New Directions in Regional Economic Development (Advances in Spatial Science) 9783642010163, 3642010164

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Advances in Spatial Science Editorial Board Manfred M. Fischer Geoffrey J.D. Hewings Peter Nijkamp Folke Snickars (Coordinating Editor)

Charlie Karlsson Paul C. Cheshire

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˚ ke E. Andersson A Roger R. Stough

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Editors

New Directions in Regional Economic Development

Editors Professor Charlie Karlsson ˚ ke E. Andersson Professor A Jo¨nko¨ping University Jo¨nko¨ping International Business School Department of Economics Gjuterigatan 5 55318 Jo¨nko¨ping Sweden [email protected] [email protected]

Professor Paul C. Cheshire London School of Economics Geography & Environment Department Houghton St. London WC2A 2AE United Kingdom [email protected]

Professor Roger R. Stough George Mason University 4400 University Drive, MS 6D5 Fairfax VA 22030 USA [email protected]

Advances in Spatial Science ISSN 1430-9602 ISBN 978-3-642-01016-3 e-ISBN 978-3-642-01017-0 DOI: 10.1007/978-3-642-01017-0 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009926181 # Springer-Verlag Berlin Heidelberg 2009 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: SPi Publisher Services Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

This book is based on papers presented at an international workshop organised in Jo¨nko¨ping, Sweden, in June 2005 to celebrate the 60th birthday of Professor Bo¨rje Johansson – a dear friend and admired colleague of ours. The book provides a limited sample of Bo¨rje Johansson’s broad ranging research interests. In this volume, some of his friends and colleagues have contributed chapters on the theme of “Innovation, Dynamic Regions, and Regional Dynamics”. This is a field of research in which Bo¨rje Johansson has been a great inspiration to us all, and to which he him-self has contributed with characteristic enthusiasm and insight as part of his prodigious output. The workshop and the creation of this book were sponsored by the Alfa Savings Bank Foundation in Jo¨nko¨ping, Jo¨nko¨ping International Business School, and the School of Public Policy, George Mason University, Fairfax, VA. We thank them for their generous support. The authors and the editors thank Kerstin Ferroukhi for all her efforts to organise the workshop and Ulla Forslund-Johansson and Uma Kelekar for working tirelessly to get the papers refereed and revised, to put together multiple edits of this book and for preparing it for the publisher. It would have been impossible to produce this book without their dedicated work. Sweden Sweden UK USA

Charlie Karlsson Åke E. Andersson Paul Cheshire Roger R Stough

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Contents

1

Innovation, Dynamic Regions and Regional Dynamics . . . . . . . . . . . . . . . . . . . 1 ˚ ke E. Andersson, Paul Cheshire, and R.R. Stough Charlie Karlsson, A

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The Pure Theory of Spatial Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Martin Beckmann

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Smith–Ricardo Specialization in the Presence of Tiring Effects . . . . . . . . 47 Tonu Puu

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Dynamics of Innovation Fields with Endogenous Heterogeneity of People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Masahisa Fujita

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Economics of Creativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 ˚ ke E. Andersson A

6

Simple Memes and Complex Cultural Dynamics . . . . . . . . . . . . . . . . . . . . . . . . 97 David Batten and Roger Bradbury

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The Fashioning of Dynamic Competitive Advantage of Entrepreneurial Cities: Role of Social and Political Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Lata Chatterjee and T. R. Lakshmanan

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The Social Capital of Regional Dynamics: A Policy Perspective . . . . . . 121 Hans Westlund

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Hidden Order in Traffic Flows Using Approximate Entropy: An Illustration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Kingsley Haynes, Rajendra Kulkarni, and Roger Stough

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Regional Input–Output with Endogenous Internal and External Network Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 John R. Roy and Geoffrey J.D. Hewings

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Regional Unemployment and Welfare Effects of the EU Transport Policies: Recent Results from an Applied General Equilibrium Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Artem Korzhenevych and Johannes Bro¨cker

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Infrastructure Productivity with a Long Persistent Effect . . . . . . . . . . . 197 Tsukai Makoto and Kobayashi Kiyoshi

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Science Parks and Local Knowledge Creation: A Conceptual Approach and an Empirical Analysis in Two Italian Realities . . . . . . 221 Roberta Capello and Andrea Morrison

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The Low Participation of Urban Migrant Entrepreneurs: Reasons and Perceptions of Weak Institutional Embeddedness . . . . . 247 Enno Masurel and Peter Nijkamp

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The Location of Industry R&D and the Location of University R&D: How Are They Related? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Charlie Karlsson and Martin Andersson

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Growing Urban GDP or Attracting People? Different Causes, Different Consequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Paul Cheshire and Stefano Magrini

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Urban–Rural Development in Sweden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Johan Klaesson and Lars Pettersson

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Patents, Patent Citations and the Geography of Knowledge Spillovers in Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Manfred M Fischer, Thomas Scherngell, and Eva Jansenberger

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Co-authorship Networks in Development of Solar Cell Technology: International and Regional Knowledge Interaction . . . . . . . . . . . . . . . . . . 347 Katarina Larsen

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Off-shoring of Work and London’s Sustainability as an International Financial Centre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Ian Gordon, Colin Haslam, Philip McCann and Brian Scott-Quinn

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The Genesis and Evolution of the Stockholm Music Cluster . . . . . . . . 385 Pontus Braunerhjelm

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409

Contributors

˚ ke E. Andersson A Jo¨nko¨ping International Business School, Jo¨nko¨ping University, Ho¨gskoleomra˚det, Gjuterigatan 5, 553 18 Jo¨nko¨ping, Sweden, [email protected] Martin Andersson Jo¨nko¨ping International Business School, Jo¨nko¨ping University, Ho¨gskoleomra˚det, Gjuterigatan 5, 553 18 Jo¨nko¨ping, Sweden, [email protected] David Batten The Temaplan Group and CSIRO, CSIRO Marine and Atmospheric Research, Private Bag 1, Aspendale, Victoria 3195, Melbourne, Australia [email protected] Martin Beckmann Economics Department, Brown University, 64 Waterman Street Providence, RI 02912, USA, [email protected] Roger Bradbury Tjurunga and the Australian National University, 9 Scott Street, Narrabundah, ACT 2604, Canberra, Australia Pontus Braunerhjelm Department of Economics , The Royal Institute of Technology, Drottning Kristinas Va¨g 30, 100 44 Stockholm, Sweden, [email protected] Johannes Bro¨cker Institute for Regional Research, University of Kiel, Olshausenstraße 40, 24098 Kiel, Germany Roberta Capello Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Giuseppe Colombo 40, 20133 Milano, Italy [email protected] ix

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Lata Chatterjee Center for Transportation Studies, Boston University, One Sherborn Street, Boston, MA 02215, USA, [email protected] Paul Cheshire London School of Economics, Houghton Street, London WC2A 2AE, United Kingdom, [email protected] Masahisa Fujita Konan University, 8-9-1 Okamoto, Higashinada-ku, Kobe 658-8501, Japan [email protected] Manfred Fisher Institute for Economic Geography & GIScience, Vienna University of Economics and Business Administration, Nordbergstr. 15/4/Sector A, 1090 Vienna, Austria [email protected] Ian Gordon Geography Department, London School of Economics, Houghton St, London WC2A 2AE, United Kingdom, [email protected] Colin Haslam Center for Research in Finance and Accounting, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom Kingsley Haynes The School of Public Policy, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA, [email protected] Geoffrey J.D. Hewings REAL, University of Illinois, 607 S. Matthews, Urbana, IL 61801-3671, USA [email protected] Eva Jansenberger Institute for Economic Geography & GIScience, Vienna University of Economics and Business Administration, Nordbergstr. 15/4/Sector A, 1090 Vienna, Austria Charlie Karlsson Jo¨nko¨ping International Business School, Jo¨nko¨ping University, Ho¨gskoleomra˚det, Gjuterigatan 5, 553 18 Jo¨nko¨ping, Sweden, [email protected] Kobayashi Kiyoshi Graduate School of Management, Kyoto University, Yoshida-hommachi, Sakyoku, Kyoto, 606-8501, Japan, [email protected]

Contributors

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Johan Klaesson Jo¨nko¨ping International Business School, Jo¨nko¨ping University, P.O. Box 1026, 551 11 Jo¨nko¨ping, Sweden Artem Korzhenevych Institute for Regional Research, University of Kiel, Wilhelm-Seelig-Platz 1, 24118 Kiel, Germany, [email protected] Rajendra Kulkarni Senior Research Analyst, School of Public Policy, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA T.R. Lakshmanan Center for Transportation Studies, Boston University, One Sherborn Street, Boston, MA 02215, USA, [email protected] Katarina Larsen KTH - The Royal Institute of Technology, Valhallava¨gen 79, 100 44 Stockholm, Sweden, [email protected] Stefano Magrini Dipartimento di Scienze Economiche, University of Venice, Fondamenta S Giobbe Cannaregio, 873 30121 Venezia, Italy Tsukai Makoto Graduate School of Engineering, Hiroshima University, 1-1-1, Noji Higashi, Kusatsu, Shiga 525-8577, Japan Enno Masurel Centre for Innovation and Sustainable Entrepreneurship, Free University, Habelschwerdter Allee 45, 14195 Berlin, Germany, [email protected] Philip McCann Economics Department, University of Reading, Whiteknights, Reading RG6 6BA, United Kingdom Andrea Morrison Department of Economics, Universita` del Piemonte Orientale, Via Bellini, 25/G, 15100 Alessandria, Italy CESPRI, Universita` Bocconi, Via Sarfatti, 25 20136 Milano, Italy Peter Nijkamp VU University, Department of Spatial Economics, room 4A-33, De Boelelaan 1105 1081 HV Amsterdam, The Netherlands

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Contributors

Lars Pettersson The Swedish Board of Agriculture, Jo¨nko¨ping 551 82 Jo¨nko¨ping, Sweden, [email protected] Tonu Puu CERUM, Umea˚ University, 90187 Umea˚, Sweden, [email protected] John R. Roy ETUDES, 3 Scenic Ct, PO Box 96, Mallacoota, Victoria 3892, Australia [email protected] Thomas Scherngell Institute for Economic Geography & GIScience, Vienna University of Economics and Business Administration, Nordbergstr. 15/4/Sector A, 1090 Vienna, Austria Brian Scott-Quinn ISMA Centre, University of Reading, Whiteknights RG6 6BA, Reading, United Kingdom Roger Stough Vice President for Research and Economic Development, George Mason University, 4400 University Dr. MS3A2, Fairfax, VA 22030, USA Hans Westlund ¨ stersund, Sweden National Institute for Working Life, Studentplan 1, 831 40 O [email protected]

