1.7 2.6 7.4 manufactu ring industries Total 8.6 12.6 18.5 *1973–1981 Source: Based on International Patent Documentation Centre data

5.9

2.3

17.5

8.4

3.4

9.2

which their major international rivals have emanated, which offer them access to alternative sources of complementary innovation. (Cantwell, 1989, p. 11) It is clear that the direction and the strength of international patenting are anyway the results of firms’ behaviour. Firms’ performances vary according to several factors, which makes the phenomenon so complex to explain. Let’s look, for instance, at the propensity to patent abroad of the major Italian multinationals. Two points clearly appear from Table 13.8 confirming our previous observations at the macro level. First, the international technology flows are increasing over time. Second, the propensity to patent abroad varies across the firms reflecting their interest in foreign markets. For example, Fiat and Alfa-Romeo, both producing in the motor vehicles sector, differ in their strategy of foreign patenting: Alfa Romeo is more specialized in exports and, thus, it shows a greater interest for protecting its products abroad. Finally, it is worth noting that even large multinationals use only a part of their technological capability abroad. This fact implies one important methodological consequence on foreign patenting use as an

272 PATENTING ABROAD

Table 13.8 Italy: propensity to pstent abroad of selected multinational firms 1973–1975 and 1980–1982 Firms

Principal industrial activity

Germany

73–75

80–82

73–75 80–85 73–75 80–82 73–75 80–82

Montediso n Oliveeti

Chemicals

13.8

38.3

France

53.6

Great Britain United States

67.5

49.7

Data23.1 50.0 35.1 63.0 59.2 processing mach. Fiat Motor 9.8 36.3 28.3 58.5 21.9 vehicles Pirelli Rubber 5.5 8.2 29.8 24.7 60.9 products AlfaMotor 25.0 28.5 16.6 80.9 25.0 Romeo vehicles Source: Based on International Patent Documentation Centre data

65.9

31.1

65.9

77.3

76.1

76.7

40.7

16.1

46.4

48.2

37.1

47.0

76.1

61.1

76.1

indicator of national technological strength. We shall come back to this subject in the last section of the chapter. It is difficult to sum up all the previous findings within a comprehensive interpretation. Our analysis has stressed the complexity of the phenomenon and it has suggested that the patenting activity abroad depends on several economic factors: a country’s technological specialization, the structure of international markets, the sectorial characteristics of production and technology, a firm’s interest for the foreign markets, the technological capabilities of the recipient country. Moreover, it should be remembered that international technology flows are also affected by cultural and geographical elements like national legislations, language, and geographical closeness. 4 AN ECONOMETRIC ANALYSIS OF THE DETERMINANTS OF ITALIAN PATENTING ABROAD In the previous section we have seen that there are inter-country and intersectorial differences in international patenting activity. It is time now to investigate the determinants of patenting abroad. We need first to stress that the organizational and financial costs of patenting abroad are very high, and even more relevant are costs for securing and maintaining effective protection. Thus, it is convenient for a firm to protect its technological assets abroad when it has positive prospective profits in its international activities. We have already said that a firm’s decision to patent abroad is related to several factors, including the nature of technology and the international competitive environment. Berlin and

ANTONIO SASSU AND RAFFAELE PACI 273

Wyatt (1988) come to the same conclusions; the empirical results of these two authors support the idea that a firm’s foreign patenting activity is related to the protection of innovation in countries where the firm is exporting, where the firm has production and research facilities or is considering establishing them, and where the firm is arranging licensing agreements.13 In this section, through an econometric analysis, we will try to obtain some insight into the determinants of the propensity of the Italian industry to patent abroad. The hypothesis to be tested is that foreign patenting is positively affected by international demand, foreign direct investment and licensing activities (Sassu and Paci, 1990). Unfortunately the statistical data on licensing and foreign direct investment for the Italian industry are not available with the geographical and sectorial breakdown we need. Therefore, at this stage of our research, we are forced to confine our analysis to the influence of Italian exports on the propensity to patent of Italian firms in some industrialized countries.14 As we mentioned, legal protection of innovation abroad requires a relevant amount of money and human resources, which is worth spending only if firms expect a high amount of revenue from the sales of their products on foreign markets. Therefore the propensity to patent in a country is, beside other things, directly related to the expected demand from that country. Within a simple hypothesis of extrapolative expectations, firms predict future demand on the basis of its past values. Since patenting activities (and, more generally, technological strategies) imply long-term decisions, we expect that a firm’s behaviour is affected by the ‘normal’ value of the foreign demand; therefore, in order to avoid conjunctural shocks, we have taken as a regressor in our equation the average value of the demand in the previous three years. Our estimated function is: (2) where ITjPPit is the propensity to patent of Italian industry in country j, sector i and time t−already defined in equation (1)−and ITjMXit−1 represents the average value in periods t−1, t−2, t−3 of Italian exports in country j and sector i. We have performed a time-series estimation over the period 1970–1982 for the major industrialized countries (j=France, Germany, Great Britain and United States) and for the four macro-sectors defined following Pavitt’s taxonomy (i=traditional, scale-intensive, specialized suppliers, science-based). Let’s start with the analysis of the aggregate estimates for the whole manufacturing industry reported in Table 13.9. First of all we have tested for the presence of serial correlation among residuals: looking at both the DurbinWatson test and the Lagrange Multiplier test, the null hypothesis of no autocorrelation is accepted and therefore the OLS estimations have been performed; in the case of France the presence of first-order autocorrelation has been detected in the OLS procedure and thus the AR (1) estimation has been preferred.15 As the second step we have defined the functional form of our

274 PATENTING ABROAD

relation on the basis of Ramsey’s RESET test:16 a logarithmic equation has been chosen for Germany, while for the other countries the linear function has been preferred. The explanatory power of the equation is quite high, the R2-bar ranging from 0.68 for France to 0.89 for Great Britain. The F-test for the significance of the equation shows satisfactory values for all countries. Turning to the analysis of the regression coefficients it appears that the coefficients of the explanatory variable have the positive signs and are statistically significant for all the four countries considered; thus the hypothesis of positive influence of expected demand on propensity to patent abroad is confirmed. The estimated function between exports and propensity to patent is inelastic in the case of France and Great Britain, while it is elastic for Germany and the United States (with a value around 1.5). As a tentative explanation for this finding it can be argued that in the last two countries the effects of demand changes on patenting activity are Table 13.9 Italian patenting activity in selected foreign countries as a function of demand: manufacturing industry (estimated function PP=a+b MX Countr Functi Estima a ies onal tion form method

b

R2bar

F test

Autoc LM Forrela test F: tion (1, 7)

Functio nal form test Reset Ftest F: (1, 7)

Germ any

1.556

0.76

29.9

1.13

1.28

0.77

0. 0014 (2.40)

0.68

F: (1, 8) 10.6

1.36

—

—

9.67

0. 0029

0.89

F: (2, 7) 71.1

2.04

0.17

0.68

10.65

(8.43)

−14. 92

0. 0088

0.76

F: (1, 8) 29.6

1.13

0.21

0.20

Franc e

Log*

Linea r

OLS

AR (1)†

−12. 09 (−4. 68) 5.77 (1.36)

Great Britia n

Unite d States

Linea r

Linea r

OLS

OLS

(5.48)

(−2. (5.44) F: (1, 66) 8) Estimation period: 1970–1982, no. obs.=10 (t-statistic in parentheses).

ANTONIO SASSU AND RAFFAELE PACI 275

Countr Functi Estima a ies onal tion form method

b

R2bar

F test

Autoc LM Forrela test F: tion (1, 7)

Functio nal form test Reset Ftest F: (1, 7) *Logarithmic form has been preferred on the basis of the Reset test. † OLS estimation has been rejected on the basis of the autocorrelation test. Legend variables: PP=Italian propensity to patent in the foreign country; MX=three years average value of Italian exports in the foreign country, 1980 billion of lire

higher because within these two markets, technologically more advanced, there is a stronger technological competition among firms. Let’s now turn our attention to the sectorial estimations. We have estimated equation (2) for the four industrial sectors defined on the basis of the taxonomy suggested by Pavitt (1984): i.e. traditional, scale-intensive, specialized suppliers, science-based. The results for each country are reported in Tables 13.10–13. The choice of the estimation method and the functional form has been based on the diagnostic tests previously described. Our hypothesis of a positive relationship between demand and patenting activity abroad is confirmed for all sectors and countries, except the traditional sectors in the United States and the science-based sectors in France. As expected, the explanatory power of the equation is higher in the scale-intensive and specialized suppliers sectors: the role of the patenting activity in protecting firms’ innovation is in fact more relevant in these economic activities. Our results have to be considered preliminary and tentative because of the under-specification of the model (i.e. the absence of licensing and foreign direct investment) and because of the limited number of observations available for the estimation. However, the good performance of the model for all the countries considered allows us to conclude that propensity to patent in a foreign country is positively affected, besides other factors, by the demand of the recipient country. 5 A METHODOLOGICAL COMMENT ON THE USE OF FOREIGN PATENTING The above results give us the opportunity to raise an interesting methodological point. We have to start from Pavitt and Soete’s paper (1980), where patents granted to foreign inventors are taken as a good indicator of the technological capacity of the country of origin. The aim of that paper was to analyse and explain the international differences of innovative activity. Then the question arises of which indicator to use. Domestic patents, in their opinion, cannot be taken as indicators of innovative activity, because patent propensity is strongly

276 PATENTING ABROAD

affected by institutional factors which differ among countries. Then, Pavitt and Soete suggest the comparison of patents granted to foreign inventors in a third country; in particular, the United States where the quality of patent activity is qualified. Let’s follow their reasoning. R&D expenditures are normally considered as an indicator of innovative activity whose output is represented by patents. Evidence shows a high correlation between R&D and patents, but correlation between R&D in one country and patents in the United States is higher than that obtained in the country of origin. As institutional conditions affecting patenting activity in the United States are the same for all countries, Pavitt and Soete conclude by saying ‘each country has the same propensity to patent in the Table 13.10 Italian patenting activity in Germany as function of demand: sectoral estimates (estimated fuction: PP=a+b MX) Autocorrelation tests

Function al form test

Sectors

a

b

R2-bar

F test F: (1, 8)

D-W test LM Ftest F: (1, 7)

Traditio nal

−13

1.712

0.56

12.20

1.47

0.00

0.85

(−3.23) −11.45

(3.50) 1.65

0.73

25.90

1.34

0.74

2.19

(−4.25) −6.77

(5.09) 1.287

0.70

21.60

2.10

0.89

0.38

(−3.67) −3.06

(4.64) 0.921

0.33

5.41

1.83

0.01

0.55

Scaleintensiv e Speciali zed supplier s Sciencebased

(−1.36) (2.33) Functional form: logarithmic. Estimation period, 1970–1982 (t-statistic in parentheses) Estimation method: OLS (no. obs.=10) Legend: Variable: PP=Italian propensity to patent in the foreign country at time t, see equation (1) in the text. MX=Italian exports in the foreign country; t−1, t−2, t−3 average value; 1980 billion lire. Sector: Traditional=leather, textiles, clothing, footwear, wood, furniture, ceramics. Scaleintensive=minerals, chemicals, metal pr., motor vehicles, electr. and electron. comp., food, paper, rubber. Specialized suppliers=mechanical engineering, railway vehicles, printing and publishing. Science-based=pharmaceuticals, data processing, aerospace, instruments.

ANTONIO SASSU AND RAFFAELE PACI 277

Table 13.11 Italian patenting activity in France as function of demand: sectoral estimates (estimated function: PP=a+b MX) Sectors

a

b

R2-bar

F test F: (2, 7)

D-W test

Traditional

–0.51 0.0033 0.72 11.60 2.35 (–0.46) (6.03) Scale-intensive 3.75 0.0042 0.77 16.22 1.63 (0.95) (3.91) Specialized suppliers 3.34 0.0082 0.48 5.16 1.50 (0.68) (1.77) Science-based 14.74 0.0113 0.08 1.37 1.58 (2.00) (0.48) Functional form: linear estimation period, 1970–1982 (t-statistic in parentheses). Estimation method: AR (1), no. obs=10. Legend: See Table 13.10.

