Routledge handbook of the economics of knowledge

Routledge Handbook of the

Economics of Knowledge

The Routledge Handbook of the Economics of Knowledge provides a comprehensive framework to

integrate the advancements over the last 20 years in the analysis of technological knowledge as

an economic good, and in the static and dynamic characteristics of its generation process.

There is a growing consensus in the field of economics that knowledge, technological knowl￾edge in particular, is one of the most relevant resources of wealth, yet it is one of the most difficult

and complex activities to understand or even to conceptualize. The economics of knowledge

is an emerging field that explores the generation, exploitation, and dissemination of techno￾logical knowledge. Technological knowledge can no longer be regarded as a homogenous good

that stems from standardized generation processes. Quite the opposite, technological knowledge

appears more and more to be a basket of heterogeneous items, resources, and even experiences.

All of these sources, which are both internal and external to the firm, are complementary, as is

the interplay between bottom-up and top-down generation processes. In this context, the inter￾actions between the public research system, private research laboratories, and various networks

of learning processes, within and among firms, play a major role in the creation of technological

knowledge.

In this Handbook special attention is given to the relationship between technological

knowledge and both upstream scientific knowledge and related downstream resources. By

addressing the antecedents and consequences of technological knowledge from both an upstream

and downstream perspective, this Handbook will become an indispensable tool for scholars and

practitioners aiming to master the generation and the use of technological knowledge.

Cristiano Antonelli is Professor of Economics at the University of Torino where he is the

President of the School of Economics and Statistics and a Fellow of the Collegio Carlo Alberto,

Italy.

Albert N. Link is Professor of Economics at the University of North Carolina at Greensboro, USA.

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Routledge Handbook

of the Economics of

Knowledge

Edited by Cristiano Antonelli and Albert N. Link

First published 2015

by Routledge

2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN

and by Routledge

711 Third Avenue, New York, NY 10017

Routledge is an imprint of the Taylor & Francis Group, an informa business

© 2015 selection and editorial material, Cristiano Antonelli and Albert

N. Link; individual chapters, the contributors

The right of the editors to be identified as the authors of the editorial

material, and of the authors for their individual chapters, has been

asserted in accordance with sections 77 and 78 of the Copyright,

Designs and Patents Act 1988.

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.

Trademark notice: Product or corporate names may be trademarks or

registered trademarks, and are used only for identification and

explanation without intent to infringe.

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

Antonelli, Cristiano.

Routledge handbook of the economics of knowledge / Cristiano

Antonelli, Albert N. Link. – First Edition.

pages cm

Includes bibliographical references and index.

1. Knowledge management–Economic aspects. 2. Information

technology–Economic aspects. 3. Technological innovations–Economic

aspects. I. Link, Albert N. II. Title.

HD30.2.A5793 2014

658.4’038–dc23

2014018440

ISBN: 978-0-415-64099-2 (hbk)

ISBN: 978-0-203-08232-4 (ebk)