Chapter 1

Innovation, Dynamic Regions and Regional Dynamics ˚ ke E. Andersson, Paul Cheshire, and R.R. Stough Charlie Karlsson, A

1.1

Introduction

The development of economic theory after World War II has focused on clarifying the necessary and sufficient conditions for the existence of an idealized general equilibrium. Debreu (1956), Arrow and Hahn (1971), and Scarf and Hansen (1973) established these conditions, building on earlier attempts by Cassel (1917) and Wald (1933–34, 1934–35). A key assumption in the formulation and proofs of the existence of a general equilibrium of a competitive economy is a large (or even infinite) number of buyers and sellers (Aumann 1964), which ensures anonymous markets and mutual independence of agents. Another assumption is the convexity of preference and production technology sets (Uzawa 1962). A third assumption is flexible pricing of goods and production factors. The flexibility of prices is the assumption that economists first called into question. Keynes formulated the most influential early criticism of the realism of assuming flexible prices in his General Theory of Employment, Interest and Money (1936). In his macroeconomic analysis, Keynes questioned the downward flexibility of the price of labor services and interest rate (i.e., the price of loanable funds), implying the possibility of equilibrium without full employment. Later, Uzawa (1976) and Benassy (1975) included such Keynesian macroeconomic fixed-price assumptions in a new general equilibrium theory and proved the existence of an even more general class of equilibrium theorems that does not depend on complete price flexibility. Frank (1969) formulated the first successful attempt to relax the assumption of a convex production technology set. He proved the existence of a set of prices that can sustain both a structure of production in general equilibrium and increasing returns to scale. Andersson and Marksjo¨ (1972) extended Frank’s analysis by assuming

R.R. Stough ð*Þ Vice President for Research and Economic Development, George Mason University e-mail: [email protected]

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continuous increasing returns of the technology sets. In both studies it was shown that sellers of each good must price-discriminate between consumers in order to sustain a general equilibrium. One of the core characteristics of Bo¨rje Johansson’s research is the development of theories and models in which increasing returns to scale are compatible with economic equilibrium. Another characteristic is his questioning of the independence of economic agents. The starting point of his research on the consequences of agent interdependence was his doctoral dissertation defended in 1978; Contributions to Sequential Analysis of Oligopolistic Competition. That game theoretic study not only assumes statically interdependent agents as in prisoners’ dilemmas and other suboptimal equilibrium games, but also takes into account strategic interactions that are truly dynamic. Interdependencies among agents take on a deeper significance for applied work when agents are distributed in continuous space or on some discrete network. Such interdependencies were almost completely disregarded by American economists, with only a few exceptions such as Hotelling (1929), Chamberlain (1936), Isard (1956), and Greenhut (1971). In Europe, there is however a separate tradition of focusing on such interdependencies, as is exemplified by von Thu¨nen (1826), Alfred Weber (1929), Launhardt (1872, 1882), Palander (1935), Lo¨sch (1954), Beckmann (1952, 1956), as well as Beckmann and Puu (1985). The role of spatial interdependence in the determination of a spatial general equilibrium with assumptions of convex production technology and preferences has been most thoroughly developed in the contributions by Beckmann (1952, 1956) and Beckmann and Puu (1985). Building on this European theoretical heritage, Bo¨rje Johansson has explored spatial and dynamic interdependencies in models where the assumption of convex production technologies is discarded in favor of assumptions of internal and external increasing returns. He has also refocused the modeling of interdependencies toward explicit dynamic economic mechanisms, instead of the simple additions of time subscripts, which is typical of static theories and models. Bo¨rje Johansson superbly follows the theoretical advice formulated by Schumpeter: This distinction [between statics and dynamics] is crucial. Statics and dynamics are two totally different areas. Not only do they deal with different problems, but they use different methods and they work with different materials. They are not two chapters in the same theoretical construction – they are two totally different buildings. (Schumpeter 1908, pp. 182–183)

1.2

Innovations and Innovation Networks

Innovation is the fundamental factor behind the development and renewal of firms, markets, regions, and entire economies. According to Schumpeter (1934), an innovation can be a new (1) product, (2) production technology, (3) market, (4) organization or (5) input. We focus on the first three types of innovation, since they

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usually constitute the majority of innovations. Similarly to production and economic growth, innovations are always unevenly distributed across countries, regions, as well as across localities within regions.1 Spatial differences result from the unequal attributes of each location. Consequently, Johansson (1998a) calls such attributes location attributes. For each type of economic activity, one can identify certain combinations of location attributes that support it better than other combinations. Some location attributes are gifts of nature, while others are created by investments in physical and human capital with low spatial mobility. Still others are the result of the behavior of economic agents with spatial preferences, such as households or firms. Standard economic theory has devoted little attention to regional differences concerning location, innovation, productivity, and growth. Research with a regional focus has therefore been forced to create its own platform and conventions, which specify relevant and challenging research problems. It is possible to identify a selforganized research program in Sweden, which is based on the work of economists, geographers and other regional scientists since the early 1950s. The inspiration for that research program harks back to the interwar period and the contributions by, in particular, Ohlin (1933) and Palander (1935) (Johansson 1998a). One economist and regional scientist who has played a central role in the research program since the 1970s is Bo¨rje Johansson. This introductory chapter has as its main purpose to provide an overview of his engagement with – and contributions to – the research field within spatial economics that deals with innovation, regional specialization, and dynamic systems of functional regions. One way to understand and analyze innovation processes is to study the increased formation of economic networks among producers, subcontractors, and buyers of final products (Johansson 1990b; Johansson and Westin 1994). Such networks consist of nodes and links (Karlsson et al. 2005). Johansson (1991b) outlines some of the fundamental elements of the emerging theory of economic networks by providing an economic model which explains the creation of linkages and networks, and which also attempts to explain the durability of such relations. The network approach recognizes the importance of repeated mutual investments in the links that connect customers and suppliers (Johansson 1990a; Teubal and Zuscovitch 1994). Investments in links between suppliers and customers create and expand networks. The amount of investment that is required to establish and strengthen a link between two economic agents is a negative function of the existing affinities between the two nodes and a positive function of the spatial friction. The dominant flows of a specific product or type of information will use links, which have the most appropriate attributes, while at the same time being constrained by barriers and other types of friction.

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Since people and firms are highly concentrated in space of course we would not expect innovation to be randomly distributed across space. The problem is that we need a priori to formulate a null hypothesis about, what would constitute an ‘‘even distribution’’ (see Glaeser and Ellison 1997; Duranton and Overman 2005).

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The links in an economic network must be analyzed as immobile capital goods, which have incurred sizable sunk costs. Existing linkages therefore impose rigidity and inertia on firms’ interaction patterns such as trade flows, deliveries of current inputs and capital equipment, and exchanges of technological knowledge. Normally, a link between a supplier and customer will not be broken unless a new supplier can offer a new input, which is clearly superior to the current input, since the new supplier has to overcome the sunk cost advantages of an established link. Emphasizing the network aspects of the economy, rather than using the traditional price-oriented view of the market, implies that link attributes increase in importance relative to node attributes as explanations of trade patterns, service networks, spatially distributed production networks, and innovation networks. The archetypical model of a market economy with independent actors, in which a quantity of a product is bought from the seller who offers the lowest price at the point of delivery, focuses in a way upon production costs in nodes and, rarely, if at all, on transport and transaction costs. Thus, it disregards the dynamic interplay between market actors, which is not only typical of the market but also shapes its development trajectory.

1.2.1

Innovation Networks

Innovations never occur in splendid isolation. Instead, it is natural to describe product development and renewal of production processes as a natural part of the interaction between a firm and its customers and suppliers through its customer and supplier networks (Johansson 1993a). As part of its research and development, a firm also buys R&D results and knowledge support through its network of knowledge channels. The opportunities for an individual firm to improve its production process are dependent upon the conditions for buying new equipment and new knowledge from the suppliers in the firm’s supplier network. Suppliers of new techniques and sellers of new equipment frequently try to use established economic networks as a means to access potential technology customers (Johansson 1991b). This is why networks within large corporations often function as arenas for innovation diffusion (Karlsson 1988). Established networks have two distinct roles. First, the seller of technical systems and production knowledge must supply products, which are either designed specifically for each customer, or which can be adapted to fit the demands of the buyer. Hence, the seller needs existing links as channels through which it is possible to find customers, who also have sufficient purchasing power to pay for the necessary customization. One should emphasize that the customers are, in fact, carrying through their own innovations – although a lot of imitation may be involved. Second, the delivery of new equipment and installation of new systems are processes that frequently take a long time to perform and require frequent interactions between the delivering and receiving firms. Both parties need a reliable link for their co-production, which may include joint development and learning.

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Firms also receive knowledge about how its products ought to be redesigned through information from their customer networks. In addition, many firms have specialized knowledge links, which were created to generate better conditions for research and development within the firm. Thus, we can talk about innovation networks as a sub-structure of a firm’s general economic network. Of course, the strength of the innovation network varies among firms due to factors such as size, age, and industry. We may combine the above observations into a model of innovation behavior in economic networks: l

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Established networks for economic interaction are important vehicles for the diffusion of technological solutions. The delivering and receiving parties make contact via direct and indirect links in such networks. The networks therefore facilitate the transmission of knowledge. Networks may play this role regardless of their initial use and rationale. The ability of a firm to improve its production, distribution and other techniques depends on its capacity to build new links to suppliers of knowledge and equipment. Network formation is equally important for a firm that tries to establish cooperative ventures with other firms in order to renew and develop products.

Knowledge plays a critical role in innovation processes. Karlsson and Johansson (2006) argue that it is meaningful to make a distinction between three types of knowledge: l

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Scientific knowledge consists of basic scientific principles that can form a basis for the development of technological knowledge. Technological knowledge comprises implicit and explicit blueprints in the form of inventions (or technical solutions) that may be transformed into new products or production processes. Entrepreneurial knowledge consists of economic knowledge about potentially profitable entities such as products, business concepts, markets, customers, and suppliers.

The different types of knowledge flow from ‘‘sources’’ to ‘‘sinks’’ using links in different types of knowledge networks.

1.2.2

Knowledge ‘‘Sources’’ and Knowledge ‘‘Sinks’’

Links that connect nodes are the conduits for flows in networks. The direction of a flow is always from a ‘‘source’’ to a ‘‘sink’’. If the flow represents an economic transaction, the ‘‘source’’ is a supply node while the ‘‘sink’’ is a demand node. The concepts ‘‘source’’ and ‘‘sink’’ include but are not limited to ‘‘supply’’ and ‘‘demand,’’ and are general starting points for analyzing transmissions of knowledge and experiences among individuals, organizations, and over space.