United States in relation to the size of its innovative activities’ (1980, p. 41). In other words, foreign patenting in the United States is strictly dependent on the technological capacity (measured by R&D) of the country of origin and it is invariant with respect to other factors. This approach would allow students to provide an objective explanation of the international differences of innovative activity. A more extensive use of this approach appears in Soete and Wyatt (1983), where, once more, the conclusion is that foreign patenting in a third country is a good indicator of the technological effort and innovative activity pattern. Since then we can find many contributions following the same line of research, sometimes without the caveats suggested by the first authors. In this way the technological capacities and the sectoral innovative performances of Germany (Narin and Olivastro, 1987), Great Britain (Narin et al., 1987) and Italy (Malerba and Orsenigo, 1993; Archibugi, 1987) are analysed. In particular, for the last country, the Italian patents in the United States have been used even to detail the distribution of regional innovation (Dosi and Moggi, 1986; Santarelli and Sterlacchini, 1989) and to comment on Italian technological competitiveness in comparison with other countries (Dosi and Moggi, 1986). The evidence produced in our chapter has cast some doubts on the accuracy of this approach. Two objections can be raised. The first is that patents do not measure only R&D activity and, even less, the sectoral R&D. The second is that the propensity to patent abroad is strongly affected by several factors, and in particular demand factors. Therefore, the composition of foreign patenting activity in the United States (or in a third country) is not a good representation of the national innovative performance or capacity, even when this is measured by R&D expenditures.

278 PATENTING ABROAD

Table 13.12 Italian patenting activity in Great Britain as a function of demand: sectoral estimates (estimated function: PP=a+b MX) Sectors

a

b

D-W test

LM F-test Reset FF: (1, 7) test F: (1, 7)

R2-bar F test F: (1, 8)

Autocorrelation tests Function al form test

Tradition 1.28 0.002 0.59 13.83 2.39 0.35 0.05 al (0.88) (3.72) Scale12.53 0.0065 0.76 28.84 1.97 0.00 0.55 intensive (7.87) (5.37) Specializ 5.08 0.0219 0.87 63.89 1.93 1.82 0.56 ed suppliers (4.43) (7.99) Science 13.72 0.0347 0.35 5.76 2.61 2.01 0.28 based (6.52) (2.40) Functional form: logarithmic. Estimation period, 1970–1982 (t-statistic in parenthese) Estimation method: OLS (no. obs.=10) Legend: See Table 13.10. Table 13.13 Italian patenting activity in the United States as a function of demand: sectoral estimates (estimated function: PP=a+b MX) Sector

a

b

R2-bar F test F: (1, 8)

Autocorrelation test Functiona l form test F: (1, 7)

D-W test

LM F test F: (1, 7)

Tradition al (0.94) Scaleintensive (−0.86) Specializ ed suppliers (−0.75) Science based

11.97

−0.0037

−0.11

0.13

0.78

2.59

3.82

(−0.36) −3.02

0.0123

0.78

33.56

1.75

0.01

2.89

(5.797) −3.16

0.0444

0.61

15.01

1.81

0.02

0.56

(3.87) −0.6

0.1093

0.57

12.71

1.76

0.13

0.07

ANTONIO SASSU AND RAFFAELE PACI 279

Sector

a

D-W test

LM F test F: (1, 7)

(−0.09)

(3.56)

b

R2-bar F test F: (1, 8)

Autocorrelation test Functiona l form test F: (1, 7)

Functional form: logarithmic. Estimation period, 1970–1982 (t-statistic in parentheses) Estimation method: OLS (no. obs.=10) Legend: See Table 13.10.

NOTES 1 A further discussion of some of the arguments dealt with in this chapter can be found in Paci et al. (1996). 2 The following general description of the world-wide patenting activity is based on OECD (1989) and on the WIPO yearbook; it refers to patent applications in 1985. Total patents in a receiving country can be broken down into two groups: (a) domestic patents filed by a resident; (b) foreign patents filed by a non-resident. Moreover, we can define external patents as patents filed by the residents of a country outside its frontiers; thus this last group refers to the country of origin of the international patenting. 3 The other countries with a high patenting capacity within the OECD countries are Sweden, the Netherlands, Switzerland. 4 In sixth position, before Italy, we find Switzerland with 4.9 per cent. 5 A detailed explanation of the methodology we have used in building our database is reported is Sassu and Paci (1989). 6 A severe examination system holds in Germany and, to a lesser extent, in the United States; in France, Italy and Great Britain the granting procedure consists mainly of a formal examination. 7 Japan is not considered as a publication country since the available data start from 1976; moreover, Japan’s statistical series refer to patent applications and thus are not comparable with the other countries. 8 In the United States the six countries considered amount to 93 per cent. This high value depends on the presence of patents with multiple application countries; we have atributed these patents to each country named in the document; the duplication should amount to 4 per cent. 9 Our findings confirm the results of the OECD report where it is affirmed that ‘the international dissemination of patentable technical knowledge…has become the dominant growth factor in patent markets throughout OECD economies’ (OECD, 1989, p. 31). 10 We have introduced a one-year lag between patents abroad and patents in the home country since, according to international agreements, firms can extend their patents abroad within twelve months from the application date in the home country.

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11 In order to compare the sectorial propensity to patent abroad across various destination countries overcoming the legislation differences, we have based our comments on the sectoral ranking within each country. 12 On the sectoral patterns of internationalization of production see OECD (1987) 13 These last two activities are strictly related, within the industrialized countries, to the presence of technological capabilities in the recipient country. 14 The involvement strategies are not very relevant in the case of the Italian industry, therefore their exclusion from our equation becomes less limiting. 15 We have used the Exact Maximum Likelihood method, which has the characteristic of estimating the AR-error regression model with stochastic initial values. 16 It should be noticed that the theory does not suggest an a priori form for the relationship we are studying.

BIBLIOGRAPHY Archibugi, D. (1987), ‘La Struttura Settoriale dell’Innovazione Industriale in Italia’, Rivista di Politica Economica, Vol. 77, No. 2, pp. 3–48. Berlin, G.Y. and Wyatt, S. (1988), Multinationals and Industrial Property, Kernel Hempstead, Harvester-Wheatsheaf. Cantwell, J. (1989), Technological Innovation and Multinational Corporations, Oxford, Basil Blackwell. Cantwell, J. and Hodson, C. (1990), ‘The Internationalisation of Technological Activity and British Competitiveness: A Review of Some New Evidence’, Mimeo. Dosi, G. and Moggi, M. (1986), ‘La Capacità Innovativa Italiana: Alcuni Confronti Internazionali, Inter-settoriali e Inter-regionali’, in F.Onida (ed.) Vincolo Estero, Struttura Industriale e Credito all’Esportazione, Bologna, Il Mulino. Dunning, J. (1988), Multinationals, Technology and Competitiveness, London, Unwin Hyman. Malerba, F. and Orsenigo, L. (1993), ‘L’Accumulazione delle Capacità Tecnologiche nell’Industria Italiana’, in C.Filippini and F.Gobbo (eds), Innovazione Tecnologica e Servizi alle Imprese, Milano, Franco Angeli. Narin, F. and Olivastro, D. (1987), Identifying Areas of Strength and Excellence in F.R.G. Technology, Bonn, Bundesministerium für Forschung und Technologie. Narin, F., Olivastro, D. and Wright, R.R. (1987), Identifying Areas of Strength and Excellence in the U.K. Technology, Bonn, Bundesministerium für Farschung und Technologie. OECD (1987), International Investment and Multinational Enterprises, Paris, OECD. OECD (1989), R&D, Production and Diffusion of Technology, OECD Science and Technology Indicators, Report No. 3, Paris, OECD. Ozawa, T. (1985), ‘Macroeconomic Factors Affecting Japan’s Technology Inflows and Outflows: The Postwar Experience’, in N.Rosenberg and C.Frischtak, (eds), International Technology Transfer, New York, Praeger. Paci, R., Sassu, A. and Usai, S. (1997), ‘International Patenting and National Technological Specialisation’, Technovation, Vol. 17, No. 1, pp. 25–38. Pavitt, K. (1984), ‘Sectoral Patterns of Technical Change: Towards a Taxonomy and a Theory’, Research Policy, Vol. 13, No. 6, pp. 343–374.

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Pavitt, K. and Soete, L. (1980), ‘Innovative Activities and Export Shares: Some Comparisons between Industries and Countries’ in K.Pavitt (ed.) Technical Innovation and British Economic Performance, London, Macmillan. Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, Cambridge, Cambridge University Press. Santarelli, E. and Sterlacchini, A. (1989), ‘Patterns Regionali di Innovazione Tecnologica in Italia: R&S, Brevetti all’Estero, Imprese Innovative’ , L’lndus-tria, Vol. 10, No. 1, pp. 25–56. Sassu, A. and Paci, R. (1989), ‘Brevetti d’Invenzione e Cambiamento Tecnologico in Italia’, Rivista di Politica Economica, Vol. 79, No. 1. Sassu, A. and Paci, R. (1990), ‘La Propensione a Brevettare all’Estero delle Imprese Italiane’, L’lndustria, Vol. 11, No. 3, pp. 485–508. Soete, L. and Wyatt, S. (1983), ‘The Use of Foreign Patenting as an International Comparable Science and Technology Output Indicator’, Scientometrics, Vol. 5, No. 1. Stiglitz, J. (1987), ‘Learning to Learn, Localized Learning and Technological Progress’, in P.Dasgupta and P.Stoneman (eds): Economic Policy and Technological Performance, Cambridge, Cambridge University Press.

14 Science and technology indicators The state of the art and prospects for the future Giorgio Sirilli1

1 INTRODUCTION This chapter deals with the state of the art of science and technology indicators with particular reference to the most advanced countries. An attempt is also made to identify future prospects in the measurement of key dimensions of a society and an economy which is increasingly based on knowledge. The indicators under examination have been divided into two groups. The first group includes indicators for which a statistical methodology has been developed and data are collected and analysed according to a standardized methodology. They are: statistics on research and development (R&D), patent statistics, survey innovation, the technological balance of payments, the analysis of trade in high-technology products, bibliometrics, indicators of human resources. The second group includes indicators for which methodologies are still being developed and the indicators, where available, cannot be compared across countries and over time. They are: indicators based on information of technical journals, intangible investment, surveys of manufacturing technologies, indicators in the field of information and communication technologies, measurement of organizational change in enterprises, technology foresight, public attitudes and public understanding of science and technology. This chapter is divided into three sections: in the first section we deal with the nature of indicators, in the second we describe the main features of the various indicators analysed, while in the third we look at the prospects for the future. 2 SCIENCE AND TECHNOLOGY INDICATORS Science and technology has become a fully-fledged policy issue both in developed and in less developed countries: the large use of resources to this end

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is inevitably focusing increasing attention on the efficiency and effectiveness of the innovation systems. In terms of science policy at the national level, governments need a set of tools to assess the qualitative performance of scientific institutions (OECD, 1987). Over the last decade governments have also been keen to use innovation to foster economic performance and competitiveness, and therefore have increased their request for information on technical change (OECD, 1992). Managers in industry making decisions on technological activities are coming up against the need to assess the costs and benefits of technological and trading operations involving high risks and uncertain results. Administrators assigning public resources to research institutes or projects are constantly seeking tools to assess the ‘quality’ and potential of individual researchers, research groups and institutions, together with the social and economic ‘value’ of research findings and inventive or innovative activities in general. It is no easy task to assemble the theoretical tools and empirical data to meet this need. However, for some years now, researchers and organizations in all the industrialized countries have made efforts to develop a view of the scientific and technological enterprise as a system interacting with other systems—social, economic, educational, environmental, etc. At the same time they have been active in seeking the right indicators to assess the demands and results of inventive and innovative activities (Sirilli, 1985). Science and technology indicators may be defined as a series of data designed to answer questions about the state of, and/or changes in, the science and technology system, its internal structure, its relationships with the outside world, and the degree to which it is meeting the goals set to it by those within or without (Fabian, 1979). The aim of science and technology indicators is therefore similar to that of social indicators: to obtain a picture of the state of science and technology and anticipate the consequences of scientific advances and technological change. Statistical data are the basic elements (‘atoms’) with which indicators (‘molecules’) are constructed; thus, the questions indicators have to answer concern aspects of the more general problems which can be tackled using quantitative techniques (OECD, 1992). By definition indicators illustrate a particular aspect of a complex, manysided matter. We therefore need an explicit model that can describe both the scientific system itself and the way it relates to the rest of society. This ideal model would offer the possibility to establish the meaning of each indicator and allow relations to be established among the various indicators themselves (Sirilli, 1993). The development and diffusion of science and technology is an extremely complicated process due to the multiplicity and intensity of links between the various components of the system (Smith, 1996). What appears as a result from one point of view may represent the starting point from another. While