Typeset in Bembo

by Cenveo Publisher Services

v

Contents

List of fi gures vii

List of tables viii

List of contributors ix

1 Yet another measure of ignorance 1

Albert N. Link

2 Innovation and creativity: a slogan, nothing but a slogan 7

Benoît Godin

3 From knowledge to innovation: the role of knowledge

spillover entrepreneurship 20

David B. Audretsch, Erik E. Lehmann, and Joshua Hinger

4 Innovation strategies combining internal and external knowledge 29

Börje Johansson and Hans Lööf

5 Networks of knowledge: an appraisal of research themes,

findings and implications 53

Müge Özman

6 Academic networks and the diffusion of knowledge 79

Rajeev K. Goel, Devrim Göktepe-Hultén, and Rati Ram

7 Transversal or linear? Knowledge externalities and the complexity

of knowledge interactions 99

Phil Cooke

8 Knowledge cumulability and path dependence in

innovation persistence 116

Francesco Crespi and Giuseppe Scellato

Contents

vi

9 Social responsibility and the knowledge production function of higher

education: a review of the literature 135

Christopher S. Hayter

10 An alternative to the economic value of knowledge 154

Heather Rimes, Jennie Welch, and Barry Bozeman

11 The international dissemination of technological knowledge 165

Fabio Montobbio and Rodrigo Kataishi

12 The economic nature of knowledge embodied in standards for

technology-based industries 189

Gregory Tassey

13 Towards non-exclusive intellectual property rights 209

Cristiano Antonelli

14 The dynamics of knowledge governance 232

Cristiano Antonelli

Index 263

vii

1.1 U.S. Total Factor Productivity Index, 1948–2011 (2005 = 100) 3

3.1 Emergence and diffusion of the knowledge spillover theory of

entrepreneurship 26

4.1 Labour productivity cross-tabulated against output of firms in an industry

in productivity-descending order 33

4.2 The two basic types of firm knowledge 34

4.3 Renewal efforts generating new solutions and adding to firm capabilities 37

6.1 Types of academic networking 80

6.2 Knowledge flows through academic networking 81

7.1 Regional innovation governance model for the Algarve, 2007 105

7.2 RIS3 phase conceptual model of DUI/STI regional

innovation system (2013) 105

7.3 Centro’s STI/DUI hybrid regional innovation system 107

7.4 Regional innovation: DUI/STI ‘Sounding Board’ regime

and ‘transversality’ paradigm 109

12.1 Roles of standards in technology-based industries 194

12.2 The structure of industrial standardization 195

13.1 From knowledge non-appropriability to knowledge as a non-exhaustible input 211

13.2 Compulsory licensing in product markets 219

13.3 Substitutability range for a given level of Y 222

13.4 R∗ and optimal combination of productive factors 223

13.5 Cost and revenue function for α > ½ and complementary

productive factors, with Y < K n 224

Figures

viii

Tables

1.1 U.S. technology- and innovation-related policies initiated in the

post-productivity slowdown period 4

1.2 Description of U.S. post-productivity slowdown period technology

and innovation policies 4

4.1 Explaining the R&D engagements of firms 36

4.2 State transitions between years, 1998–2004 38

4.3 Construction of nine conjunction-variable categories 42

4.4 Classification of firms with regard to size of internal knowledge 43

4.5 Impact of the conjunction variable on a firm’s output (value added) 44

4.6 Impact of the conjunction variable on a firm’s TFP growth 45

4.7 Knowledge sources and export performance of local industries

2002 and 2006 47

4.8 Definition of six category variables 48

4.9 Productivity premium in percent associated with the six category variables 49

4.10 Year-to-year persistence in the same classification 49

5.1 Theoretical and methodological issues in network research 55

5.2 Summary of the debates 60

6.1 Knowledge transfer mechanisms 86

8.1 Main recent empirical contributions on innovation persistence 124

9.1 Models of corporate social responsibility derived from the literature 137

10.1 Churn theory of knowledge valuation in comparison 161

12.1 Functions of standards in knowledge-intensive industries 191

13.1 The combination of property and liability rules 217

14.1 From knowledge spillovers to knowledge interactions 239

14.2 Ingredients and mechanisms of the variety of knowledge

governance modes 245

ix

Cristiano Antonelli, University of Torino and Collegio Carlo Alberto, Italy.

David B. Audretsch, Indiana University, USA.

Barry Bozeman, Arizona State University, USA.

Phil Cooke, University of Cardiff, Wales.

Francesco Crespi, University of Rome III and Collegio Carlo Alberto, Italy.

Benoît Godin, INRS (Montréal), Canada.

Rajeev K. Goel, Illinois State University, USA.

Devrim Göktepe-Hultén, Lund University, Sweden.

Christopher S. Hayter, Arizona State University, USA.

Joshua Hinger, Indiana University, USA.

Börje Johansson, Jönköping International Business School, Jönköping, Sweden.

Rodrigo Kataishi, University of Torino, Italy.

Erik E. Lehmann, University of Augsburg, Germany.

Albert N. Link, University of North Carolina at Greensboro, USA.

Hans Lööf, Royal Institute of Stockholm, Sweden.

Fabio Montobbio, University of Torino, Italy.

Müge Özman, Telecom Ecole de Management, France.

Contributors

Contributors

x

Rati Ram, Illinois State University, USA.

Heather Rimes, University of Georgia, USA.

Giuseppe Scellato, Politecnico di Torino and Collegio Carlo Alberto, Italy.

Gregory Tassey, University of Washington, USA.

Jennie Welch, University of Georgia, USA.

1

1

Yet another measure of ignorance

Albert N. Link

Since we know little about the causes of productivity increase, the indicated importance of this

element may be taken to be some sort of measure of our ignorance about the causes of economic

growth in the United States and some sort of indication of where we need to concentrate

our attention.