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Scientific knowledge is disseminated in open scientific networks with universities and research institutes as permanent ‘‘sources’’ and with courses, conferences and scientific publications as links to the ‘‘sinks,’’ which are students and scientists as well as firms that are interested in transforming scientific knowledge into inventions and innovations. Technological knowledge includes knowledge about production methods as well as technical solutions about the design and construction of goods and services. Technological knowledge usually differs from scientific knowledge in that intellectual property rights in the form of patents and copyrights prevent general use of the knowledge. This implies that new technological knowledge is traded for a price or – if the knowledge creating firm decides to use it as a strategic resource – is simply unavailable. As a technology ‘‘sink’’ we can imagine a firm with an intention to start new production or to improve on its current production methods. To make this possible, it needs two types of technological knowledge: (1) knowledge about alternative designs of the planned product, which is the same as knowledge about product outcomes, and (2) knowledge about available production technologies or processes for producing the product. There are many ‘‘sources’’ of new technological knowledge. They include the firm’s own experiments, surveys of and contacts with customers, imitation of other firms’ technological knowledge, purchases of patents and licenses, employment of other firms’ employees and new university graduates, as well as in some cases industrial espionage. Technological knowledge is transmitted from ‘‘sources’’ to ‘‘sinks’’ in three ways: l l l

As individuals (human capital) As books or software (information) As equipment (physical capital)

When new technology is embodied in individuals, technology transfer takes place when individuals move from one organization to another or when individuals from different organizations come together in face-to-face meetings. After technological knowledge has ‘‘matured,’’ knowledge workers may codify and transfer it by using drawings, software, and texts or by structured education. When firms buy patents and licenses, they buy codified knowledge. The third form of technology transfer emerges when a firm buys physical capital such as technical equipment or machines, which embody new technological knowledge. It is not unusual for technology transfer to be a complex process, which may involve a combination of hiring individuals which embody critical human capital, training, acquisition of patents, and acquisition of capital goods. The third type of knowledge – entrepreneurial knowledge – is also critical for innovation processes. It includes knowledge about the demand for products with varying characteristics and the willingness among customers to pay for such products. Entrepreneurial knowledge also includes knowledge about competitors such as their strategies to attract various types of customer. The ‘‘sources’’ of knowledge are customers and competitors, both actual and potential. The links

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are whatever connects a producer with its customers and competitors, such as information and transport networks. Inventions and innovations are acts of creation2 with elusive ultimate causes. It is difficult to go beyond the distinction between inventions and innovations. An invention is the solution to a technical problem. To transform the invention into an innovation it is necessary that the innovator expects the technical solution to be economically viable. Economic viability is determined by production costs (including development costs) and revenue generated from the potential customers. Innovation processes often involve a combination of developing new production methods and new product characteristics. However, there are innovations that only introduce new production methods for producing existing products without any new characteristics, and there are also innovations that only concern the introduction of new product attributes with negligible process innovation. Maillat et al. (1993) distinguish between three types of product innovation. The most modest as well as the most common type entails the incremental addition of new elements to an already existing product. In this case, the aim may be to make the product more reliable and versatile. A transformation of the functionality of the product implies a more far-reaching product innovation. Now the product not only fulfills the needs of customers better, it offers new and unexpected functions. Most radical are those innovations that not only create new functions but also new markets. During the post-war period, many studies analyzed the innovation intensity of firms by measuring their patent frequency. These studies have been conducted even though there is a general agreement that patents only reflect a small part of all innovations. One question that has interested many economists is the extent to which the market and developments on the demand side stimulate product innovation, and to what extent the internal forces within companies together with the technological conditions for each product group generate new products. A large study by Scherer (1984) relates patent frequencies to: l l l

The size of the market for a firm. Differences in technological opportunities for different kinds of goods. The renewal readiness of the individual firm. In Scherer’s study the market explains a little more than 40%, technological opportunities explain about 30%, and the individual renewal readiness explains a little more than 10% of the variability in patent frequencies.

Energy and skills in knowledge ‘‘sources’’ and knowledge ‘‘sinks’’ govern the diffusion of technological knowledge (Johansson 1993a). The diffusion of knowledge and technology does not depend on the volume and intensity of the flow from the knowledge ‘‘source.’’ Technology transfer also results from the demand from

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The same is true of new scientific knowledge.

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the knowledge ‘‘sink.’’ The implication is that innovation networks primarily contain links between strong knowledge ‘‘sources’’ and strong knowledge ‘‘sinks.’’

1.2.3

Cost and Innovation of Product Characteristics

It is common in analyses of innovation and technology diffusion to make a schematic distinction between innovations that focus on improving production techniques (i.e., process innovation) and those that focus on improving existing products or introducing totally new products (i.e., product innovation). Conventionally, process innovation denotes all changes of production techniques that are used in the production of a given product in a given firm. However, the term process need not exclusively imply a narrow conception of technology but may also imply ‘‘non-technological’’ activities in a firm (Fischer and Johansson 1994). This more inclusive interpretation of ‘‘process’’ corresponds to its use by Nelson and Winter (1982). They argue that a firm embodies a set of interdependent production routines, which combine to form a complex process. Nelson and Winter’s definition implies that a complex production process includes the following sub-processes: l l l l

Distribution Production Routine design and construction Management, administration and commercial activities

Improvements to any of these sub-processes are process innovations. They primarily refer to changes that lead to more efficient resource use, which, for example, reduce production or distribution costs. In such cases, process innovation equals cost-reducing technical changes. But process innovation also includes those changes in the production processes which increase a product’s quality and reduce the proportion of defects, while preserving the original functions of the product. Process innovations are therefore all innovations that are not product innovations. What is then a product innovation? To answer this question we need a systematic way of describing products. Lancaster (1971) offers one such approach. He suggests a product description, which specifies the various attributes that characterize the product. He calls the attributes ‘‘characteristics’’ and assumes that it is possible to measure the quantity of each such product characteristic. As a consequence, each good or service becomes a specific combination of characteristics. Lancaster’s approach is closely related to Schumpeter’s analysis of innovation. Schumpeter (1934) treats innovation as the result of a process of new combinations. When a firm develops a new capital good, we can distinguish between two cases. In the first case, the firm intends to use the good itself and will for that reason attempt to prevent competitors from learning about it. In the second case, the firm will market the new good with the goal of making a profit. The goal is thus to find as

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many customers as possible with a sufficient willingness to pay for the new capital good with its various attributes. In this case, the firm has made a product innovation. When the buyers of the new capital good start using it in their production process they are making a process innovation.3 A need to cut production costs usually causes a firm’s efforts to improve its production process. This need is most obvious and persistent for products that are exposed to price competition from rival producers. The impetus to improve the efficiency of the production process recurs every time a competitor has succeeded in improving its production methods, and it also recurs at the onset of each cyclical downturn. The ability to manage continual improvements to the production process requires a continuous supply of new technology in the form of new technological knowledge. This includes imitating rivals, taking up suggestions from consultants and suppliers, and adapting information that has been gathered through the firm’s intra-regional and inter-regional innovation networks. We should also note that there are interdependencies between product and process innovation. For mature products, there is often a choice between old and new production processes, but new products normally require new production processes.

1.2.4

Innovation at the Industry Level

At the industry or sector level, economists study both product and process innovation as entry and exit processes (Johansson 1987; Johansson and Holmberg 1982). This approach builds on an important insight in Schumpeter’s theory of economic development, which is that the original entrepreneurs receive a premium in the form of greater profits for being pioneers (Schumpeter 1934). This ‘‘extra profit’’ to innovators is a temporary monopoly, which results from the specific knowledge that they do not (yet) share with their competitors or only share with a few of them. Irrespective of whether one assumes that such innovations occur continuously or continually and irrespective of the character of the imitative diffusion process, one should expect an uneven distribution of productivity and profits among the firms in an industry. We should expect pioneering entrepreneurial firms to earn greater profits and have greater productivity than their imitating followers. Empirical data confirm that economic rewards are ‘‘Schumpeter-distributed,’’ and that such distributions have a characteristic form (Johansson and Marksjo¨ 1984; Johansson and Stro¨mquist 1981). Moreover, not only does the general form of such reward distributions persist in each industry, but the specific parameters of the distributions exhibit long-term stability. 3

This implies that product innovations in one industry often show up as process innovations in one or several other industries. What is a product or a process innovation depends upon the perspective taken in the analysis. In the case of consumer goods there is normally no need to make this type of distinction.

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For innovations among firms in an industry, it is important to observe that innovations appear in two distinct forms: each firm may renew its production technique, but it may also adjust its old technique in order to develop new products. One may use Lancaster’s (1971) approach to analyze the effects of introducing new products. It is possible to combine the substitution of new for old products with the dynamic substitution of new for old production techniques. The dynamic processes of entry and exit generate specific distributions of process and product vintages that are associated with observable profit and productivity distributions. Different assumptions about the entry and exit dynamics generate different forms of the productivity and profit distributions in an industry. Product changes with logistical substitution processes explain the steepness of empirically observed productivity and profit distributions. In the absence of product evolution, technical change generates productivity and profit distributions, which are quite flat.

1.3

Regional Specialization

In the previous section, we analyzed innovation processes from the perspective of the firm, without considering the fact that innovation processes tend to locate in certain regions, in particular, large urban regions. In this section, we turn to the question of which factors determine the specialization of regions. Before considering these factors, however, we need to consider what a region is. In a functional economic region, one can identify one or (often) several spatial economic nodes, for example population centers, which physical infrastructure networks and established economic interaction networks jointly connect (Johansson 1993a). Of special importance are labor market networks, where the links between employees and employers create a tentative structure. Every employment relationship presupposes a contract, which also (indirectly) connects a dwelling to a workplace. A region’s accessibility patterns decide how these contract links generate geographically contiguous labor markets of various sizes. The links in the labor market constitute one of many networks, which integrate a regional economic system. Another such network is the communication network which job-seekers use to find suitable jobs and employers use to find workers with suitable skills. A functional economic region becomes an integrated economic system through the interaction, which takes place in established networks and includes communication, decision-making, and distribution of goods and services. A functional region has greater mobility of production factors within its interaction borders than with areas outside. Commuting and all other forms of interaction, even within functional regions, give rise to interaction costs. The size of these costs determines the spatial extent of the region. The heterogeneity of natural conditions and historical development paths means that functional regions differ from one another in their economic milieus, and thus

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offer dissimilar conditions for economic specialization. The regional economic milieu comprises those location attributes that are durable (fixed or slowly changing), that the individual firm cannot control, that are not traded other than as land attributes, and that influence firms’ production activities (Johansson 1998a).