284 GIORGIO SIRILLI

respecting the integral nature of the process, a distinction has traditionally been made between input, output and impact indicators (Freeman, 1982, 1987). More recently this distinction has been superseded by a vision of innovation as a process where feedbacks play a crucial role in chain-linked models, and national systems of innovation propose a perspective in which science and technology should be analysed simultaneously with organizational, institutional, economic and other factors (OECD, 1992; Nelson, 1993; Lundvall, 1992). In practice, as things stand we have no explicit model capable of determining causal relations between science, technology, economy and society in a single synthesis; as a rule, reference is made to implicit or partial theoretical schemes such as models of the link between innovative activities and the economy. The limitations and shortcomings of science and technology models must not, however, be considered an insurmountable obstacle to the devising and application of a set of indicators. On the contrary, they should be considered a natural part of a knowledge-developing process that has already yielded significant results and that promises to live up to the expectations of researchers, the scientific community and the decision-makers in the coming years. The fact that statistical data on various aspects of inventive and innovative activities have been continuously gathered for over thirty years attests to the interest of the scientific community and the decision-makers, while at the same time demonstrating that there are in fact theories, at least implicit, that can guide the operator in the choice and analysis of certain data, rejection of others and appreciation of the need to acquire further data (Patel and Pavitt, 1995). National and international organizations have published for many years indicators resulting from ad hoc surveys and data gathered for administrative, accounting, operating and scientific purposes. These indicators are also available on databases which are maintained by international organizations and private organizations.2 Taken individually these data do not yield a full picture of the various aspects of science and technology but, analysed together, they shed light on the multi-faceted aspects of the same phenomenon, providing greater depth and range to the analysis. Finally, we must point out that, at the level of science policy, science and technology indicators must be seen as a useful support for knowledge; they cannot replace, but integrate the assessment and capacity for choice of the decision-makers. The OECD has played a major role in developing statistical manuals aimed at homogenizing at the international level procedures for data collection and analysis of various indicators. Obviously, the guidelines set forth in the manuals are the recommendations of an international organization based on the principle of common consensus and are to be observed within the limits of feasibility for the sake of improved international data comparability. Various international organizations3 are also active in publishing indicators based on data collected by national organizations on the basis of agreed-upon international procedures as well as on data coming from commercial databases.

SCIENCE AND TECHNOLOGY INDICATORS 285

3 THE MAIN FEATURES OF SCIENCE AND TECHNOLOGY INDICATORS 3.1 Indicators for which data are collected and analysed according to a standardized methodology 3.1.1 Research and development Expenditure and personnel for R&D are the first indicators constructed ad hoc for measuring scientific and technological activities. The first attempts at measurement in the field of statistics on research and development date back to the 1930s in the Soviet Union and the 1940s in the United States. However, it was only in the 1950s that the National Science Foundation (NSF) of the US embarked on a regular survey of research and development in the United States. The vast experience acquired by the NSF exerted a decisive influence on the activities of the OECD, which in 1963 led to the adoption of the Frascati Manual on the measurement of technical-scientific activities. Over the years this manual has been revised four times, reaching maturity with the last revision of 1993 (OECD, 1994a). In the latest revision of the Manual no radical modifications have been made to the basic definitions and concepts. Rather, a lot of updating, elaboration and further specification have been introduced in the light of experience acquired both through the collection of data and through research on the various indicators and their use in analysis and policy-making. The areas where additions or major changes have been introduced are: R&D in the business enterprise sector, classifications of R&D, R&D in the national accounts, R&D and software, R&D in the defence sector, research staff, the fiscal aspects, forecasting and projections, internationalization of R&D. In the field of industrial research, it has been shown that while R&D is a major means to generate technology (Griliches, 1995), it is also necessary to consider ideas and applications deriving from production activities and from activities of technological nature including design, software, technical testing and quality control. The launching of innovation surveys, which cover all the main innovative activities, represents a step forward in this direction. R&D and innovation have an important regional dimension. Local technological competence affects not only the development of a region but also the economy of a country as a whole. EUROSTAT has prepared a manual on statistical indicators for regional activities on R&D and innovation (EUROSTAT, 1995a) which broadens the scope to regional aspects of the OECD Frascati Manual and Oslo Manual. The two major R&D indicators are personnel and expenditure. Data on R&D are regularly collected, either annually or biannually,

286 GIORGIO SIRILLI

by national statistical agencies while international organizations like OECD, UNESCO and the EUROSTAT are active in the collection, analysis and publication of data from an international perspective.4 In the last few years R&D data have become available on individual firms from published balance sheet publications. This has opened up a new stream of economic analyses which correlate R&D with performance indicators at the firm level (Griliches, 1995). The main features of the R&D indicators are the following: • data are reliable, especially because respondents to surveys have learnt over the years to apply the definitions of R&D; • data are comparable over time and across countries; • the breakdowns by sector of performance and sector of financing, type of R&D, type of personnel, etc. allow a rather detailed analysis; • surveys cover only R&D carried out in institutionalized settings, captur-ing very imperfectly non-structured and occasional R&D carried out in small units (i.e. small firms); • it is not clear to what extent R&D covers information-technology-related innovative activities like software; • it is not clear to what extent the recent increase of R&D carried out in the service sector is due to the externalization from manufacturing firms and to what extent it is due to an increasing involvement of service companies in research and development (Young, 1996b). 3.1.2 Patent statistics Patents are the most widely available indicator of output of technological activities. For many years patents counts have been used as indicators of technological achievement of firms and countries. More sophisticated indicators are being developed to give account of the value of patents, based on econometric techniques, and using information possibly correlated to value: renewal, international patenting and citations. Patents can also be used for assessing patterns of technical change: science-technology linkage, intersectorial spillovers, competitiveness of countries in various industries. A patent is a right the state grants to an inventor in return for the publication of his or her invention; for a limited period and with certain conditions, it gives the inventor exclusive rights over the commercial exploitation of the invention. The juridical and legal provisions concerning application for, and granting and protection of, the patent vary considerably from country to country, although there has been a certain levelling out in the last few years. Statistics on patents started to be collected for administrative reasons, with no intention of contributing to the analysis of the inventive process or technological innovation. Nevertheless, patents are a valuable source of information of a quite

SCIENCE AND TECHNOLOGY INDICATORS 287

unique type on the developments of technology, with a highly detailed breakdown by technological fields, firms, countries and over long time-series. During the past few years an increasing debate has centred around the use of patents statistics as an alternative or addition to traditional statistics on scientific research in order to highlight inventive activity, innovative activity and technological progress in general.5 Patents statistics are more detailed in terms of technological classes and cover longer periods than data on R&D. In some countries the historical patents archives date back to the second half of the nineteenth century, while figures on R&D cover only the past thirty years. Moreover, patents reveal inventive activities extending outside the research laboratory including design, quality control, technical services, production and non-structured inventive activities. The utilization of data on patents to measure technical and scientific output raises a number of problems: • the requisites for an invention to be patented, and the type of examination it is subjected to, vary from country to country; • the propensity to patent varies according to the industrial sector, size of firm and type of inventor (individual or employed in an organization); • it is not known what proportion of inventions are patented and thus we cannot tell to what extent patenting reflects the entire area of inventive production; • the ‘quality’ and ‘value’ of patents varies greatly (Lanjouw et al., 1996); • insufficient data are available on the extent to which patents issued are in fact utilized (Napolitano and Sirilli, 1990); • a significant proportion of patents are of the strategic type, i.e. applied for in order to forestall potential competitors. These issues are addressed in detail by the patent manual adopted by the OECD in 1994 with the aim of providing users and producers of science and technology indicators with basic information on how patents data can be used as indicators, and how they can be linked to other statistics on science, technology and economic activity (OECD, 1994d). As regards the sources of data on patents, we may distinguish three types of data providers: individual patents offices in the various countries, a number of international organizations, and commercial companies providing data . Among international agencies we may mention the World Intellectual Property Organisation (WIPO), which has published statistics on patent applications and patents granted all over the world since 1979. The European Patent Office has published data on patent applications since 1978, and is playing an increasing role in the extension of patents in Europe. A variety of national sources have also been used. In particular patents granted in the United States have been used for cross-industry and cross-country comparisons (Patel and Pavitt, 1991).

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The International Patent Documentation Centre, known as Inpadoc, has built up a database with more than ten million patents granted by 51 countries which identifies the patent families, i.e. groups of patents covering the same invention in various countries. Derwent Publications Ltd, a private firm whose main activity is the publication of patent abstracts, has built up an electronic system to process statistics on series of data, including qualitative data, contained in patents documents. CHI Research has built up a database on patents and patent citations at the level of individual countries and companies. One of the most interesting possibilities for the utilization of patents is in the area of technological forecasting: analysis of co-citations can identify groups of patents, pointing the way to areas of intensive technological development (Van Raan, 1988). 3.1.3 Surveys of technological innovation Technological innovation is one of the main development factors in our society: thus the acquisition of tools to interpret and control it is a priority in scientific and technological policy. In the past ten years different countries have launched various initiatives to gather statistical data on the subject (Hansen, 1992; Archibugi and Pianta, 1996; Smith, 1989). The data gathering follows two approaches: the first consists in identifying the most significant innovations and then sending survey questionnaires to the firms that have introduced them in to the country; the second involves submitting questionnaires to the firms that have introduced innovations during a given period of time (Smith, 1992; Arundel et al., 1995). The first approach, therefore, focuses on individual innovations (object approach), while the second focuses on the innovating firms (subject approach) (Archibugi, 1988). Various countries have launched innovation surveys adopting methodologies which are not fully comparable (OECD, 1990b). Experience to date, however, shows that surveys on innovation are not only feasible but yield extremely interesting and useful results (Evangelista et al., 1996; OECD, 1996e). For example, data show that R&D represents only a limited fraction of the innovation expenditure, while other factors, like investment in equipment and machinery and design, represent the largest part of firms’ financial efforts for innovation. Data show also that the pattern of innovation expenditure changes significantly across industrial sectors (Cesaratto et al, 1991). The OECD has published the Oslo Manual on technological innovation, and the first revision has been adopted in 1996 (OECD, 1996c). The Oslo Manual deals with definitions and methodologies for collecting data on the following issues: corporate strategies, the role of diffusion, sources of innovative ideas and obstacles to innovation, inputs to innovation, the role of public policy in industrial innovation, the output of innovation, the impact of innovation.

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The OECD methodology was adopted by EUROSTAT and DG-XIII (European Innovations Monitoring System) within the European Commission, and implemented on a EU-wide basis using a common questionnaire; this survey was known as the Community Innovation Survey (CIS) project. EUROSTAT has now built a comprehensive firm-level database with the CIS data, which contains data on almost 41,000 European firms. CIS was designed to address two main sets of issues. First, the general structure of innovation processes at the level of all European industry, as well as across main typologies of firms and industries. Second, and the most ambitious one, the way national innovation patterns of European countries differ from each other and the determinants of such heterogeneity. As any other innovation survey, the CIS dataset is characterized by strengths and weaknesses. Some problems have emerged as data collected by various countries are not fully comparable (Archibugi et al., 1994). However, in spite of these problems, the CIS exercise has provided extremely useful, providing a description of the main factors influencing the innovation behaviour of firms (Evangelista et al., 1996). It is likely that the second CIS survey, expected to be launched in 1997, will yield a fully comparable set of data for the European countries. The OECD Oslo Manual has been revised in 1996. The basic theoretical background of the Manual has not significantly changed. Greater emphasis has been attached to the necessity to orient the collection of data to the relevant policy questions and the increasingly knowledge-based characteristics of technology and innovation. The Manual has also been improved in the definition of technology and innovation by means of providing borderline examples aimed at distinguishing between technological innovation from aesthetic improvements of products and purely organizational changes. The major novelty consists in the explicit inclusion of services as a target of investigation of the innovation surveys. This implies some revisions in the definition of technological innovation and innovation activities so as to make them applicable both to manufacturing and service firms (Evangelista and Sirilli, 1995; Young, 1996a). 3.1.4 The technological balance of payments The technological balance of payments (TBP) records the flow of funds for transactions concerning industrial property rights. It covers invisible transactions in a country’s balance of payments concerning the purchase and sale of ‘disembodied’ technology in the form of intellectual and industrial property rights, including patents, licences, know-how and technical assistance. It is therefore an indicator of technology transfer across countries (Madeuf, 1984; Hatzichronoglou, 1996). The main features of the indicator are the following:

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1 the TBP offers a partial view of the general phenomenon of world technology transfer and may thus be used as an indicator of the diffusion of technology or competitiveness, possibly together with other indicators including, for instance, data on foreign trade and direct investment; 2 the range covered by the technological balances of the various countries is not uniform: in addition to the flow of funds relating to technological transfer in the strict sense of the term (patents, manufacture licences, technical know-how), in some cases they also include technical services (technical assistance, staff training, consultancy) while in other cases they may even cover payments for intellectual and industrial property rights not directly connected with technology (management services, film rights, etc.); 3 international comparability of the TBP indicator is also limited by the different survey procedures (direct survey through agent banks, or indirect ad hoc survey, sample survey or census); 4 by its very nature, ‘disembodied’ technology, which can be exchanged without financial transactions, gives rise to problems of interpretation; it may occur in the context of cross-licensing agreements, with transfer of know-how to a foreign subsidiary, or in the field of non-commercial international co-operation; 5 trends in transactions included in TBP are significantly conditioned by the multinationals which, according to the available data, are responsible for about two-thirds of the total value of transactions recorded in the technological balance of payments (in the case of the United States these incompany transactions account for 80 per cent of the TBP receipts). In 1982 the OECD began the systematic collection and publication of member countries’ TBP data, and a series of seminars and conferences on the subject have led to the adoption of the Manual for the collection and publication of TBP data (OECD, 1990a) and to later methodological developments in the area (OECD, 1996a). Here we shall list the operations suggested by the OECD experts for inclusion in member countries’ TBP: patents (purchase, sale, licences) know-how (nonpatented), trade-marks (including franchising), technical assistance, R&D financed abroad. The manual recommends the omission of the following operations: commercial, financial, managerial and legal assistance; advertising, insurance, transport; films, recordings, material covered by copyright; design; software. Looking to the developments in TBP statistics of the 1990s, the European market liberalization has had a significant effect since the public authorities are now obtaining technology transfer data from operators on the basis of the intrinsic merit and usefulness of the statistics rather than merely applying an administrative act (Sirilli, 1991). The abolition of currency controls by many countries has given operators greater freedom in their currency transactions overseas. Residents enjoy the right to open bank accounts in foreign currency in

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their own country and in other countries, are no longer obliged to convert currency purchased abroad, and may acquire foreign currency whenever they like without having to prove that it serves for the payment of imports. In other words, possession and conversion of foreign currency are no longer tied to, or limited by, specific trading transactions. This has modified the role played by banks as authorized intermediaries, and thus the function of notifying the central bank on currency operations between resident and overseas operators. Data gathering on TBP in European countries is now carried out through statistical surveys: therefore a break in series has occurred since the beginning of the 1990s. A major problem at present is the linking of old and new series which are based on different statistical populations and data-gathering procedures (Prisco and Sirilli, 1995). 3.1.5 Analysis of international trade in high-technology products The analysis of trade in terms of products grouped according to technological content raises a number of methodological issues. The problem is to distinguish the high, medium and low technology products on the basis of criteria that do not always meet with general agreement among the experts (Grupp, 1995; Guerrieri and Milana, 1995). Most of the studies carried out so far have defined ‘high-tech’ as the fields in which the ratio between research and development expenses and turnover or value added is above a certain threshold. This approach has a number of limitations: • research intensity and technological intensity are not necessarily equivalent concepts; • the statistical data from which technological intensities are drawn are not uniform; • the choice of threshold values for high, medium and low technology is arbitrary; • technological intensity can vary greatly within one group of products; • the intensity ratios take no account of technologies indirectly acquired with machinery and materials. Moreover, they are intrinsically static (taking no account of the accumulation of scientific and technological know-how with time) and vary according to the firm and the country; • within sectors there may be products with widely different technological intensity. Intensities have been recalculated also on the basis of indirect R&D intensity based on input-output matrices (OECD, 1996b). Another method is to determine the innovative content of products according to the assessments of the experts in the individual product group different from

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the standard aggregation of industrial sectors (OECD, 1988). This approach has the merit of allowing a precise distinction of individual products on the basis of their innovative level, with the possibility to separate them from other products belonging to the same group of goods. However, it also has some limitations due to: • the need for a very large team of experts from the various areas involved; • the methodologies used for technological level assessment and thus for the achievement of consensus among the experts; • inevitable concentration on high-tech products, due to the huge difficulty of covering the entire range of products included in foreign trade classifications. As for the differences shown by the findings of analyses carried out using the two approaches, we may observe a reduction in the number of products and related revenue with the second method.6 3.1.6 Bibliometrics Bibliometrics is a tool by which the state of science and technology can be observed through the overall production of scientific publications. It may be defined as the application of statistical methods to data on articles in scientific journals, books and other means of communications. It should be pointed out that scientific activity is much more complex and multi-faceted than that which is captured by indicators focusing on publications only. At the theoretical level, what these indicators actually measure is still an open question. To answer the question we need a clear picture of the nature of science and scientific output. Bibliometric indicators, based on the number of publications, citations and cocitations, mainly refer to basic research, much of which is carried out in academic institutions. The main problems with these indicators include the following: • the propensity to publish and cite varies in the various disciplines; • works of great importance rapidly become part of common knowledge and are thus referred to in the literature without citation; • citations may be critical rather than positive; however, it has been argued that even contested results make a contribution to knowledge; • the various scientific fields are cultivated by groups of varying size, and thus the probability of being cited varies from sector to sector; • the number of citations does not follow a linear rate in the course of time; • the value of scientific work is not always acknowledged by contemporaries; • available databases are subject to some bias towards English-language publications;

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• papers represent only one output of laboratory-based activity. Scientific results relating to information and software are not published to the same degree (Hicks and Katz, 1996). While solutions are being found for some technical problems associated with publications such as reference to the first author in co-authored published papers in the Science Citation Index, and self-citation others remain. Further problems relate to the expectations of potential users. For example, science policy-makers need information on where the scientific work behind the publication has been carried out, the sources of funding, costs, etc. Some of these data which cannot be obtained from existing databases must be sought in the texts themselves or through direct contact with the authors. Bibliometric indicators reflect an aspect of the social structure of science, i.e. communication, which also takes place through other channels such as informal interaction between researchers. Thus, indicators are used on the assumption that the significant advances in science are adequately represented by bibliometrics, although this assumption has been shown to be valid for a restricted number of eminent scientists only. Moreover, it is doubtful whether the same conclusions can be applied, at least without particular distinctions, to all the researchers normally engaged in group activities ranging from study and up-dating to experimental laboratory work, teaching, technical services, advisory services, technology transfer and management (Silvani and Sirilli 1995). To ascertain whether such generalization is possible we need empirical verification, which can only be carried out through the parallel application of bibliometric and non-bibliometric (e.g. peer review) indicators. Various databases established by commercial companies or institutions are used to construct bibliometric indicators: Chemical Abstracts, Compendex, Embase, Inspec, Pascal, Science Citation Index. The Science Citation Index (SCI), which is produced by the Institute for Scientific Information (ISI) in Philadelphia, is the only database that systematically gathers information on the scientific papers published by a large set of journals, and therefore bibliometric indicators are primarily based on it. Various databases and models have been constructed on the basis of SCI data, which is working on a data bank on scientific literature with information on publications and citations appearing in a selected group of 2,100 scientific journals; ISI and the Centre for Research Planning have developed co-citation models to describe the structure and dynamics of scientific literature, setting up databases limited to certain years and identifying some thousands of research areas through the analysis of cocitation between papers. The Pascal database is used for analyses of the co-presence of key words (coword analysis) which reveal the evolution of themes in scientific literature and thus development in the research areas. The OECD has developed some draft guidelines on the use of bibliometrics as a science and technology indicator which covers both scientific literature and

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patents (Okubo, 1995), and which it is hoped will encourage the standardization process. The developments foreseeable for bibliometrics for the next ten years basically follow two directions: closer and more extensive knowledge of the scientific system and relations between its components; assessment of scientific activity as a support for decisions on science policy, particularly in the case of funds for research in the public sector and in the universities. In-depth studies on the scientific system involve a broad range of analytic aspects, of which we may mention: • ‘science mapping’, i.e. the definition of research areas and their transformation, in order to identify the dynamics of scientific activity and the emergence of new disciplines; • the relationship between developments in the scientific system and technological knowledge, with comparison with technology indicators (especially patents) within specific research areas; • analysis of scientific specialization profiles in various countries and institutions, with comparison between countries in order to identify the position of national scientific systems in the global context of scientific activity (Archibugi and Pianta, 1992); • the use of bibliometric indicators in overall analysis of national science policy, in order to identify the features and trends of research in a given country. 3.1.7 Human resources Human resources are a key factor for the production and distribution of knowledge. Science and technology indicators of various countries increasingly include information about students, teachers, researchers, technicians in the context of analyses of the supply and demand for skilled workers for an increasing knowledge-based society. The OECD has adopted the Canberra Manual (OECD—EUROSTAT, 1994b) with the aim of providing a framework for compiling data on stocks and flows of human resources in science and technology, for analysing profiles and trends and for preparing up-to-date series for policy analysis. The Manual provides guidelines for assembling data using both the series that are already available and the results of special surveys, and for facilitating the exchange of joint use of internationally comparable statistics by OECD, the European Commission (EUROSTAT) and other international agencies, notably UNESCO. The concept of ‘human resources in science and technology’ refers to the human resources actually or potentially devoted to the systematic generation, advancement, diffusion and application of scientific and technological knowledge. The Manual, however, covers only individuals with higher-level skills, in particular those with third-

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level education or a job requiring similar skills for which stocks and flows are measured. The Manual represents a major step forward in the international harmonization of data available from various sources. However, given the national specificities of education systems and data-gathering procedures, data are not fully comparable (EUROSTAT, 1995b). Social security databases represent an interesting source of data on the employment of scientists and engineers. In some countries this source of data is used to analyse mobility of specialists across sectors (manufacturing as well as services) and firms (large as well as small firms) over time (Stenberg et al., 1996). 3.2 Indicators for which methodologies are still in a development stage 3.2.1 Indicators based on information of technical journals The ‘literature-based innovation output approach’ consists of the analysis of information about innovations reported in technical and trade journals (Kleinknecht and Bain, 1993; Kleinknecht, 1996). One of the most comprehensive surveys has been undertaken by the US Small Business Administration, which has collected information on 8,000 innovations commercialized in the US in 1982 from technical and scientific journals and magazines (Acs and Audretsch, 1990). The great majority of new products and services are publicized by firms through technical and trade journals which have an edited section on ‘new products’ compiled on the basis of press releases provided by the innovating firms. The information provided by journals usually consists of a brief description of the new product or service and the address and phone number of the firm. This makes it possible to carry out phone or mail interviews asking firms a number of questions about the innovation itself7 as well as the objectives of, and obstacles to, innovation, the sources of information used for introducing the innovation, the means used to secure the appropriation of the benefits of the innovation, the link between the innovation and R&D carried out within or outside the firm, the role played by R&D government programmes, etc. Other information concerns the major economic features of the firm: sales, employees, location. This methodology allows us to collect information about the individual innovation—product, service or project—(object approach), at variance with the innovation surveys conducted on the basis of the Oslo Manual and the R&D surveys based on the Frascati Manual, which refer to the innovating firm (subject approach). The advantages of this methodology are the following:

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1 the burden for responding firms to report information is rather limited, and firms are usually willing to provide information, especially if they are approached soon after the announcement of the new product when they expect potential customers to show up; 2 in principle all sectors of the economy are covered, including services and agriculture; 3 innovations in small firms are more likely to be included, while innovation surveys cover only firms above a certain size (usually with more than ten employees); 4 the data gathered with this methodology may be linked with data from micro-databases like R&D and innovation surveys as well as publicly available data from balance-sheets. The major limitations of this methodology are the following: 1 the statistical universe of innovations is not known. The coverage of the surveys therefore does not allow to use standard statistical sampling procedures. Thus, comparisons of the number of innovations is not possible; 2 since firms have no interest to provide information on process innovations, the overwhelming majority of innovations covered through this methodology concerns products and services; 3 there exist problems of double-counting the same innovation, once it has been imitated by competitors, and problems in identifying imitations introducing some improvement and differentiation of the product or service; 4 until there is no international standardization, the data gathered through this method are not internationally comparable. At present this approach has been adopted in few countries. After the pioneering work by Kleinkhnecht in the Netherlands (Kleinknecht and Reijnen, 1993), a comparative study has been carried out (Kleinchnecht and Bain, 1993) and more specific analyses have been carried out in the UK (Coombs et al, 1996) and in Italy (Santarelli and Piergiovanni, 1996). 3.2.2 Intangible investment In modern societies the knowledge content of production activities is becoming more and more important, and investment is rapidly evolving towards the acquisition of services and the carrying out of activities that pay off over a long period of time. Intangible investment may be defined as the costs of intangible products that become available in the period under review and that remain in use for more than one year (Vosselman, 1992). In principle it includes a series of items: R&D, training, software, marketing, as well as goodwill, mineral exploration,