Moses Abramovitz ( 1956 , p. 11)

The Abramovitz ( 1956 ) paper, from which the above epigram came, and the seminal work by

Solow ( 1957 ) are heralded by many as foundational reading for what Antonelli and Link ( 2014 )

have referred to as the area of investigation known as “the economics of technological change.”

Antonelli and Link ( 2014 , p. xiii) write:

The analysis of the causes and consequences of the increase of the general efficiency of labor

and the associated changes in production, consumption, and distribution brought about by

the introduction of new technologies in economic systems is a field of economic investiga￾tion of growing interest and widening activity both in research and teaching. This field has

evolved over time, partly in response to the changing focus of economic analysis. This area

of investigation was identified as “the economics of technical progress” for a large part of the

20th century. In the 1960s and 1970s, it was referred to as “the economics of technological

change,” and through the 1980s and 1990s it became known as “the economics of innova￾tion.” Since then a new shift occurred to bring to the attention of scholars “the economics of

knowledge” as a crucial crossing between the economics of science and the innovation. …

The discovery of the so-called residual, along with an appreciation of its size, pushed eco￾nomics to investigate more deeply the characteristics of the new technologies in terms of

factor intensity, elasticity of substitution, output elasticity, and technology diffusion. This

phase of academic understanding coincides with “the economics of technological change.”

The residual mentioned by Antonelli and Link ( 2014 ) refers to the residual calculated by Solow

( 1957 ). Implicit in the Solow model is a Cobb-Douglas production function written in terms of

output (Q), capital (K), labor (L), and time (t):

Albert N. Link

2

Q t = F K, L; ( ) (1)

And if technical change, that is “ any kind of shift in the production function” (Solow 1957 , p. 312),

is neutral then equation (1) becomes:

Q A f (K, L) = ( )t (2)

Mathematically it follows that:

Q /Q K /K L /L A /A ′ ′ ′′ − −= α β (3)

where Q′, K′, and L′ are time derivatives and α and β are relative shares. The residual, A′/A,

represents the percentage growth in output that is not attributable to the percentage growth of

inputs K and L. Following Solow ( 1957 , p. 320):

Gross output [Q] per man hour doubled over the interval [1909–1949], with 87½ per cent

of the increase attributable to technical change and the remaining 12½ per cent to increased

use of capital.

Conventionally interpreted, the lion’s share of the changes in output from 1909 to 1949 cannot

be explained in terms of changes in K and L inputs; thus, residually measured growth is for the

most part unexplained.

Decades of research by able scholars have been devoted to explaining the residual, that

is explaining what had previously been viewed to be merely a measure of ignorance. They

have accomplished this by identifying empirical correlates with either A′ or A′/A. What has

evolved is an empirical as well as conceptual understanding that research and development

(R&D) investment spending is a driver of so-measured technological change (Griliches, 1996 ;

Hall et al., 2010 ).

However, another measure of ignorance has, in my opinion, arisen, and it relates to the

effectiveness of public policies, be they U.S. policies or not, initiated with the intent of actually

stimulating R&D spending.

Consider Figure 1.1. It shows the movement over time in the total factor productivity (TFP)

index for the private business sector in the United States from 1948 through 2011. Simply, and

with reference to equation (1) above, the TFP index is calculated as Q/F(K, L, M, E, S), where M

refers to material inputs, E to energy inputs, and S to purchased business services.1

Two time periods are noteworthy in the figure; they are associated with two periods of

productivity slowdown that are particularly important for understanding the evolution of U.S.

technology and innovation policies. The first time period is from 1973 through 1974, and the

second time period is from 1978 through 1982.

The slowdown in measured productivity in 1973 and 1974 was presumed by many econo￾mists, and likely many policy makers, to be a result of non-recurring and periodic events, such

as the energy crisis of 1973. Many economists and policy makers in the United States thought

at the time that such events were normal, one-time cyclical shocks to the economy, and move￾ment in the TFP index was accordingly a normal cyclical response around a more stable, long￾term growth in productivity.

More important, however, from both an economic and a policy perspective, was the pro￾ductivity slowdown that began in 1978 and ended in 1982. In fact, in 1978 the Bureau of

Labor Statistics’ TFP index was 78.906, the highest it had been in the post-World War II

Yet another measure of ignorance

3

period. By the end of 1982, the TFP index was 74.401, only slightly higher that it had been

a decade earlier.