1.3.1

The Infrastructure as a Set of Durable Location Attributes

A special type of durable location attributes is that part of the built environment in a region that qualifies as material infrastructure. The material infrastructure is durable capital that generates location attributes services, which influence the regional economic milieu (Johansson and Snickars 1992). It comprises of three parts: networks that convey people, goods, and messages; facilities that supply public goods; neighborhoods that provide access to housing and workplaces. Johansson (1991a) maintains that one may envisage the infrastructure as a landscape of interaction possibilities for resource flows as well as inter-personal and inter-firm contacts. Infrastructural changes are slow in comparison with the fast adjustments of most social and economic activities, which mean that in the short term the material infrastructure provides an arena for rapidly changing social and economic processes. The material infrastructure supplies services to a collective of users, but the spatial extent of the services is limited. It satisfies at least one of the three following criteria (Johansson and Snickars 1992): polyvalence; inter-temporal consistency; a systemic or network function that generates accessibility. It is also possible to identify a non-material regional infrastructure that consists of collective, durable, and relatively immobile location attributes, for example agglomerations of human capital and regional institutions (Andersson 1985). For both the material and the non-material infrastructure, the slow time scale is essential. The durability of location attributes implies that the allocation of other more mobile production factors has sufficient time to adjust to persistent spatial differences (Johansson 1998a). Johansson (1993b) recognizes that the material infrastructure, with its associated networks, functions as a set of systems for economic interaction. He claims that the development of prototypes, the adaptation of novel products, and the routine processing of mature products each constitutes a distinct type of activity. Each such type has specific interaction characteristics and needs. Thus, each type demands particular combinations of infrastructure attributes from its regional economic milieu. A network is an infrastructure, which facilitates interaction within and between regions. The interaction between intra-regional and inter-regional networks determines the long-term evolution of spatial economic systems (Johansson 1993b). Intra-regional networks make it possible for economic actors to benefit from the proximity of dense urban structures and to develop and restructure interpersonal networks. Such development and restructuring of links between economic partners and between buyers and sellers constitute the most basic mechanism for the

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evolution of every market. These link-shaping activities are almost exclusively hosted by urban environments with suitable infrastructure attributes (Johansson 1989a). They are investments in more or less durable links for communication and the exchange of information and knowledge, where the formation and maintenance of the links require personal face-to-face contacts. Frequent contacts require appropriate intra-regional and, in particular, urban infrastructure. Such infrastructure combines accessibility in local networks with a dense environment of meeting places and multi-purpose facilities. A productive urban economic milieu offers a variety of opportunities for personal contacts among people with diverse experiences, competences and skills (Johansson 1993b). In a city with general and polyvalent characteristics, maturing activities often migrate to peripheral parts of the city region, while new activities benefit from a central location. Production that benefits disproportionately from a certain location can force out other activities by offering higher land rents. In this way, new and alert economic actors can use the same infrastructure over and over again. This implies that the market does not treat the infrastructure as a sunk cost. The urban infrastructure instead displays ‘‘hotel attributes’’ (Johansson 1993b).

1.3.2

Regional Economic Milieus and the Economic Specialization of Regions

The dynamic processes that over time reshape a region’s economic milieu are driven on the one hand by external forces, and on the other hand by adjustment, development and investment processes within the region. The dynamics of these processes are often extended in time, due to the inertia associated with the transformation of regional resources. This inertia gives functional regions their identity and implies that their economic structure only changes gradually and at a slow pace. The economic milieu of functional regions influences economic agents and their behavior in three ways: l

l

4

The production capabilities of regions differ between industries. This implies that a specific set of infrastructural location attributes influences the productivity and cost structure of firms in a non-uniform fashion. (Johansson 1998a).4 The attractiveness of regions regarding different activities, for example the inand out-migration of households and firms, and the expansion and contraction of firms (Johansson 1998a).5

In Johansson (1993c), a quasi-dynamic model is applied to estimate how the economic milieu in municipalities influences the production in different manufacturing industries (see also, Johansson et al. 1991; Johansson and Karlsson 1994; Forslund and Johansson 1995). 5 The study by Holmberg and Johansson (1992) indicates, for example, that service sectors, such as wholesale, transportation, consulting and financial services are concentrated in municipalities in which the infrastructure facilitates interpersonal contacts and mobility.

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The innovative capabilities of regions, such as the creation of new knowledge, inventions, and innovations.

Regional scientists have employed two types of models to explain location patterns and regional specialization, both of which can be extended to include dynamic change processes. The first type consists of models with a central place system. Central place models focus on demand-driven specialization, in the sense that regions that are large and dense can host a richer variety of output than smaller and sparser regions (Beckmann 1958, 1996; Tinbergen 1967). In such models, it is the size of the set-up costs for each product that determines the size a region’s market area which a product must have. If the market area is too small, the region will not host the activity in question. At a given point in time, it is possible to identify products, which are only produced in those regions where the regional demand is large enough. The location advantages offered by a region’s economic milieu may also determine its specialization. Location advantages are relative characteristics of regions. It is only possible to evaluate a region by comparing the location advantages offered by different regions. Every functional region’s profile of location advantages has its basis in the region’s relative supply of resources. Lasting location advantages can only derive from resources that are immobile and change slowly. This builds on the assumption that it is possible to classify economic adjustment processes according to their speed (Johansson 1985; Johansson and Karlsson 1987). Johansson (1989b) presents results from mathematical models of dynamic systems, with the aim of identifying the importance of separating processes that operate on significantly different time scales. The formation of a network infrastructure and network flows constitute, a slow and a fast process, respectively. In the second type of model – location advantage models – the relative supply of trapped resources determine the specialization patterns of regions in a multi-regional system (Johansson and Karlsson 1987; Johansson 1997). The assumption of trapped resources has long been important for explaining regional specialization and trade within a Heckscher–Ohlin framework (Ohlin 1933). Certain economic activities use natural resources, which producers have to extract or harvest within the region of production. A standard location advantage model will predict where, among available regions, such resource production will take place (Moroney and Walker 1966; Smith 1975). Location advantages are not limited to the spatial distribution of natural resources, but also include various localized (i.e., regionally trapped) non-land production factors, such as infrastructural and human capital. These resources are not as immobile as natural resources but their potential relocation (‘‘speed of adjustment’’) is slow relative to other economic adjustment processes. A starting point for analyzing how location advantages influence regional specialization is that at each point in time, the various types of trapped resources are unevenly distributed over functional regions. Moreover, certain trapped resources are highly concentrated in functional regions with specific characteristics, such as their positions in networks for communication and transportation. If we assume that the spatial density of certain trapped resources is changing at a much slower pace than technology, it is possible that a technological change induces a relocation of

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production and a corresponding change in interregional trade patterns. As a consequence, slowly adjusting resources govern the emergence of new patterns of regional specialization. The structural economic development in a system of functional regions is the outcome of various interlinked adjustment processes that operate at different time scales. Both the central place and the location advantage approach stress the role of durable regional characteristics. Central place models focus on the accessibility to local and external markets, while location advantage models focus on durable trapped characteristics. Nevertheless, both these types of traditional models have limited explanatory power. If we assume durable regional characteristics as the only explanation of trade patterns, it becomes impossible to explain why regions that produce an almost identical set of goods trade with each other. The traditional approaches are also unable to explain how the behavior of economic agents may change the specialization of regions.

1.3.3

Spatial Transaction Costs and Endogenous Specialization

By combining assumptions about internal market potentials, increasing returns and spatial transaction costs, Johansson and Karlsson (2001) provide a framework for analyzing the endogenous specialization of functional regions. Both internal and external economies of scale can generate increasing returns. External economies of scale (i.e., agglomeration economies) consist of localization economies and urbanization economies. Localization economies are specialized external economies of scale, and are common in both large and small functional regions. An abundance of general positive supply externalities cause urbanization economies, and they are therefore associated with large urban regions (Vernon 1960). While large regions can specialize in diversity, Johansson and Karlsson (2001) argue that localization economies provide an opportunity for small and mediumsized functional regions to develop competitive specialization clusters, even though the internal market potential of such regions is much smaller than that of a large metropolis. They therefore elaborate on the role of internal and external scale economies in combination with product-specific spatial transaction costs in the economic development of small and medium-sized functional regions. Spatial transaction costs comprise both transportation and general transaction costs, which vary with the geographical distance between seller and buyer, and the properties of each specific spatial interaction link. Using the two concepts of functional (urban) regions and spatial transaction costs as their starting point, Johansson and Karlsson employ the following assumptions in order to generate a framework for analyzing endogenous regional specialization: l

The overall pattern of spatial transaction costs delimits functional regions. For contact-intensive transactions, the spatial transaction cost level is much higher across than within regions.

1 Innovation, Dynamic Regions and Regional Dynamics l

l

l

15

A region’s population size and total purchasing power determines its internal market potential. Internal and external markets make up the total market potential of a functional region. Networks for trade and other economic interactions connect each functional region to its external markets. The interaction intensity varies across such networks, and makes it possible to identify a hierarchy of sequentially widening transaction areas for each region, so that transaction costs rise in a stepwise sequence. A region’s location of activities and specialization is a process, which is influenced by two basic conditions: technology and scale effects; and durable regional characteristics.

Using this framework, Johansson and Karlsson (2001) explain internal and external scale economies theoretically, by showing how these phenomena combine and interact to generate cumulative specialization processes in functional regions. In particular, they focus on the specialization of small and medium-sized regions. An insightful contribution is their development of the spatial transaction cost concept, which is essential for understanding both the specialization opportunities of regions of different sizes and scale-based specialization. In relatively small regions, they show that the development of localization economies is indispensable in the absence of natural resource endowments. Combinations of three phenomena cause external scale effects: specialized labor markets, specialized neighborhood firms, and information spillovers. The first two phenomena give rise to intra-market effects, whereas information spillovers among firms are collective extra-market effects. They also illustrate how it is possible to order contact-intensive goods and services with respect to their dependence on the size of the internal market potential. Generally speaking, the flow intensity of longdistance inter-regional trade drops discontinuously at the borders of affinity-classified transaction areas, where such borders act as affinity barriers. It is important to observe that spatial transaction costs do not remain constant over time. A general development path is the seemingly unlimited extension of markets until they become global. Two network phenomena explain this (Hacker et al. 2004): The first phenomenon, which usually involves multinational corporations, is the development of economic links that allow transactions to be carried out over long distances at reduced cost. The second associated phenomenon is the development of networks for conveying information, services, goods, and people. The evolution of such networks reflects ambitions of making transactions less distance-sensitive (Andersson 1986). External economies play a key role in current explanations of location advantages and regional economic specialization. However, the literature is not always unambiguous in its use of this concept. Johansson (2005) suggests that it is possible to avoid such ambiguity by making three distinctions: the ‘‘source’’ of the externality (proximity versus network externalities); the economic nature of externalities (pecuniary versus non-pecuniary externalities); and the consequence of the externalities (efficiency versus development externalities).