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development of organizations, rights to use intellectual property or concessions, etc. Ad hoc studies have been carried out in a few European countries launching special surveys (Finland, Norway and Sweden) and using existing statistical data (France and Netherlands). Innovation surveys carried out using the Oslo Manual methodology yield, as a by-product, data on various components of intangible investment, even though the coverage of each item may not be the same i.e. the definition of marketing in the Oslo Manual covers only market exploration, while the one for intangible investment encompasses all marketing expenditure which includes expensive activities such as the building of sales networks. The OECD has started an exercise aimed at harmonizing existing practices in view of the preparation of a statistical manual similar to the others of the ‘Frascati’ family. The core components of intangible investment are: (a) research and development; (b) education and training; (c) software; and (d) marketing. Taking the experience of Finland and the Netherlands, these four components make up about 80 per cent of the total intangible investment which, in turn, represents between 20 and 50 per cent of tangible investment (Vosselman, 1992). In Austria it has been calculated that intangible investment is 43 per cent of all business investment (Hammerer, 1996). The measurement of intangible investment is still in the development stage and the data available in some countries are not easily comparable because of heterogeneity in definitions, coverage and data collection procedures. In perspective, when a harmonized statistical methodology is adopted on the basis of an internationally agreed procedure, it may be envisaged that the data on intangible investment will be characterized by the same kinds of problem of similar industrial data: in particular, the estimation of stocks of intangibles on the basis of agreed-upon assumptions about depreciation, conversion of data for international and intertemporal comparisons. These and other problems have to be settled in the context of the System of National Accounts, which represents the background on which intangible investment should be analysed. 3.2.3 Surveys of manufacturing technologies Innovation and the application of new technologies have moved into the centre of firms’ strategies. However little quantitative data is collected, and new analytical tools to measure innovation and diffusion of new technologies in a harmonized context are needed. Ad hoc surveys have been carried out in a few countries in order to measure various aspects of the use and diffusion of technologies (Northcott and Vickery, 1993): microelectronics, information technology, various manufacturing industries.8 The studies have investigated the objectives and barriers to the introduction of the technologies, their diffusion among firms, public policies

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towards the adoption of the technologies, the impact of their introduction in the firm (OECD, 1993). The results of these surveys are quite encouraging and interesting; however, the surveys are still uncoordinated and deserve a harmonization of concepts and procedures. In particular it is necessary to categorize qualitative aspects of technologies which evolve quickly, making the statistical codification problematic. 3.2.4 Indicators in the field of information and communication technologies Information technology is changing the very nature of work and society. The convergence of information technology and communication raises a number of policy issues: how to bridge the gap between the ‘information rich’ and the ‘information poor’ within societies, the changes in the number and profile of employees in a continuously adjusting economy, the home-based work opportunities, the greater participation of women in the workforce, the implications of electronic commerce using the Internet for conducting business, and the implications of electronic banking. Building indicators in this area is quite necessary; at the same time there are conceptual and operational problems to be overcome before internationally comparable data can be gathered and analysed. The major problems identified by statisticians refer to the very definition of information and communication technologies, the classification of sectors and activities to be surveyed, and the products and services to be covered. In the last few years statistical agencies in various countries have started surveys, both of firms and households, addressing issues concerning the generation and use of information and communication technologies (Pattinson and McGeachie, 1996; Statistics Canada, 1996). In a particular case, information and communication indicators are published in widely diffused statistical publications (Dutch Ministry of Economic Affairs, 1994). The results appear quite interesting, and it may be expected that activities currently under way at the OECD will make it possible to develop a methodology which does not appear to pose particularly difficult theoretical problems. 3.2.5 Measurement of organizational change in enterprises Technology has a potential for providing new opportunities for expanding the range of goods and services, and for increasing productivity and employment; however, the changes in the organization of firms and in the institutional context determine the effectiveness and impact of the adoption of new technologies (OECD, 1996d). Firms can reorganize, both changing their internal structure (e.g.

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altering the relationships and responsibilities of employees) and changing their external relationships (e.g. buying more goods and services from external markets). Changes, which may or may not be directly linked to the introduction of new technologies, regard basically the strategy, the structure, the work-place organization, the human resource management of the firm, as well as its relationship with other firms. Over the last few years some countries have undertaken ad hoc surveys: Canada, Sweden, Japan, Germany; other countries are developing new surveys along similar lines (Picot and Wannell, 1996). The main aim of these surveys is to describe the features of the new work organization and, where possible, link these structures and changes—both to performance indicators at the firm and to human resource management (Vickery and Wurzburg, 1996). Although there are signs of improvement in the number and depth of surveys and indicators, the measurement of the most relevant dimensions of change in organizations still poses a host of conceptual and practical problems. Up to now it has not been possible to put forward proposals for collecting figures on very qualitative phenomena like organization and strategy, and their linkage with performance and structure variables. 3.2.6 Technology foresight Technology foresight may be defined as a systematic attempt to look into the long-term future of science, technology, economy and society, with the aim of identifying the areas of strategic research and the emerging generic technologies likely to yield the greatest economic and social benefits (Martin, 1996). The implementation of this approach has some basic features: prospecting to foretell the future must be systematic; the time horizon taken into account should be a long period, from five to thirty years; the approach should combine the foreseen opportunities offered by science and technology with the expected demands from, and compatibilities with, society and the economy; the identification of emerging generic technologies which may deserve some type of government intervention, both in terms of financing and in strategic orientation for firms. The main objective of technology foresight is to identify potentially important technologies early enough to facilitate their development and utilization. Governments, enterprises and the general public all have an interest in identifying the new emerging generic technologies which are likely to have a large impact on society, the economy, environment and which, by their very nature, require important advances in the science base for their development. Technology foresight differs from technology assessment. While the former is aimed at identifying the most promising technologies in terms of socio-economic returns from which to choose (top-down), the latter identifies a specific

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technology, or a bunch of technologies, and evaluates its overall impact along a set of relevant dimensions (bottom-up). It may be pointed out that the distinction between technology foresight, technology assessment and R&D evaluation (which focuses on the selection and the impact of research projects) is becoming increasingly blurred, and that scholars and practitioners in the field are developing a conceptual and operational framework aimed at unifying the three exercises into a unique approach. The methodology used for the technology foresight exercises consists of making experts interact by means of different techniques: Delphi surveys, panel discussions, brainstorming, scenarios, commissioned studies, expert networks, etc. In order to acquire the necessary information and an adequate variety of options, a large number of experts should be involved: in most of the exercises carried out until now several thousand experts have been drawn from academia, industry, government organizations; and even journalists, science-fiction writers, and representatives of the general public are involved. The countries where government agencies have started or supported foresight initiatives include Japan, Germany, the United Kingdom, the Netherlands, Australia, France and Italy (OECD, 1996f). The questions usually addressed in the exercise are: • how social and economic needs can be addressed by the new technology; • the scientific advancements required to develop the new technology; • strengths and weaknesses of the country in exploiting the new technology and in acquiring comparative advantages vis-à-vis other countries; • strengths and weaknesses of the country’s science base compared with the other countries; • cost of the scientific and technological resources required to reap the expected socio-economic returns; • time dimension of the development, implementation and use of the new technology. The exercise envisages that a few areas are identified by a steering group, and that a larger number of topics are dealt with by field experts9 (NISTEP and ISI, 1994). As an example, the technological areas covered by the Italian exercise are: advanced materials, microelectronics, advanced technologies for information processing, microsystems, software, biotechnologies, production and management technologies (Fondazione Rosselli, 1996); the areas of the last Japanese-German foresight are: material and processing, information and electronics, life science, space, particles, maritime science and earth science, mineral and water resources, energy, environment, agriculture, forestry and fisheries, production, urbanization and construction, communication, transportation, medical care and health, culture and lifestyles (NISTEP and ISI, 1994). The advantages of foresight are the following:

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• it offers governments and firms a set of options on the basis of which to make strategic choices; • even though technology foresight is a comparatively young field, the accuracy of its results is more than acceptable: in the case of the first Japanese Delphi carried out in the early 1970s, 28 per cent of topics had been fully realized and 40 per cent partially realized in the following twenty years; • experience has shown that the process in itself is a valuable output for participants, on top of the expected outputs in terms of priorities for stakeholders in the public and private sectors. The limitations of foresight are the following: • the procedure is very expensive and resource consuming; • consensus building amongst experts may be problematic and depends on the methodologies used (Delphi, panels, brainstorming, scenarios, etc.); • up to now a different mix of methodologies has been used in various countries; • experts may have a vested interest in the outcome of the process; • the results cannot be compared across countries because different lists of technologies are relevant to each country; • foresight may lead companies to concentrate their innovative efforts on a limited set of technologies expected to have high returns, with the consequence on the one hand of missing out promising opportunities which are not given the highest priority and, on the other, of exacerbating competition on those where efforts are concentrated. Only a limited number of countries have up to now carried out foresight exercises. This may also be due to cultural specificities which make it difficult to mobilize a large number of experts working on a project associated with an effort to ‘building the future’, especially in countries where constitutional stipulations stress the concept of the freedom of science. 3.2.7 Public attitudes and public understanding of science and technology Most traditional S&T indicators put the emphasis on the characteristics and impact of science and technology from the point of view of organizations responsible for their implementation. Surveys on public attitudes and understanding of science and technology allow a fundamental dimension to be added to this social process: what are the citizens’ views about scientific developments, scientific programmes or current problems requiring research efforts? Examples include the atom or DNA, the thinning of the ozone layer or the relative importance of medical discoveries vis-à-vis space exploration and

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environmental issues, the understanding of basic scientific concepts like molecules, light speed, the moving of continents. Surveys of samples of citizens are regularly conducted in this field in various countries (National Science Board, 1996) and by international organizations (European Commission, 1993). 4 PROSPECTS FOR FUTURE DEVELOPMENT Having outlined the main science and technology indicators, it is now possible to look into the future. Science and technology are now being analysed in a much wider context than in the past. Policy-makers are interested in disentangling the relationship between science and technology on the one hand, and growth, employment, the environment, organization, the institutional governance of the system, on the other (OECD, 1996d). In order to address this issue, it is necessary to develop theories which not only explain the working of increasingly more complex systems, but which are also amenable to statistical measurement. A case in point is the new wave of analyses of national systems of innovation: the theory is very promising but it has to be turned into more operational and quantitative terms. Building indicators have become a ‘big science’ exercise. No single organization or country can go alone: international organizations are likely to continue to play a major role as promoters of methodological advancements and providers of databases and analytical reports. In the future the OECD is expected to continue to act as a stimulus and ‘clearing house’ for the activities of the various member countries, in particular through the Group of National Experts on Science and Technology Indicators. The increasing number of dimensions to be investigated, and the need for tracing relevant phenomena over time, does not allow data collection of any series to be discontinued, even in the presence of stable or even diminishing resources of statistical agencies. In the last few years firms have been subjected to a demand for information from a growing number of research agencies. They therefore exhibit a natural reluctance to devote further resources to supplying data which are not perceived as a direct advantage to them. However, in the case of statistics on innovation considerable interest is displayed by the firm in collaborating—possibly in view of the fact that the growing attention on ‘innovation’ leads company management to take advantage of this supply of data to carry out an analysis of the firm’s innovative strategies. Data referring to firms’ technological and economic activities is increasingly affected by the process of internationalization, which makes national activities diverge from the technological capacity located within the country’s borders. With respect to the data sources used to construct science and technology indicators, it may be expected that in the future the weight of the databases