Many explanations have been offered for this precipitous and unprecedented decline in

productivity, or technological advancement, from 1978 through 1982 as summarized in Link

( 1987 ). For example, Link and Siegel ( 2003 , p. 58), reflecting the concern that the slowdown

was not a response to cyclical, one-time, and temporary shocks but rather due to a more funda￾mental and enduring change in long-term growth prospects, wrote:

In the early 1980s there was great concern among economists and policymakers in the

United States regarding the pervasive slowdown in productivity growth and the concomi￾tant decline in the global competitiveness of American firms in key high-technology indus￾tries. One of the alleged culprits of this productivity slowdown was a decline in the rate of

technological innovation, which is a reflection of declining entrepreneurship.

In response to these productivity declines, the U.S. Congress passed five major initiatives, as

shown in Table 1.1 and described in more detail in Table 1.2. Each of the initiatives was

intended to have a direct and/or indirect impact on private R&D activity under the implicit

assumption that investments in R&D drive technological advancement.

As an aside, it is interesting to point out that while the foundation for U.S. technology and

innovation policy has been traced by some to the five initiatives in Table 1.1 and Table 1.2, it

was not until 1990 that there was a formal technology policy statement for the United States.

Many point to President George H.W. Bush’s 1990 U.S. Technology Policy as the nation’s first

formal domestic technology policy statement. Albeit important as an initial formal policy state￾ment, it failed to articulate a foundation for government’s role in innovation and technology.

Rather, it implicitly assumed that government had a role, and then set forth the general statement

(Executive Office of the President, 1990 , p. 2):

The goal of U.S. technology policy is to make the best use of technology in achieving the

national goals of improved quality of life for all Americans, continued economic growth,

and national security.

120

1949

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985 1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

110

100

90

80

70

60

50

40

Figure 1.1 U.S. Total Factor Productivity Index, 1948–2011 (2005 = 100)

Table 1.1 U.S. technology- and innovation-related policies initiated in the post-productivity slowdown

period

Legislation Targeted

party(s)

Direct impact

on R&D

Indirect impact

on R&D

University and Small Business Patent

Procedure Act of 1980 (known as the

Bayh-Dole Act of 1980)

Universities

Private-sector fi rms

Yes

Stevenson-Wydler Technology Innovation Act

of 1980 (known as the Stevenson-Wydler

Act of 1980)

National laboratories

and other research

organizations

Private-sector fi rms

Yes

Economic Recovery Tax Act (ERTA) of 1981

(relevant portion known as the R&E Tax

Credit of 1981)

Firms conducting

R&D

Yes

Small Business Innovation Development Act

of 1982

Small fi rms (< 500

employees)

Yes

National Cooperative Research Act of 1984 Firms of all sizes and

their research

partners

Yes

Table 1.2 Description of U.S. post-productivity slowdown period technology and innovation policies

Legislation Description

Bayh-Dole Act of 1980

(Public Law 96-517)

The Act redefi ned property rights that facilitated the transfer of

existing knowledge resulting from public-sector support from

universities to the private sector.

Stevenson-Wydler Technology

Innovation Act of 1980

(Public Law 96-480)

The Act called for federal laboratories to actively promote

technology transfer to the private sector for commercial

exploitation. Each national laboratory was mandated to establish

an Offi ce of Research and Technology Applications to facilitate

this technology transfer.

R&E Tax Credit of 1981

(Public Law 97-34)a

The Act provided a 25 percent marginal tax credit, that is a

25 percent tax credit for qualifi ed R&E expenditures in excess of

the average amount spent during the previous three taxable

years, in an effort to increase private-sector R&D spending. The

marginal rate was later lowered to 20 percent.

Small Business Innovation

Development Act of 1982

(Public Law 97-219)

The Act created the Small Business Innovation Research (SBIR)

program to provide research grants to small fi rms for the

purposes of stimulating technology development and its

subsequent commercialization.

National Cooperative Research

Act of 1984 (Public Law

98-462)

The Act created a registration process under which joint research

and development ventures, or more simply research joint

ventures (RJVs), can voluntarily disclose their research intentions

to the U.S. Department of Justice and thereby gain partial

indemnifi cation from antitrust laws and penalties.

Note: a

Research and experimentation (R&E) expenditures are more narrowly defined than R&D expenditures, which

include all costs incident to development. R&E does not include ordinary testing or inspection of materials or products

for quality control of those for efficiency studies, etc. R&E, in a sense, is the experimental portion of R&D.