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1.3.4

Combining Resource-Based and Scale-Based Models of Regional Specialization

The discussion of regional specialization in the preceding sections has focused either on resource-based or scale-based specialization. However, Holmberg et al. (2003) shows that it is possible to combine resource-based and scale-based assumptions into an integrated theoretical framework of endogenous regional specialization and growth. They do this for each sector in the regional economy by associating resource-based advantages with input-market potentials and scale-based advantages with customer-market potentials. Input-market and customer-market potentials tend to vary with the economic size of functional regions. This makes it possible to combine resource-based with scale-based regional specialization and growth processes. Modern resource-based models emphasize the supply of knowledge-intensive labor as a primary specialization factor. Thus, Holmberg et al. (2003) focus on the interaction between population changes and the development of economic activity in functional regions, paying special attention to the knowledge intensity of the labor force. This includes labor location dynamics relating to housing and job opportunities as well as the supply of household services. A major concern is to combine two conflicting assumptions, which are: l l

People follow jobs Jobs follow people

Holmberg et al. (2003) assume the self-generating processes that change regional specialization over time to have the form of interdependent dynamic processes that involve economic activities and the population size. The literature contains a number of empirical models that emphasize the exact form of the dynamic interdependence (Mills and Carlino 1989; Holmberg and Johansson 1992; Johansson 1996). In this theoretical framework, the infrastructure for interaction functions like an arena that links resource-based and scale-based models of regional specialization. The market potential of a firm refers to its accessibility to customers and input suppliers, including suppliers of labor services. The infrastructure facilitates the development and growth of the market potential as well as its density.6 The location factors for households include accessibility to jobs, household services, and amenities. Again, the same infrastructure helps to create accessibility and density. A basic idea in this approach is that not only physical infrastructure but also market potentials are slowly adjusting variables. Holmberg et al. (2003) illustrate how a set of self-reinforcing processes contributes to the growth (decline) of the market potential of a region that is experiencing a process of endogenous change. The development of firms interacts with the development of customer-market potential, input-market potential, and 6

A number of recent empirical studies illustrate the importance of ‘‘economic density’’ in functional regions (Ciccone and Hall 1996; Johansson 1996; Johansson et al. 1998; Karlsson and Pettersson 2005).

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labor-input market potential. Households interact with job-market, housing-market, and consumption-market potentials. The input-market labor-input-market potentials are core variables in resource-based models of regional specialization and growth. The customer-market potential refers to the opportunities of firms to benefit from both internal and external scale economies. The job-market potential is a measure of the friction households with a given location face when they search for jobs with acceptable commuting conditions. A combination of large job-market and housingmarket potentials increases a household’s opportunities of finding an efficient match between its job and housing locations. The size of the consumption-market potential determines the opportunities for households to benefit from variety in consumption. What can these self-reinforcing processes look like? We can imagine a functional region whose market potential has increased due to improvements in the transportation infrastructure. This will stimulate firms with internal scale economies to locate in the region and existing firms to expand their activities. Inmigration of firms to the region and expansion of the region’s native firms will increase the market potential of the region, generating further in-migration and expansion. As production grows the cost per unit of output falls, due to scale economies. This allows the price of interregional exports to fall, which stimulates growing export flows. In such a process, the external market potential grows as a share of the total market potential. When firms with similar activities locate and expand in the region they generate external economies, which induce more firms in the same industry to locate and expand in the region. A growing demand for inputs stimulates input suppliers to locate and expand in the region as long as their deliveries are distance-sensitive, which in turn stimulates the in-migration and expansion of customer firms. A growing demand for inputs increases the opportunities of input suppliers to take advantage of their internal economies of scale but also to develop their own external economies. When the internal market potential expands this may induce falling output prices, which in turn further stimulates exports to other regions. In this way, the external market potential increases its impact on the cumulative growth trajectory. The demand from export markets may also by itself generate self-reinforcing growth (Johansson and Lo¨o¨f 2006). What about the location of labor? The assumption here is that functional regions with attractive location characteristics for consumers attract households, especially households with a lot of human capital. A region’s attractiveness depends on the infrastructure, which comprises the region’s housing market and the accessibility from dwellings to the supply of household services, the supply of amenities of various kinds, and job opportunities (i.e., to household market potentials). This implies that regional labor markets must increasingly adjust through a process where firms follow the location of the supply of labor, rather than the opposite. The location of households and jobs forms a self-reinforcing dynamic process. The process is affected by the formation of regional infrastructure, which gradually improves or deteriorates, from the economic actors’ points of view. Naturally, the job-location process partly shapes the economic milieu. However, the assumption is that the infrastructure changes at a much slower pace than the location of jobs. In

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the short run it is therefore possible to treat the infrastructural characteristics as approximately fixed. The same argument applies to the relation between location characteristics and the dynamics of household location. The overall regional change process is dynamic in which jobs and households mutually adjust to each other. This formulation is in sharp contrast to the well-known export-base model. According to that model, economic activities have fixed locations while the labor supply of households adjusts to the demand for labor through a process where households follow jobs.

1.3.5

Economic Specialization in Small and Large Regions

When analyzing a functional region and its location advantages, it is useful to make a distinction between two dimensions as in Table 1.1. The table highlights the differences between large and small regions, and therefore their specialization opportunities. A region with a clear and narrow specialization is quite different from a region that has a diversified economy with many specializations. Smaller regions may rely on the availability of a particular natural resource, on economies of scale, or on localization economies, which are always combined with a limited intra-regional market potential. In small regions, the material and non-material infrastructure are less general and diversified than in a large regional economy (Johansson and Karlsson 1990a). According to Marshall (1920), localization economies derive from a pooled market for labor with specialized skills, the provision of non-traded inputs of a collective nature, and spillovers of entrepreneurial and technological knowledge, which can spread more easily in a local environment. Localization economies may develop when firms with similar activities locate together, whereby they form a ‘‘cluster.’’ This implies that cluster formation is a cumulative process (Johansson 2006). At each point in time, one may analyze a static cross-section of co-located industries and firms. It is common to interpret such location patterns as equilibrium outcomes. However, it is also possible to conceive of such a cross-section as a momentary image of a dynamic process, where an attractor drives the dynamics, and where this attractor may have (implicit) equilibrium properties. A small region can specialize in the exploitation of natural resources (to the extent that they are available) and in the development of a limited number of Table 1.1 The infrastructure of functional regions Demand conditions Supply conditions Intra-regional Intra-regional accessibility to general Intra-regional accessibility to labor with infrastructure customers, specialized customers, varying skills, natural resources and purchasing power amenities, housing and consumer services, producers in different industries, knowledge resources Inter-regional Inter-regional accessibility to general Inter-regional accessibility to suppliers, infrastructure customers, specialized customers, competitors, knowledge resources purchasing power

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industries serving distant markets. If it is successful in harnessing its location advantages, one or a few specialized clusters may emerge. Early phases of cluster development often build on notable innovative successes. Large and dynamic urban regions offer agglomeration economies which provide a creative milieu (Andersson 1985), a diversified supply of producer services, a diverse supply of human capital, as well as intra-regional and inter-regional information flows. For the most part, large urban regions offer a more diverse supply of markets than smaller regions (Hacker et al. 2004). This reflects differences in geographic transaction costs among goods and services. Profit-seeking firms cannot supply distance-sensitive goods or services in functional regions where the demand is too small to cover fixed costs. The theoretical background is as follows: Diversity in the set of regionally produced consumer goods or producer inputs can yield external scale economies, even if all individual competitors and firms earn normal profits. The size of a functional region in terms of aggregate purchasing power determines the number of specialized local consumer goods and producer inputs, given the degree of substitutability among the specialized local goods in consumption and among specialized inputs in production: A larger city will have a greater variety of consumer products and producer inputs. Since the greater variety adds to consumer well-being, it follows that larger cities are more productive, and the well-being of those living in cities increases with their size. This is true even when all firms in these cities earn a normal rate of profit. (Johansson and Quigley 2004, p. 170)7

There are two well-known models, which deal with the advantages of a diversified urban economy. The first model focuses on urbanization economies in general such as consumers’ taste for variety and, in addition, the productivity of specialized production factors. The second model is quite different: the proximity and linkages of firms in an agglomeration enhance their productivity. The perspective here is forward and backward linkages among economic agents such as firms. Thus, large functional regions have quite different specialization opportunities compared with smaller regions, since the demand for diversity and variety favors location of activities and households in large functional regions. Large home markets in conjunction with high accessibility to external markets enable many large urban regions to develop specializations (i.e., clusters) in many different industries. Firms in the same cluster may represent different stages in the production chain and also industries offering supplementary services. In many large regions, services predominate. Because of their great market potential, large regions also make it possible for firms with a ‘‘thin’’ but distance-sensitive demand to find sufficient demand to earn a profit, even with substantial fixed costs. Large urban regions are especially attractive to such firms which imply that one

7

However, there are factors, which limit the growth of cities. Otherwise, cities would grow continuously. There are costs which rise with city size, most obviously prices (space in particular), and some external costs like congestion and pollution. Also probably, crime rises with city size.