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constructed using information collected for administrative purposes is likely to be greater than that of ad hoc surveys (R&D, innovations, etc.). In this way more information will be made available without having to turn to firms for further data supplies. Moreover, it may be foreseen that the role of commercial producers of science and technology indicators will increase vis-à-vis the institutional statistical agencies. The methodologies developed within OECD countries have de facto become the standard also for former socialist and developing countries. This makes the task of building indicators even more challenging: on the one hand data may be compared across almost all countries of the world; on the other, differences in the level of economic development introduce further dimensions and differences in emphasis which make the complete picture even more complex. The agenda of experts in the field of science and technology indicators is rather full. There is a series of areas which have been identified and for which exploratory work has already been started. The measurement of innovation in service companies, organizational innovation, the various dimensions of information technology, innovation in the environment and in other socially relevant fields, the distribution power of knowledge systems, are the most challenging ones. One of the main challenges for those who are in charge of providing indicators is linked to the timing of indicator building. A decade usually elapses from the identification of the need for an indicator and the availability of internationally comparable data. This makes crucial the ability to identify well in advance users’ needs and to build a robust design which can accommodate a moving set of objectives. In conclusion, science and technology indicators may be said to be in a period of rapid evolution. In the coming years the efforts of statisticians, researchers and policy-makers will have to be concentrated on addressing emerging conceptual and methodological difficulties, as well as on improving the systems of data collection and standardization. This will make available a powerful conceptual and information system which will allow us to understand better the complex phenomenon of knowledge creation and distribution, the effects of which are increasingly felt on our values and needs and, ultimately, also on our daily lives. NOTES 1 I wish to thank Daniele Archibugi, Rinaldo Evangelista and Mario Pianta for their useful comments and suggestions. 2 European Commission (1994), National Science Board (1996), OECD (1994c, 1995a, 1995b). 3 E.g. OECD, EUROSTAT, UNESCO. 4 UNESCO (1979, 1994, OECD (1994a), EUROSTAT (1994). 5 Cf. extensively Archibugi (1992), Archibugi and Pianta (1996), Basberg (1987), Griliches (1990), Pavitt (1988).

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6 In the case of Italy the share of high technology products imported drops from 11– 12 per cent to 6 per cent (Amendola and Perrucci, 1990). 7 Such as the degree of complexity, degree of novelty, qualitative properties of the innovation and its description, sector of the economy expected to be the major user of the innovation, etc. 8 E.g. CAD/CAM, FMS, LAN, robots, etc. 9 During the last foresight exercise of Japan, 16 areas and 1,070 topics have been identified; the respective numbers for Germany have been 14 and 1,150.

BIBLIOGRAPHY Acs, Z.J. and Audretsch, D. (1990), Innovation and Small Firms, Cambridge, Mass., MIT Press. Amendola, G. and Perrucci, A. (1990), ‘La competitività dell’Italia nelle industrie hightech: un approccio per prodotti’, Seminario CNR-ENEA ‘La tecnologia italiana’ Rome, CNR, 25 January. Archibugi, D. (1988), ‘In Search of a Useful Measure of Technological Innovation’, Technological Forecasting and Social Change, Vol. 34, No. 3, pp. 451– 471. Archibugi, D. (1992), ‘Patenting as an Indicator of Technological Innovation: A Review’, Science and Public Policy, Vol. 19, No. 6, pp. 357–368. Archibugi, D. and Pianta, M. (1992), The Technological Specialisation of Advanced Countries, Dordrecht, Kluwer. Archibugi, D. and Pianta, M. (1996), ‘Innovation Surveys and Patents as Technology Indicators: The State of the Art’, in OECD, Innovation, Patents and Technological Strategies, Paris, OECD. Archibugi, D. Cohendet, P., Kristensen, A. and Schaffer, K.-A. (1994), ‘Evaluation of the Community Innovation Survey (CIS)—Phase I’, EIMS Publication No. 11, European Commission, Brussels, October. Arundel, A., van de Paal, G. and Soete, L. (1995), ‘Innovation Strategies for Europe’s Large Industrial Firms: Results of the PACE Survey on Information Sources, Public Research, Protection of Innovation and Government Programmes’, Maastricht, MERIT. Basberg, B.L. (1987), ‘Patents and the Measurement of Technological Change: A Survey of the Literature’, Research Policy, Vol. 16, Nos. 2–4, pp. 131–141. Cesaratto, S., Mangano, S. and Sirilli, G. (1991), ‘The Innovative Behaviour of Italian Firms: A Survey on Technological Innovation and R&D’ Scientometrics, No.l, pp. 207–233. Coombs, R. Narandren, P., Richards, A. (1996), ‘A Literature-based Innovation Output Indictor’, Research Policy, Vol. 25, No. 3, pp. 403–413. Dutch Ministry of Economic Affairs (1994), Indicators. Science and Technology, The Hague, November. European Commission (1993), ‘Europeans, Science and Technology. Public Understanding and Attitudes’. Report by Intra Europe and Report International, DG XII, Brussels. European Commission (1994), The European Report on Science and Technology Indicators, EUR 15897, Luxembourg, Office for Official Publications of the European Communities .

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Statistics Canada (1996), ‘Measuring the Global Information Infrastructure for a Global Information Society. Concepts and Performance Indicators’, OECD, ICCP, September. Stenberg, L., Gustafsson, E. and Marklind, G. (1996), ‘Use of Human Resource Data for Analysis of the Structure and Dynamics of the Swedish Innovation System’, Conference on New S&T Indicators for a Knowledge-based Economy, 20–21 June, Paris, OECD. UNESCO (1979), Manual for Statistics on Scientific and Technological Activities, (ST-79/ WS/10), June, Paris. UNESCO (1994), Statistical Yearbook—1994, Paris, UNESCO. Van Raan, A.F. (ed.) (1988), Handbook of Quantitative Studies of Science and Technology, Amsterdam, North-Holland. Vickery, G. and Wurzburg, G. (1996), ‘The Challenge of Measuring and Evaluating Organisational Change in Enterprises’, Conference on New S&T Indicators for a Knowledge-based Economy, 20–21 June, Paris, OECD. Vosselman, K. (1992), ‘Workshop on the Measurement of Intangible Investment. A Note on the Collection and Dissemination of Data’, Paris, OECD. Young, A. (1996a), ‘Revising the Oslo Manual, International Conference on Innovation Measurement and Policies’, 20–21 May, Luxembourg, European Commission. Young, A. (1996b), ‘Measuring R&D in the Services’, STI Working Papers 1996/7, Paris, OECD.

Index

Abernathy, W. 129, 191, 196, 208 Abramovitz, M. 15, 93 accumulation mechanisms: knowledge 38, 126–7; technological changes 55, 58 Acs, Z.J. 294 action theory 38, 94 adaptation 52–3, 195–6, 253 adaptive non linear network (ANN) 253 adjustment, production 64, 217–18 Adobe 113 adoption of technology 15, 29, 127–32, 133 aggregate demand 68 aggregate production function 10 Alchian, A.A. 33–4 Alexandroff Syrquin, A. 41 (nl) algorithms 160–1 Allen, R.C. 127 Amendola, M. 30, 32, 74, 78, 80, 216, 217– 18, 219 Anderson, L. 59 Antonelli, C. 131 Antonelli, G. 6, 24, 26, 37, 93, 96 Apple Computers 109, 113 appropriability, innovation 55, 232 Arai, T. 141 Archibugi, D. 276, 287, 288, 293 Armstrong, PJ. 68 Arrow, K. 5, 13, 14, 57, 125, 157, 230–1 artefacts 94, 126, 189 Arthur, B. 133 Arthur, W.B. 37, 208 Arundel, A. 287 Ashby, W.R. 195, 198 Audretsch, D. 294 automobile industry 129, 268–9

axioms 160 Babbage, Charles 7, 35 Bagnasco, A. 237 Bain, D. 294, 295 Bairoch, P. 89 balance of payments, technological 288–90 Balcer, Y. 130 Balcet, G. 241 Baldini, V. 252 Barro, R.J. 14 Barren, J.J. 245 Barzel, Y. 130 Baum, E.B. 247, 254 (n16) Becattini, G. 38 Beccaria, Cesare 5 Becker, G. 91 Bellini, N. 250 Bertin, G.Y. 261, 271 Bettio, P. 91 Bianchi, P. 91, 234, 237, 250, 254 (n10) bibliometrics 291–3 Bijker, W.E. 60 Binmore, K.G. 151 Binswanger, H.P. 189 black boxes concept 88, 188, 189, 197, 231 Blattner, N. 68 Boltzmann, Ludwig 203 Bosworth, D.L. 66 bounded rationality 157, 159, 249 Bowman, A. 189 Bowman, M.J. 10 Boyer, R. 29, 72, 73 Britain: exports 272;

309

310 INDEX

innovation 276; patenting 261–2, 263, 264, 268, 272 Brusco, S. 38 Burns, A.F. 136 business unit 56, 57–8, 59–60 Camagni, R.P. 233, 235, 236 Cameron, H.M. 145 Campbell, D.T. 53 Cantalupi, M. 218 Cantwell, J. 260, 261, 268–9 capital: human 91; knowledge 14, 217; and labour 66, 72; natural 98, 99; physical 91–2; vintage capital simulation model 72–3 capitalism 16, 17, 109 Centre for Research Planning 292 Cesaratto, S. 287 Chenery, H. 15 CHI Research 287 choice 54, 156–7 citations 291 Clark, J. 72 Clark, J.A. 68 class, technology effects 73 Coase, R. 163, 165, 233, 234 Cobb, C.W. 12 Cobb-Douglas production function 12, 14 Cohen, W.M. 221 Colombo, M.G. 242 communication code 57 communication technology 297 Community Innovation Survey 288 compensations effect theories 64–8, 77; income 65, 66–7, 74, 75–6; price 65–6, 73–4, 75, 76 competence gap 156, 161–2 competition 29; design configurations 59; incompatible standards 112–13; innovation 210–12; markets 76, 77; niches 133, 137; policy 3;

selection 51, 60; technological 132, 133, 137, 151–2, 210 competitive advantage 55–7, 60, 242 complementarities, technology 66, 132 computer science 244–6 Constant, E.W. 58, 59, 140 consumer theory 8, 17–18 consumption 92, 232 contract 165 control function 236, 251 Coombs, R. 18, 188, 295 Cooper, A.C. 61, 132 Cooper, C.M. 68 co-operative agreement, network model 241–2 co-ordination: decision-making 163; information costs 234–5; innovation 231; market mechanisms 37, 166; policies 3 Coriat, B. 29 cost function, cumulated 178 costs 9, 13–14, 147, 173, 202, 234–5 creative destruction 16, 51, 197 creativity 53, 56, 58 credit mechanism 110–11 cumulative growth model 29 currency controls, abolition 289–90 cycle time 170, 171–3, 181 (n5) Cyert, R.M. 66 Darwin 52 David, P. 218 David, P.A. 129, 131, 141 Davis, H.T. 151 Dawkins, R. 56 De Liso, N. 37, 38, 93 decentralization 237, 259–60 decision-making 35, 100–1, 156–7, 163 Delphi surveys 299, 300 demand 18, 24, 68, 71–2, 150 ‘demons premium’ 216 Department of Trade and Industry 141 Derwent Publications Ltd 287 designs, technology 58–9, 191, 208

INDEX 311

development 6–8, 17, 51, 52, 187–8; see also sustainable development Di Bernardo, B. 234 differentiation 53, 54, 72, 115 diffusion: innovation 218; knowledge 53, 96, 127–8; supply/demand rates 125; technological 79–80, 125–7, 132–3, 135–7, 139–41, 145; see also technology flows; technology transfer disequilibrium 5, 63, 197 distribution 116–17 Domar, E.D. 26–7 Dosi, G. 58, 59, 78, 126, 139, 191, 208, 218, 230, 232, 237, 276 Douglas, P.M. 12 Duchin, F. 21–2, 67, 73, 80 Dunning, J. 259, 261 Dutch Ministry of Economic Affairs 297 ecological modernization 99 economic agents 125, 159, 164, 236–7 economic behaviour, evolution 52–4 economic growth: actual/potential 15–16, 95; components 88; cumulative model 29; endogenous 88–9; equilibrium 124; extinction 99; human resources 7, 16, 88, 216; model, Pasinetti 201, 208; structural change 124; sustainable development 98–100 economic neural network 243–4, 245, 247– 9, 250–2, 253 economic revolutions 86 economics: business-led 17; dynamics x, xi, 73, 78; innovation 187–8, 197–8; structural changes 89–90; variety 52, 54, 197–204 economies, internal/external 9 education 219;

see also training Egidi, M. 165 electrical engineering case study 181 (n12) Eliasson, G. 60 Elster, J. 53 employment: demand 24; innovation 68, 71–2; investment 5, 28–9; markets 71; national income 27; production 3; scientists/engineers 294; technology 69, 78–9, 80; wages 31; see also labour endogeneity 5–6, 88–9 Engel’s law 20 entrepreneurs 16, 28, 109 entropy 202–4, 206–7, 213 environmental protection 3, 88, 92, 94–5, 99 environmental resources 96–8, 100; see also natural resources equilibrium: economic growth 124; macroeconomic/sectoral 74; partial/ general 63, 69–70 equilibrium niche 134–7, 145–51 equity, natural capital 99 European Commission 301 European Innovations Monitoring System 288 European Patent Office 262, 286 European Union 4, 289 EUROSTAT 284–5, 288, 294 evaluation policy 3 Evangelista, R. 287, 288 evolutionary approach: economic behaviour 52–4; innovation 232; path dependence 249; profitability 34; R&D 35–6; technological progress 35–7, 51–2, 209–10; technology 192–6, 208; variety 207–10, 212–13