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important characteristic of large urban regions, besides hosting many clusters in different industries is the diversity of goods and services offered to consumers as well as to other firms. Another characteristic of large urban regions is that they host great concentrations of knowledge in the form of human capital, both in the form of labor and infrastructural facilities such as universities. Head offices of multinational corporations are nearly always in the downtown areas of major cities, and so are often their research and development divisions. Large regions are almost always well connected to the global air transportation network and – in Europe and Japan – to high-speed rail networks. Their access to global transportation networks makes large cities attractive meeting places in which to stage conferences, trade fairs and the like. Taken together, these conditions imply that large cities often perform gateway functions, which means that they functions as import nodes for new ideas, inventions, and innovations, which are then disseminated to their low-accessibility hinterlands (Andersson and Andersson 2000). Most large urban regions have high per capita incomes, relative to smaller regions in the same part of the world. Their relative affluence implies a greaterthan-average demand for income-elastic goods and services. Such regions also tend to host a number of firms that are demanding customers in their own right, such as hospitals and high-technology firms with a high demand for new advanced technology. There is consequently a large demand for new advanced products from both consumers and producers. The demand for new advanced products in large urban regions has two implications. First, there is an especially great demand for imported products, since most new advanced products are first produced somewhere else. Second, most large regions offer good conditions for the development and introduction of new products since there is a spatial concentration of customers with a sufficient willingness to pay. We see a general pattern emerging. The larger the region is, the better are the conditions for innovation. And the better the conditions for innovation are, the more dynamic the region becomes. Of course, large urban regions are not equally dynamic, but there is nonetheless a strong tendency for the largest regions to be the most dynamic in a global sense. The names of the most successful dynamic regions are well known in both the scholarly and popular literature, and they coincide with those functional urban regions that have the greatest aggregate purchasing power. Many of these large urban regions have been dynamic and innovative for a long time. Still, historically there are plenty of examples of urban regions, which have lost much of their creative potential. One colorful description of how a dynamic region lost much of its innovative power can be found in Jacobs (1969), where she describes the fate of Manchester, which for a long time was the world’s leading innovative milieu in the textile industry. An interesting aspect of Jacobs’ analysis is that she contrasts Manchester with Birmingham, where Birmingham fared better because it was less dependent on (standardization-prone) large-scale manufacturing. We will not discuss any more examples of large urban regions, which over time seem to have become less dynamic. Instead, we turn to the problem of keeping regions innovative.

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Regional Dynamics

The international economic system contains large metropolitan regions, which serve as spatial focal points (Johansson and Karlsson 1990b). In small countries, it is common for a single urban region to develop into the only international gateway. In other cases, several large regions share the role of being gateways to a system of functional regions. Gateways specialize in importing recent ideas, inventions, and innovations. Besides being ‘‘sinks’’ for novel inputs from the world economy, gateways function as incubators for new products. Imports are important stimuli for product innovation. On the one hand, they may stimulate direct imitation. More importantly, imports may induce the product innovation, both in the form of incremental adaptations of the design and new complements. Given their dual nature as ‘‘sinks’’ and incubators, gateways are normally the most dynamic regions in each country. All gateways coordinate spatial customer networks, which link a set of peripheral nodes to the central gateway node. Peripheral nodes usually specialize in production for export. They receive information about new ideas and trends from the central gateway node. Export nodes sometimes have strong linkages to several more central nodes, including nodes in other countries. Multinational corporations are especially notable as facilitators of such international links. Not all large urban regions are gateways, and some gateways have mediumsized populations. For example, one group of large regions specializes in largescale industrial production for long-distance export. If we take a snapshot of a country and its system of functional urban regions, we can observe how the different regions have acquired specialized roles in the national and global economy by pursuing different development paths. The development path of an individual region is the result of a dynamic interplay between internal and external forces. Different paths additionally have path-dependent risks and uncertainties, since some – but not other – regional specializations may become technologically obsolete or uncompetitive due to new low-cost competitors in other parts of the world. At the same time, accumulated investments in specialized skills, capital, and institutions may create rigidities which make necessary restructuring both slow and difficult (Johansson and Karlsson 1990a). To understand how the regional specializations and patterns of interaction change over time, it is necessary to adopt a time scale that is long enough to accommodate cyclical changes in production patterns. The production of most goods and services evolves through a cyclical pattern where an initial expansion yields to standardization and, in the long run, obsolescence. In the production of standardized goods, only those firms survive that manage to cut production costs, either through relocation to regions with cost advantages or through process innovation. In the long run, however, it is only through the substitution of new goods for obsolete ones that the relative wealth of a region can endure (Johansson and Karlsson 1990b). Economic development depends on the pace and coordination of such renewal processes. In successful cases, the expansive phase gives rise to

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product and process innovations that cause temporary monopoly profits. Successful innovations not only cause temporary profits but also give rise to long-term productivity and employment gains. Regions without natural resource advantages must develop knowledge advantages to achieve high living standards. In order to observe the product life cycle it is necessary to devise a method that can distinguish between different products in such a way that each product has a definite market entry (birth) and market exit (death), to the extent that the latter occurs (Batten and Johansson 1989). With such a method, it also becomes possible to superimpose individual life cycles that result in aggregated cycles, which describe long-term economic waves and their associated spatial relocation waves. Such aggregation generates an image of long waves for entire product groups and technological families. The importance of this observation becomes significant when we note that during recent centuries it is possible to distinguish periods with identifiable technological shifts. Every such period of restructuring has intensified the initiation of new product cycles. What are then conditions for successful innovation, leading to a take-off and expansion of production as well as the initiation of a new product cycle? The simple answer is that production must be profitable enough to redirect resources from existing production, within the region and sometimes from other regions. This implies that entrepreneurs bid up the prices for land and labor. When the prices of production factors increase, firms that produce mature products discover that they suffer losses even if they introduce process innovations. Before its long, they face the choice between terminating and relocating production. Whatever they decide, the result is that resources become available for newer products. Recurrent structural change is a precondition for a region’s long-term viability. Even if higher wages induce a flow of labor to the innovative regions, the termination or relocation of older production is necessary in order to release land to the new, higher-valued, and thus more efficient use. In addition, improvements to the physical infrastructure make an increased density possible and leads to greater overall accessibility. The key point in this section is the importance of the out-migration of mature products from dynamic, innovative regions for the development of both innovative and imitating regions in a multi-regional system. In the next section, we will present two theories, which both offer dynamic explanations of the location behavior of firms in a system of functional urban regions.

1.4.1

Location Dynamics in a System of Functional Urban Regions

The filtering-down theory and the spatial product cycle theory provide alternatives to the neo-classical convergence theories. They both offer dynamic explanations of the location behavior of firms in a system of functional urban regions, employing – in

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the first case – a central place system or – in the second case – the concept of location advantage. Both theories assume that the development of a product or an industry follows a sequence with an introduction, a growth, and a maturation phase, that takes the form of an S-shaped growth curve. The life-cycle perspective makes it possible to see patterns in the continuously adjusting spatial structure of both intraregional and inter-regional development. In particular, the life-cycle concept seems to be a useful device for explaining location dynamics, especially in the case of inter-regional relocation processes for new products and industries (Aydalot 1984).8 However, we should note that some activities do not exhibit cyclical behavior. These activities are mainly non-standardizable activities such as customized delivery of goods and services, where each delivery has new and individual attributes (Forslund 1997). Even if there are many similarities between the two theories there is also one major difference: while the filtering-down theory stresses that products and industries filter down through the system of functional urban regions in a hierarchical manner from regions with larger market areas to regions with smaller market areas (Thompson 1969; Moriarty 1991), the spatial product cycle theory does not present any similar strict hypothesis concerning the spatial diffusion and relocation pattern as products age (Karlsson 1988). Both theories distinguish between the development of new (young) products and the production of mature standardized products with routine production. Both theories further assume that a high proportion of all new products are initiated or imitated (at an early stage) in the leading functional urban regions, with opportunityrich economic milieus and with substantial concentrations of knowledge resources (Johansson et al. 2006). Non-standard goods and services comprise customized deliveries as well as young products. Firms with such products find it advantageous to locate in large urban regions with good accessibility to diverse customer segments, R&D resources, and other suppliers of knowledge services. Other desirable location characteristics include a high purchasing power and good contact opportunities. When a product and its market mature, it often becomes possible to standardize its design and automate its production process. At this stage, its production depends less on metropolitan market, making production in other regions possible. If a relocation or diffusion of production takes places, it can take many different forms (Johansson and Karlsson 2003). Firms may relocate part or all of their production to other regions, but they may also outsource part or all of their production to one or several firms located in such other regions. Firms with production units in several regions may change its inter-regional division of labor. Chain-type firms may gradually expand production in different regions or franchise their business concept. In addition, firms in smaller regions may imitate products developed in large urban regions. According to the filtering-down theory, technical and demand changes induce firms to shift the location of the production of existing products (or product groups) 8

The continual self-reorganizing and evolution of the global spatial economy at a macro scale can also be analyzed by applying the ‘‘new economic geography approach’’ (Fujita and Mori 1998).

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over time, thereby transforming the specialization pattern of regions (Camagni et al. 1986). This makes the filtering-down process both market-driven and technologydriven. On the one hand, standardization of products and production may lower both the set-up costs and variable production costs. On the other hand, demand may increase due to increasing real incomes, changing preferences, and outsourcing of activities from firms and households. Consequently, the production of different products may gradually filter down or diffuse downwards in the hierarchy of functional urban regions. In this way, the filtering-down theory refers to products that spread from one level of the hierarchy to all functional regions at the next lower level. If we consider a new product with high spatial transaction costs that has been introduced in the largest region in an economy, it will start to filter down the system of functional urban regions when the real income and hence demand increases in smaller regions or when the set-up costs for production have decreased sufficiently. This process stops at some level in the hierarchy of urban regions, if it is inconceivable to mobilize enough demand to make production profitable. The spatial product cycle theory similarly assumes a relocation of production from the leading urban regions. However, the number of followers is limited, since economies of scale normally prevent decentralization to many regions, except in cases with very high spatial transaction costs. Changes in location are dependent on location advantages, even at later stages of the product cycle (Vernon 1960; Hirsch 1967; Andersson and Johansson 1984a). Hence, this theory stresses the importance of external economies for the location of production. When relocation does take place, it is limited to a small set of specialized regions. Localization economies are decisive and provide individual regions with their most important location advantages (Marshall 1920; Krugman 1991). Andersson and Johansson (1984a,b) use microeconomic models to show how product cycle assumptions generate location and relocation processes (see also Johansson and Karlsson 1986, 1987). Both papers demonstrate how clusters of product cycles can be observed empirically in the form of aggregate specialization patterns, which describe a time-space hierarchy. In a later contribution, the two authors reformulate their results into a more coherent framework and emphasize new directions for this type of model formulations (Johansson and Andersson 1998). In their later model, knowledge intensity, product standardization, and process routinization are key notions. Along a product cycle path, Johansson and Andersson assume that the knowledge intensity is high when a product is nonstandardized and the production process is non-routinized. Standardization and routinization imply reduced knowledge intensity. Andersson and Johansson proceed to present a class of models that explains this regularity. They then derive interdependencies between location dynamics and product cycles that incorporate elements from models of monopolistic competition. Moreover, they use notions of product and process vintages to classify structural properties and the associated markets. Products with relocated production usually have low spatial transaction costs and in this case production may be relocated for defensive as well as offensive reasons. The occurrence of new locations indicates that the product is no longer

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new, but the production process may continue to be renewed (Johansson 1998b). When the product is in its growth phase it may be too expansive to expand production in large urban regions and thus new locations are sought for the organization of large-scale production. Since the scale of production increases, there are strong preferences for locations offering good accessibility in the national and international logistical systems. At later stages of the product cycle, cost considerations become more important and production relocates to more peripheral regions or to regions abroad. Comparative advantages in the case of the spatial product cycle are often in regions that have lower land and property prices as well as lower costs of unskilled labor inputs (Andersson and Johansson 1984a,b; Johansson and Stro¨mquist 1986; Johansson 1993b).

1.4.2

Lead–Lag Models

Johansson (1993b) emphasizes the dynamics of product vintages as the force that drives the behavior of filtering-down and spatial product cycle models – an assumption that forms the foundation for empirical lead–lag models9 (see also, Forslund and Johansson 1995; Forslund 1996, 1997; Johansson and Karlsson 2003). The lead–lag model has the specific objective of generating hypotheses, which can be tested empirically. It classifies economic activities in such a way that it is possible to refer to them as clusters of products with synchronized location dynamics.10 For a given system of functional urban regions, the model specifies – for each type of product group (industry) – its average share of all product groups (industries) in the system of functional regions (measured as employment or value added). The model identifies a specific leading region, for a given system of functional regions. The relative industry shares for the leading region are predictive indicators. The basic hypotheses in lead–lag models are associated with the location leadership of the leading region. The first hypothesis states that product groups (industries) with high relative shares in the leading region should be expected to grow in other regions in the system of functional regions. The second hypothesis states that product groups (industries) with low relative shares in the leading region should be expected to decline in other regions in the system of functional regions. The first hypothesis implies that new product groups (industries) originate in the leading region. The second hypothesis implies that leading regions lose employment in mature product groups (industries) before other regions. Hence, they are leading regions also as regards the decline of products.

9

For example, the ‘‘flying geese model’’ proposed by Fujita and Mori (1998) can be considered as a special case of the more general lead–lag model. 10 The lead–lag model does not apply to activities, which have to be harvested in the region where they are located. The location of such production is analysed by standard location advantage models, where the comparative advantages are of Ricardo type.

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The above basic hypotheses yield a number of sub-hypotheses. For example, industries with both high relative shares and fast growth rates in the leading region are non-routinized activities and have non-standardized products that compete on the basis of product rather than price. They also tend to involve research and other knowledge production. Industries with low relative shares are on the other hand routinized activities that compete on the basis of price that aim at reducing the labor input coefficient. Lead–lag models assume that a high proportion of new products are initiated or imitated (at an early stage) in the leading region in the system of functional regions. As the production expands, products are frequently standardized and production techniques routinized, which is referred to as product vintage dynamics. As new product vintages are introduced, the pertinent activities are relocated or diffused within the system of functional regions. Analyzing vintage process dynamics as the driving force in spatial product-cycle and filtering-down models, it is possible to show that a gradual change in location takes place. The follower regions host industries for which the vintage renewal is dominated by standardization and routinization (Forslund and Johansson 1998). The main point we want to stress here is that the economy of nations and regions is rejuvenated when production from large urban regions relocates to successively smaller regions. Of course, the establishment of new production in these regions will also generate structural change. The new production units will tend to bid up prices for land and labor in these regions, thereby making the previous marginal production activities unprofitable. To the extent that labor relocates to large regions when new product cycles emerge, the structural changes in medium-sized and smaller regions may be even more pronounced.

1.5

Content

The subsequent chapters of this book are arranged in a sequence running from theoretical to empirical perspectives and ending with a chapter written by Bo¨rje Johansson.

1.5.1

Theoretical Contributions

In Chap. 2, Martin J. Beckmann develops a systematic theory of spatial markets. It is expositional, drawing on previous work by T.C. Koopmans, the author himself and others of the ‘‘efficient allocation school.’’ The underlying theme is the familiar one of showing how prices (indexed by location) in competitive markets can serve to guide the allocation of resources in space, and the deviations from this optimum that occur under various types of institutions. To¨nu Puu in Chap. 3 reconsiders the Smith–Ricardo paradigm of complete specialization and comparative advantage. He shows how minute effects arising from the monotony of repetitive work shatter linear theories.

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The ultimate concern of Chap. 4 by Masahisa Fujita is the further development of the New Economic Geography towards a more comprehensive theory of spatial economics in the age of the brain-power society, in which the dynamics of the spatial economy arise from the dual linkages in the economic and knowledge fields. ˚ ke E. Andersson and David Emanuel Andersson the mechanisms In Chap. 5 by A of creativity are discussed as well as individual and social aspects of creative organizations. They show that because of the public nature of creativity there are increasing returns to scale in many creativity-based production systems, and that economic incentives to promote creativity are complicated by the importance of uncertainty and the importance of ‘‘star performers.’’ David F. Batten and Roger Bradbury in Chap. 6 present the view that the building blocks of regions and regional policies such as ideas, actions, habits, skills, and inventions are akin to selfish Darwinian entities – memes – that, like genes, interact and replicate in complex ways with humans to shape our culture. Whether good or bad, a single, omnipotent meme can dominate a local region of meme-space. The objective of Chap. 7 by Lata Chatterjee and T.R. Lakshmanan is to briefly describe the process by which entrepreneurial cities fashion or socially create their dynamic competitive advantages, which underlie their ability to function and thrive in the new global economy. The authors argue that three autonomous and independent urban sectors – economic, political and social – are involved in the joint production and maintenance of urban dynamic competitive advantage. Chapter 8, by Hans Westlund, deals with social capital as an extra-market externality, and its role for innovation and growth. The chapter provides analyses of the changes in innovation activity over time from early industrialism to the global knowledge economy, how the relations between the actors of today’s innovation systems have developed and the role of social networks for innovations. Kingsley E. Haynes, Rajendra Kulkarni and Roger Stough in Chap. 9 use information methods to describe the patterns in urban freeway traffic flows in order to analyze ‘‘hidden order’’ in such high volume congested systems. They introduce and develop a method for measuring order in linear flow patterns based on Kolmogorov entropy. Chapter 10 by John R. Roy and Geoffrey J.D. Hewings deals with multi-regional input–output analysis where five sets of component flows are jointly determined, thus ensuring that observed flows, which contain all these components, are consistent with the flows being modeled. In addition, rather than assuming just a single abstract path between each pair of regions, feasible multiple paths are assumed.

1.5.2

Empirical Contributions

In Chap. 11, Artem Korzhenevych and Johannes Bro¨cker study the implications of factor mobility and wage rigidity assumptions for the evaluation of infrastructure policy effects using a multiregional computable general equilibrium model. Their

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modeling results suggest that the introduction of wage rigidity matters for inference, expanding the range of values notably. Makato Tsukai and Kiyoshi Kobayashi in Chap. 12 stress the lack of studies that focus on the persistent (lagged) production effect of infrastructure. To remedy this lack, the authors present an approach where the lagged effects of infrastructure productivity and technological innovation are specified as a multiple time series model with a long persistent effect. Chapter 13, by Roberta Capello and Andrea Morrison, aims to measure the effectiveness of science parks in fostering knowledge transfer processes at the local level. Their empirical analysis provides prima facie evidence that Science Parks play a role in creating relationships among local actors while they perform their gate-keeping function rather poorly. In Chap. 14, Enno Masurel and Peter Nijkamp address the lack of institutional collaboration among urban ethnic (or migrant) firms as a reason for their low innovation profile. Poor communication, a low chance to be accepted by the external party, and economic market factors appear to be important reasons why ethnic entrepreneurs do not join franchise organizations. The purpose of Chap. 15 by Charlie Karlsson and Martin Andersson is to analyze the location relationship between industry and university R&D in Sweden using a simultaneous equation approach. Their results indicate that the location of industrial R&D is quite sensitive to the location of university R&D and that the location of university R&D is sensitive to the location of industrial R&D. Chapter 16 by Paul Cheshire and Stefano Magrini investigates growth differences in the urban system of the EU12 over the last decades of the twentieth century. It contrast the set of factors associated with population growth with those associated with growth in output per head. There are several factors common to both types of growth but while economic growth is associated with factors driving innovation and productivity such as highly skilled human capital and concentrations of R&D, population growth responds strongly to differences in climate but only to differences within countries. Johan Klaesson and Lars Pettersson in Chap. 17 analyze the influence of urban size on the development of neighboring rural population and employment. Employing a Carlino-Mills type of model they find that working age population is the most significant factor for explaining changes in the working-age population. Generally, their analysis seems to support the hypothesis that ‘‘jobs follow people.’’ In Chap. 18, Manfred M. Fischer, Thomas Scherngell and Eva Jansenberger analyze patent citation data pertaining to high-technology firms in Europe to test the extent of knowledge spillovers. Using the case-control matching method they find strong evidence of geographic localization at two different spatial levels (country and region), even after controlling for the tendency of inventive activities in hightechnology sectors to be geographically clustered. The aim of Chap. 19 by Katarina Larsen is to increase our understanding of science output, structure, and impact within the area of nano-structured dyesensitized solar cells. The results are based on co-authorship data and interpretative

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analyses of the Swedish network hub and point to the importance of factors such as citation window and journal impact factor for citation eminence. Ian Gordon, Colin Haslam, Philip McCann and Brian Scott-Quinn in Chap. 20 first discuss the current activity and employment base of London’s financial center in relation to the kinds of capacity that is developing in offshore centers (particularly in India), and then examines the approaches which City investment banks are currently adopting to these issues. The objective of Chap. 21 by Pontus Braunerhjelm is to shed new insights on the forces that prompt the emergence of clusters, and how these forces interact with more well-known mechanisms to enforce and sustain existing clusters, using the Stockholm music cluster as an example.