312 INDEX

evolutionism 36–7, 52, 108 expectations 29–30, 130–1, 157 exports, and patenting 272 externalities management 238 Fabian, Y. 282 Fazzari, S.M. 216 Feller, I. 129 finance: constraints 132; for innovation 108–11; investment 216–17; resource allocation 110–11 firms: competitive advantage 60, 242; decision-making 101; information costs 202; innovation propensity 111–15, 232–3, 247; knowledge 189, 217; neural network 243–4, 245, 247–9; as organization 163; R&D 109, 232; selective pressure 56–7; technology 91; uncertainty 233–242 fixwage path 31 flexibility 2, 77, 171, 174–6 Fondazione Rosselli 299 foreign patenting 258, 259–71, 274, 276 France: exports 272; manufacturing industry 272; patenting 261–2, 263, 264, 267, 272 Franke, R. 73 Frankel, M. 128 Freeman, C. 18, 35, 72, 126, 197, 219, 231, 283 Gaffard, J.-L. 30, 32, 74, 78, 80, 216, 217– 18, 219 game theory 15 Gardner, P. 127 Gatlin, L.L. 204 gender factors, labour 91 Georgescu-Roegen, N. 33, 181 (nl) Georghiou, L. 126

Germany: exports 272; innovation 276; manufacturing industry 272; patenting 261–2, 263, 264, 266, 272 Gersting, J.L. 161 Gibbons, M. 56, 132, 145, 189, 210 global economy 253 Gordon, R.J. 30 Gort, M. 127 Gould, J.D. 109 government intervention 2, 140, 230–1 Graham, G.S. 132 Griliches, Z. 126, 284, 285 Grillo, M. 116 Group of National Experts on Science and Technology Indicators 301 growth accounting 9 growth: see economic growth Grupp, H. 290 Guerrieri, P. 290 Hagedoorn, J. 58 Hagemann, H. 73 Håkansson, H. 233, 238, 240, 251 Haken, H. 201 Hammerer, G. 296 Hammond, P.J. 232 Hansen, A.H. 27 Hansen, J. 287 Hanusch, H. 97 Harley, C. 135 Harrison, B. 38, 86 Harrod, R.F. 12, 27 Hatzichronoglou, T. 288 Heffernan, S.A. 65, 71 Heiner, R.A. 156 Helpman, E. 14 heredity mechanisms 52 heuristics 101, 191, 209 Hicks, D. 292 Hicks, J.R. 12, 28, 30, 31–2, 182 (n17) hierarchies: neural networks 246–7, 248, 254 (n10); in organizations 207 high-technology products 290–1

INDEX 313

Hodgson, G.M. 35 Hodson, C. 260, 261 Holland, J. 253 Holt, C.C. 91 Hounshell, D.A. 188 Howell, D.R. 67, 73 Hughes, T.P. 58 human capital 91 human resources: economic growth 7, 16, 88, 216; integration 4; job vacancy 216, 224–6, 228; labour 90–1; learning process 80, 216, 222–4, 293– 4; local markets 38; recruitment 224 IBM 112–13 Ilzkovitz, F. 66 income: see national income; nominal income income compensation effects 65, 66–7, 74, 75–6 industrial districts 29, 232, 233 Industrial Organization and Public Policy approach 91 industrial revolution 87 inference, rules of 160 information: asymmetric 216; costs 202, 234–5; local 235–6; markets 157; organizational changes 204–13; science at national level 282; variety 201–4, 207–9 information revolution 87 information technology 297 innovation: adoption 29, 128–32; advantage 117–18; as agent of change 188; appropriability 55, 232; competition 210–12; constraints 216–19;

co-ordination 231; decision-making 156–7; diffusion 218; economic effects 187–8, 197–8; employment 68, 71–2; evolution 232; feedbacks 283; financing 108–11; firms’ propensity 111–15, 232–3, 247; interdependence 115–18; investment 123–4; lead time 171, 176–80; learning/discovery 155–6; local environment 234–8; market structures 2, 114; national system 38; neoclassical economics 155; networks 113–14, 238–42; organizational 155–6; problem-solving 157–61; process 68, 218; products 2, 68, 195, 218, 219–24; R&D expenditure 284–5, 287–9; radical/incremental 196; rational choice 156–7; resource-saving 117; Schumpeter 16–19, 197, 212, 231; search 166, 235–6, 251; sectors 22; sociocultural factors 219; spatial factor 260; structural change 123–4; supply/demand factors 132; surveys 287–8; time factor 27, 32, 69, 219; variety/development 197–204 innovation management 235–6, 251 innovation policy 230–2 input-output analysis 20–3, 73, 76, 93, 94 Institute on the Dynamics of Economic Systems, National Research Council xi Institute for Scientific Information 292, 299 institutions 55, 90, 95, 124, 219 interdependence: innovation 115–18; production 7, 86, 128; public policy 100;

314 INDEX

resources 241; sectorial 115; strategic 158–9; technological change 108; technological systems 197–8 International Patent Documentation Center 262, 265, 287 International Science Park Association 250 international trade 290–1 internationalization, R&D 258, 259 inventions 16 inventor 109–10 investment: employment 5, 28–9; financial constraints 216–17; innovation 123–4; intangible 295–6; multiplier-accelerator principle 73, 76; optimal period 180; sustainable development 97; technology adoption 128–32; time factor 27, 129–30; wages 66–7 IS/LM model 28 Italy: foreign patenting 271–4; innovation 276; manufacturing industry 273; multinational firms 270; patenting 261–2, 263, 264, 268–9, 275– 8

Keynesianism, unemployment 64–5, 73 Kindleberger, C. 129 Kim, L. 59, 132 Kleinknecht, A. 294, 295 Klepper, S. 127 Knight, F.H. 159, 232 knowledge: accumulation 38, 126–7; adoption 127–8; as capital 14, 217; codification 239–40; context-specific 94; decision-making 100–1; direct/ indirect 95; dissemination 53, 96, 127–8; firm theory 189, 217; indivisibility 232; information organizing 96; investment 295–6; learning process 96; organizations 53–4; as power 94; production factors 92–6; structured 57–61; system dynamics 95; tacit 93, 94, 259, 267; technological 54–5, 57–61, 93, 126, 162–6, 189 knowledge incompleteness 158–9, 166 Kurz, H.D. 73, 76, 80 Kuznets, S. 15, 136

Japan, patenting 261–2, 263, 264 Jevons, W.Stanley 8 Jewkes, J. 109 job vacancy 216, 224–6, 228 Jovanovic, C. 131

labour: and capital 66, 72; education 219; human resources 90–1; local environment 38; supply/demand mismatch 217; women’s participation 91 labour division 4, 166, 205–7 Lach, S. 131 Lancaster, K. 189 Lanjouw, L.O. 286 Lawsonn, C. 73 Layton, E. 37, 54, 126 Lazonick, W. 129 lead time 171, 176–80

Kaldor, Nicholas 27–8, 29 Kaldor-Verdoorn law 2, 28 Kalecki, M. 27 Kalmback, P. 73 Kaplinsky, R. 69 Kapp, K.W. 89 Katsoulacos, Y. 65, 66, 68, 71, 76 Katz, S. 292 Keynes, John Maynard 26

INDEX 315

learning: collective 236; human resources 80, 216, 222–4, 293– 4; innovation 155–6; organization/ technology 162–6; ongoing 96; productivity 74, 78–9; self-organization 247; see also knowledge learning-by-doing 4–5, 127, 217 learning-by-using 14, 217 Lekvall, P. 128 Leoncini, R. 22 Leoni, R. 216 Leontief, W. 20–2, 23, 67, 73, 80 Levin, S. 132 Levinthal, D.A. 221 life cycle model 68, 91, 196 Linstone, D. 128, 137 Lippman, S. 130 Loasby, B.J. 95 local environment: constraints 243; information functions 235–6; innovation 234–8; labour markets 38 localization 234, 237 logic, formal 160 lot-sizing, optimal 173 Lundvall, B.-Å. 35, 127, 283 Lutz, F. and V. 129 Maastricht agreement 4 McCulloch, John Ramsay 6 McGeachie, D. 197 McNulty, P.J. 210 Madeuf, B. 288 Maggioni, M.A. 250, 251 Magnusson, L. 33 Magun, S. 73 Mahajan, V. 137 Malerba, F. 276 Malthus, Thomas Robert 6 Mansfield, E. 126, 132 manufacturing industry, sectors 272, 274 manufacturing technology surveys 296–7

Marengo, L. 22 Marginalists 8 Mariotti, S. 241 Mariti, P. 241 market entry 116 market lock-in 115 markets: competitive 76, 77; co-ordination 37, 166; demand 150; employment 71; financial resource allocation 110–11; information/ knowledge 157; innovation 2, 114; internalization 233; liberalization 289; prices 29, 114; R&D 114; reabsorption 65; second-hand 129 Markov processes 36 Marsden, D. 99 Marshall, A. 8–9, 13, 33, 129, 233, 234, 254 (n9) Martin, B. 298 Marx, Karl 7–8, 52 Mathias, P. 188 mechanisms: characteristics approach 193–6; elementary 209–10 mechanization 5, 6, 87 Menger, Carl 8 mercantile revolution 87 Metcalfe, J.S. 34, 35, 36, 37, 38, 54, 56, 59, 79, 93, 132, 145–6, 189, 210 Microsoft 113 Migliarese, P. 241 Milana, C. 290 milieu concept, environment 237–8 modernization, ecological 99 Moggi, M. 276 Momigliano, F. 241 Monod, J. 53 monopoly push 18–19 Morgan, B. 57 motor vehicles: see automobile industry Mowery, D.C. 66

316 INDEX

multiplier-accelerator principle 73, 76 multisectoral models 20–6, 116–17 Napolitano, G. 286 Narin, F. 276 national income 27 National Research Council xi National Science Board 301 National Science Foundation 284 national science policy 282, 293 natural capital 98, 99 natural resources: environment 92, 93, 94–5; sustainable development 98–100 Neary, J.P. 65, 71 Nelson, R.R. 33, 34–5, 36, 78, 109, 191, 208, 197, 209, 218, 283 neo-Austrian economics 30–1, 74, 80, 182 (n17) neo-Keynesians 26–30 neoclassical economics 8–16; employment 78; innovation 155; market entry 116; price effects 76–7; production function models 9–10; reabsorption mechanisms 71; value theory 116 networks: and environment 242–7; hegemony 243; innovation 113–14, 238–42; language 238, 239–40; production 33 neural networks: adaptive non linear 253; computer science 244–6; evolutionary framework 247–9, 253; firms 243–4, 245, 247–9; model 243–4; organization theory 246–7; science parks 250–2 neurocomputers 244–6 neutrality 11, 12, 13 New Industrial Economics 91 niches: competition 133, 137;

diffusion 145; equilibrium 134–7, 145–51 NISTEP 299 Noback, C.R. 245 Nolan, R.L. 245, 246 nominal incomes 68, 72–3, 74, 81 non-produced means of production (NPMP) 25–6 Northcott, J. 296 Nuti, F. 38 Obermeier, K.K. 245 OECD: (1987) 282; (1988) 291; (1990) 287, 289; (1992) 282, 283; (1993) 297; (1994) 283, 286; (1996) 287, 289, 290, 297, 299, 301; bibilometrics 293; Canberra Manual 293–4; Frascati Manual 284, 294; Oslo Manual 284–5, 287–8, 294, 296; patents 261–2, 263 OECD—EUROSTAT 293 Okubo, Y. 293 Olivastro, D. 276 Olsen, T. 131 organization: changes 237, 297–8; design configurations 59–60; firms 163; hierarchical 207; information 204–13; innovation 155–6; knowledge 53–4; labour division 205–7; neural networks 246–7; openness 57; problem-solving 164–5; profitability 36; routines 209; technology 162–6, 297–8 Orsenigo, L. 276 Oster, S. 132 ownership advantages 259, 260

INDEX 317

Ozawa, T. 259 Paci, R. 261, 271 Pascal database 292–3 Pasinetti, L.L. 23, 68, 73, 201, 208 Patel, P. 283, 286 patenting activity: concentration 261–2; country-specific behaviour 267–8; foreign 258, 259–71, 274, 276; regulation 230–1; sectorial analysis 262, 264–9; statistics 285–7 path dependence 158, 218, 249 Pattinson, W. 297 Pavitt, K. 127, 189, 272, 274, 274, 283, 286 Pearce, D. 98 Pegoretti, G. 26, 96 Ferez, C. 219 Personal Computer, clones 112–13 personnel, skilled 132; see also human resources Peterson, R. 137 Petit, P. 29 Petretto, A. 92 Phillips, A. 237 physical capital, technology 91–2 Pianta, M. 287, 293 Picot, G. 298 Piergiovanni, R. 295 Pini, P. 29 Piore-Doeringer thesis, training 222 policy-making: competition 3; co-ordination 3; innovation 230–2; natural and environmental resources 97; science at national level 282, 293; structural change 124; technological change 1–4, 139–41; technology foresight 298–300; unemployment 2, 3; see also public policy population: evolutionary 192;

technological 191–2, 193, 211 populational adaptation 52–3 Postscript language 113 predictability 35, 36 price compensation effects 65–6, 73–4, 75, 76 prices: demand 71; markets 29, 114; neoclassical economics 76–7; neo-Keynesians 29–30; rigidity 19; scarcity 6; systems 116; technological change 6, 24, 81 Prigogine, I. 201 Prisco, M.R. 290 probability 36, 203 problem-solving: innovation 157–61; organization 164–5; satisficing 161–2 process innovation 68, 218 producer, and consumer 17–18 production 88–9; adjustments 64, 217–18; Austrian process 30; constraints 228; decentralized 237; displacement 124; dynamics 89–92; and employment 3; interdependence 7, 86, 128; knowledge 92–6; as network 33; organization 217; Smith, Adam 4; technology 33, 86–9, 188–9; time 30–3, 170–1; unit costs 147 production factors: “broad” 87 production function models 9–10 production process 180–1 (nl); factor productivity 71; flexibility 171, 174–6; NPMP 25–6; task 181 (n4)

318 INDEX

productivity 20, 72, 74, 77–9 products: characteristics/pattern of imaging 190; costs case study 181 (n12); differentiation 72, 115; high-technology 290–1; homogeneity/ diversity 211; innovation 2, 68, 195, 218, 219–24 profitability: evolutionary approach 34; expectations 131; maximization 9; organizational forms 36; technology adoption 133 proportionality, investment/ employment 5, 28–9 public policy 100, 249–50 Pugno, M. 28 Quadrio Curzio, A. 6, 24, 26, 70, 86 R&D (research and development): bibliometrics 291–3; business-financed 16–17, 188–9; evaluation policy 3; evolutionary theory 35–6; expenditure 15, 274, 287; firms 109, 232; innovation 284–5, 287–9; international 258, 259; market presence 114; and production 112–13; regional level 284–5; returns 125; statistics 281, 284–5; structural transformation 15–16; technological change 14; technological knowledge 93; types 220–2, 223, 224 rational choice, innovation 156–7 rational expectations 30, 157 rationality: see bounded rationality Ravetz, J.R. 101 reabsorption process 63, 65, 68, 70, 71 recruitment, human resources 224 Redclift, M. 98

Reijnen, J.O.N. 295 Reinganum, J.F. 15 Reisman, D. 234 research: see R&D residual 9, 11 resources: environmental 96–8, 100; intangible 96–8, 101, 102; interdependence 241; natural 92, 93, 94–5, 98–100; rationed 117; specialization 239; technological 260; underutilized 208 retention, selective 53 returns to scale 10, 13–14, 146–7 revealed technological performance concept 189 revenue function, ex ante/ex post 178, 179 Ricardo, David 6, 24, 25 Richardson, G.B. 95 rigidities: nominal 29–30; price 19 risk 110–11, 232, 237 Robertson, D.H. 166 Romeo, A. 132 Romer, P.M. 14 Rosenberg, N. 7, 14, 58, 78, 95, 127, 130, 132, 188, 260 Rossegar, G. 129 Rothwell, R. 127 routines 35, 191, 209 Rullani, E. 38, 94 Sahal, D. 58, 127, 128, 131, 137, 191, 208, 222 Sala-i-Martin, X. 14 Salter, W.E.G. 13, 129 Santarelli, E. 276, 295 Sassu, A. 261, 271 satisficing, problem-solving 161–2 Saviotti, P.P. 36, 54, 59, 189, 190, 195, 201, 205, 207, 209, 211, 212 scale economies 208 scarcity, relative 6, 24, 87, 97 Scazzieri, R. 33, 70, 86, 181 (n4)

INDEX 319

Schendel, D. 61, 132 Schilirò, D. 73 Schmookler, J. 19 Schumpeter, Joseph: competition 210; economic development 17, 187–8; entrepreneur 109; innovation 16–19, 197, 212, 231; monopoly 18–19; mutation 34; selection 37 Science Citation Index 292 science parks 232, 250–2 science and technology indicators 281–4; bibliometrics 291–3; future development 301–2; mapping 293; public attitude 300–1; standardized 284–94; statistics 283–4 screening-signalling function 236, 251 Screpanti, E. 35 search function 235–6, 251 second-hand market, equipment 129 sectoral analysis 23–4, 72, 115–17; multisectoral models 20–6, 116–17; patents 262, 264–9 selection 36, 37, 51, 52–3, 56, 60 selection function 236, 251 self-organization 247, 249 Shackle, G.L. 54, 125 Shannon, C.E. 203 Silvani, A. 292 Simon, H.A. 158, 166, 190 Sirilli, G. 282, 286, 288, 289, 290, 292 Sismondi, J.C.L.S. de 6 skills 94, 126–7 Smiley, R.H. 241 Smith, A.K. 188 Smith, Adam 4–5, 181 (n4) Smith, K. 282, 287 social capabilities 93, 95 social security databases 294 social wealth 16 sociocultural factors, innovation 219 Soete, L. 72, 139, 274, 276 Solow, R.M. 10, 11, 36 spatial factor:

innovation 260; technological change 230, 232 specialization 194, 210, 239, 293 speed economies 242 Spence, A.M. 216 Spence, M. 91 Sraffa, P. 23, 116 standards, incompatibility 113 Standing, G. 64 statistics, science and technology 283–4 Statistics Canada 297 Steedman, I. 13 Stenberg, L. 294 Stengers, I. 201 Sterlacchini, A. 22, 276 Stiglitz, J. 14, 260 Stoneman, P. 10, 65, 66, 67, 68, 71, 74, 79, 80, 128, 132 Storper, M. 38, 86 structural change 123–4; economic dynamics 73, 78, 89–90, 124; policy-making 124; R&D 15–16; technology 187; women in labour force 91 structural unemployment 64–5 substitution: elasticity 109; price effects 66; old/new technology 67, 137–9, 146, 148–9, 193–6, 210 substitution curve 137–9, 151–2 sustainable development 97, 98–100 switching time 170–1, 173–6 Sylos Labini, P. 5, 28–9, 66, 72, 73, 79 synergy 24, 201 Syrquin, M. 15 systemic analyses 37–9, 51 Szyld, D.B. 73 Tarantelli, E. 90 technical change: see technological change technical journals 294–5 technical personnel 224–6, 227 technical progress:

320 INDEX

see technological progress technological balance of payments 288–90 technological change x–xi, 9–12; accumulation 55, 58; benefits 66; conceptualization 78; cumulative/ local 260; embodied/disembodied 10, 12–13, 14, 289; employment 69, 78–9, 80; endogenous 5–6, 20, 188–9; expectations 130–1; interdependence 108; nominal income 72–3; perspectives 107–8; policy making 1–4, 139–41; price 6, 24, 81; R&D 14; rapidity 114; spatial factor 230, 232 technological designs 58–9, 191, 208 technological diffusion: see diffusion technological guideposts 191, 208 technological innovation: see innovation technological paradigm 58, 60, 191, 208 technological population 191–2, 193, 211 technological progress: capitalism 17; decentralization 259–60; effects 23–4; efficiency 25; evolutionary model 35–7, 51–2, 209– 10; production systems 33, 86–9, 188–9; Ricardo 24, 25; substitution 67, 137–9, 146, 148–9, 193–6, 210 technological regime 59, 208 technological revolution 87 technological systems: analysis 24–6, 37–9; coherence 51; environment 93–4; heuristic model 101; interdependence 97–8; national 260;

production factors 89 technological unemployment 1–2, 63, 64– 5, 70–5 technology 125–7; adoption 15, 29, 127–32, 133; characteristics 54–5; competition 132, 133, 137, 151–2, 210; competitive advantage 55–7; complementarities 66, 132; designs 58–9, 191, 208; evolution 192–6, 208; fundamental/adaptive variables 195–6; as knowledge 54–5, 57–61, 93, 126, 162–6, 189; negative effects 7–8; organization 162–6, 297–8; physical capital 91–2; production 86– 9, 188–9; representation 190–2; as skill set 126; strategy 55–7; structural change 187; see also diffusion; technological change; technological progress technology flows 261–2, 263, 264, 265; see also diffusion technology foresight exercises 298–300 technology multiplier effects 65, 67, 73, 76 technology transfer 288–9; see also diffusion Teece, D.J. 237, 242 theorems 160 thermodynamics, non-equilibrium 201 time: cycle time 170, 171–3, 181 (n5); innovation 27, 32, 69, 219; investment 27, 129–30; lead time 171, 176–80; nominal incomes 68; production 30–3, 170–1; switching time 170–1, 173–6 time-duration indicators 170 time saving 5, 170, 171, 177 Toulmin, S. 52 trade cycle theory 27 trade-offs 94 training, human resources 222, 225

INDEX 321

transaction 238–9 transaction costs 234 transcoding function 236, 251 transformation apparatus 86 transformer function 236 transport 87, 132, 200 Tushman, M. 59 uncertainty 158, 159, 232–42 unemployment: Hicks 32; Keynes 26; Keynesian 64–5; policy-making 2, 3; structural 64–5; technological 1–2, 63, 64–5, 70–5 United States: exports 272; patenting 261–2, 263, 269, 276; Small Business Administration 294 Ure, Andrew 7 Utterback, J.M. 59, 132, 191, 196, 208 Vaccà, S. 233, 238, 239 Valentin, F. 127 value theory 116 Van Raan, A.F. 287 variety: and diversity 3–4; economics 52, 54, 197–204; information 201–4, 207–9; in innovation 197–204; output/process 202; technological evolution 207–10, 212– 13 Veblen, T. 33–4 Venables, A.J. 71 Vickery, G. 296, 298 Vincenti, W. 126, 127 vintage-capital simulation model 72–3 Von Hippel, E. 127 Vosselman, K. 295, 296 wages: employment 31; flexibility 77; investment 66–7;

neo-Austrians 31; neo-Keynesians 29–30; productivity 20, 77–8 Wahlbin, C. 128 Walras 8, 23 Wannell, T. 298 Waterson, M. 74, 80 Weaver, W. 203 Weghorst, W. 73 welfare, conduct/performance 91–2 Whitley, J.D. 66, 73, 76, 80 Williamson, O.E. 233, 234 Wilson, R.A. 66, 73, 76, 80 Winter, S.G. 33, 35, 36, 78, 109, 191, 208, 197, 209, 218 Wojick, D. 58 women in labour force 91 World Intellectual Property Organisation 286 Wurzburg, G. 298 Wyatt, S. 261, 271, 276 Young, A. 13, 285, 288 Young, K. 73