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Johansson B, Marksjo¨ B (1984) Interactive system for regional analysis of industrial sectors. In: Nijkamp P, Rietveld P (eds) Information systems for integrated regional planning. NorthHolland, Amsterdam, pp 231–249 Johansson B, Quigley J (2004) Agglomeration and networks in spatial economics. Pap Reg Sci 83:165–176 Johansson B, Snickars F (1992) Infrastruktur, BT33:1992, Byggforskningsra˚det, Stockholm Johansson B, Stro¨mquist U (1981) Rigidities in the process of structural economic change. Reg Sci Urban Econ 11:336–375 Johansson B, Stro¨mquist U (1986) Teknikspridning och importsubstitution – Stockholmsregionens roll fo¨r svensk teknikfo¨rnyelse, Rapport 1986 nr 7:2. La¨nsstyrelsen i Stockholms la¨n, Stockholm Johansson B, Westin L (1994) Revealing network properties of Sweden’s trade with Europe. In: Johansson B, Karlsson C, Westin L (eds) Patterns of a network economy. Springer, Berlin, pp 125–141 Johansson B et al (1991) Infrastruktur och produktivitet, Expertutredning Nr 9 till Produktivitetsdelegationen. Allma¨nna Fo¨rlaget, Stockholm ˚ berg P (1998) Regioner, handel och tillva¨xt. RTK, Stockholms la¨ns Johansson B, Stro¨mquist U, A landsting, Stockholm Johansson B, Karlsson C, Stough RR (2006) Entrepreneurship, clusters and policy in the emerging digital economy. In: Johansson B, Karlsson C, Stough RR (eds) The emerging digital economy. Entrepreneurship, clusters and policy. Springer, Berlin, pp 1–19 Karlsson C (1988) Innovation adoption and the product life cycle. Umea˚ economic studies no. 185. University of Umea˚, Umea˚ Karlsson C, Johansson B (2006) Towards a dynamic theory for the spatial knowledge economy. In: Johansson B, Karlsson C, Stough RR (eds) Entrepreneurship and dynamics in the knowledge economy. London, Routledge, pp 12–46 Karlsson C, Pettersson L (2005) Regional productivity and accessibility to knowledge and dense markets. CESIS working paper 32. Jo¨nko¨ping International Business School, Jo¨nko¨ping Karlsson C, Johansson B, Stough RR (2005) Industrial clusters and inter-firm networks – an introduction. In: Johansson B, Karlsson C, Stough RR (eds) Industrial clusters and inter-firm networks. Edward Elgar, Cheltenham, pp 1–25 Keynes JM (1936) The general theory of employment interest and money. Macmillan, London Krugman P (1991) Geography and trade. MIT, Cambridge, MA Lancaster K (1971) Consumer demand – a new approach. Columbia University Press, New York Launhardt CWF (1872) Kommercielle Tracirung der Verkehrswege. Zeitschrift des Architectenund Ingenieur-Vereins Hannover 18:515–534 Launhardt CWF (1882) Die Bestimmung des Zweckma¨ssigsten Standortes einer Gewerblichen Anlage. Zeitschrift des Vereines Deutscher Ingenieure 26:105–116 Lo¨sch A (1954) The economics of location. Yale University Press, New Haven Maillat D, Crevoisier O, Lecoq B (1993) Innovation networks and territorial dynamics: a tentative typology. In: Johansson B, Karlsson C, Westin L (eds) Patterns of a network economy. Springer, Berlin, pp 33–52 Marshall A (1920) Principles of economics. Macmillan, London ˚ E, Batten DF, Mills ES, Carlino G (1989) Dynamics of county growth. In: Andersson A Johansson B (eds) Advances in spatial theory and dynamics. North-Holland, Amsterdam, pp 195–206 Moriarty B (1991) Urban systems, industrial restructuring and the spatio-temporal diffusion of manufacturing employment. Environ Plann A 23:1571–1588 Moroney JR, Walker JM (1966) A regional test of the Heckscher–Ohlin theorem. J Polit Econ 74:573–586 Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge, MA Ohlin B (1933) Interregional and international trade. Harvard University Press, Cambridge, MA Palander TF (1935) Beitra¨ge zur Standortstheorie. Almqvist and Wicksell, Uppsala

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Scarf HE, Hansen T (1973) The computation of economic equilibria. Yale University Press, New Haven Scherer FM (1984) Innovation and growth – Schumpeterian perspectives. MIT, Cambridge, MA Schumpeter JA (1908) Das Wesen und der Hauptinhalt der Theoretischen Nationalo¨konomie. Duncker&Humblot, Leipzig Schumpeter JA (1934) The theory of economic development. Oxford University Press, New York Smith B (1975) Regional specialization and trade in the UK. Scott J Polit Econ 22:39–56 Teubal M, Zuscovitch E (1994) Demand revealing and knowledge differentiation through network evolution. In: Johansson B, Karlsson C, Westin L (eds) Patterns of a network economy. Springer, Berlin, pp 15–31 Thompson WR (1969) The economic base of urban problems. In: Chamberlain NW (ed) Contemporary economic issues. Pichard Irving, Homewood, IL, pp 1–47 Tinbergen J (1967) The hierarchy model of the size distribution of centres. Pap Reg Sci Assoc 20:65–68 Uzawa H (1962) Aggregative convexity and the existence of competitive equilibrium. Econ Stud Q 12:52–60 Uzawa H (1976) Disequilibrium analysis and Keynes’s general theory Vernon R (1960) Metropolis 1985. Harvard University Press, Cambridge, MA Von Thu¨nen JH (1826) Der isolierte Staat in Beziehung auf Landwirtschaft und Nationalo¨konomie. Perthes, Hamburg ¨ ber die Eindeutige positive Lo¨sbarkeit der neuen Produktionsgleichungen. Wald A (1933–34) U Ergebnisse eines Mathematischen Kolloquiums 6:12–20 ¨ ber die Produktionsgleichungen der o¨konomischen Wertlehre. Ergebnisse Wald A (1934–35) U eines Mathematischen Kolloquiums 7:1–6 Weber A (1929) Theory of the location of industries. The University of Chicago Press, Chicago

Chapter 2

The Pure Theory of Spatial Markets Martin Beckmann

2.1

Introduction

Spatial phenomena which had been topics in theoretical Geography and Location Economics were seen as common objects in the new Regional Science of the 1950s. A point of crystallization was the notion of spatial markets going back to Wilhelm Launhardt (1886), who perceived them, as market (and supply) areas. Market areas are territories in which a given firm is the nearest and ceteris paribus the cheapest and hence exclusive supplier. This essay develops a systematic theory of spatial markets. It is thus an expository, drawing on previous work of T.C. Koopmans, Martin Beckmann and others of the ‘‘efficient allocation’’ school. The underlying theme is the familiar one of showing how prices (indexed by location) obtained in competitive markets can guide allocation of resources in space, and the deviations from this optimum that occur under various types of institutions. While thus backward looking, spatial markets can still provide an organizing framework to our contemporary interest in innovation. When buyer and seller are not in the same place, distance intervenes and transaction costs for transportation and/or communication arise, ordinary market theory no longer applies, e.g., ‘‘law of the single price’’ is violated. We are then, faced with spatial markets. This is actually among the oldest subjects treated in location theory. In Von Thu¨nen’s ‘‘Isolated State’’ land use, production and sales to a metropolitan market are investigated as functions of distance (Von Thu¨nen 1826). Wilhelm Launhardt’s location theory is grounded on market areas as sets of exclusive sales in a territory surrounding a firm or localized industry (Launhardt 1886). In this paper we offer a modern perspective of the theory of spatial markets in perfect competition. We exclude spatial price policies under monopoly, which have a rich literature of their own (Greenhut and Ohta 1975; Beckmann 1976). Market

M. Beckmann Senior Academy Secretary, Brown University

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strategies of oligopolists aimed at defence or penetration of market areas, in which pricing is confounded with locational choice are also beyond the scope of this paper. The pure theory of spatial markets considers only those questions that exist when locations are given. In essence, it is location theory without locational choice.

2.2

The Transportation Problem of Linear Programming

Traditionally, spatial markets have been classified as either market or supply areas centered on an exporting or importing location. But these do not exhaust all conceivable market configurations. At any rate spatial structures like these should be discovered, not assumed, by spatial economic theory. The modern approach to spatial markets takes off from the Transportation Problem of Linear Programming (Koopmans 1949). We reformulate it in terms of given constant excess supplies q. In a set of locations i, j, k ¼ 1, . . . , n, allowing transhipments in the presence of necessary restrictions of flows to a given transportation network N, so that in all summations it is understood that indices run only over locations in the network. Our interest is in flows xij of a commodity from point location i to point location j at unit transportation costs rij that will satisfy a commodity balance condition to meet excess demand qi X xji  xij ¼ q: ð2:1Þ j

Competitive market equilibrium, achieving a Pareto optimum, must then minimize total transportation costs X min rij xij : ð2:2Þ xij 0

i;j

This linear program (2.1) and (2.2) is feasible provided X qi ¼ 0;

ð2:3Þ

i

i.e., aggregate excess demand must be zero. An optimal solution, i.e., a market equilibrium is characterized by necessary and sufficient ‘‘efficiency conditions’’ (Koopmans 1949). The equilibrium involves ‘‘dual variables’’ pi that can be interpreted as competitive market prices. The efficiency conditions are     ¼ < x^ij ð2:4Þ 0 , pj  pi r :  ¼ ij In equilibrium the transactions x^ij are ‘‘efficient’’ and thus recover their transportation cost rij, while any of the excluded inefficient transactions do not. The

2 The Pure Theory of Spatial Markets

37

remarkable thing is not that this should be necessary, but that this is sufficient and together with constraints (2.1) will determine all valid equilibria. The efficiency conditions (2.4) now imply pi ¼ max pj  rij ;

ð2:4aÞ

pj ¼ min pi þ rij :

ð2:4bÞ

j

i

These equations state that sellers in i export to buyers j in order to maximize prices pi received in i. Buyers in j look for the cheapest sources i after transportation costs rij. A trading post j may both buy from cheapest sources i and sell to best buyers k min pi þ rij ¼ pj ¼ max pk  rjk : i

ð2:4cÞ

k

Geographically the areas containing sellers (sources) or buyers (sinks) may, but need not, overlap. In the fur trade the sources were Indians in the wilderness and the buyers, residents of Europe. Observe, that since only price differences occur in (2.4) the price level is indeterminate, a consequence of the feasibility equation (2.3). The ‘‘primal variables’’ x^ij satisfying (2.1)–(2.4) need not be unique. A theorem assures the existence of an optimal trade pattern with at most n1 flows, where n is the number of locations. This means that the flow system can form a tree or a set of trees, each being an independent market by itself. A set of demand locations j receiving from a single supplier i is i’s market area and a set of supply locations i for a single demand location j is j’s supply area. In addition there may be locations that will both import and export (forward). These may be called transit points or trading posts: they are common on given transportation networks. An interesting identity relates the minimal transportation cost T to the price system X X X X   X T¼ rij x^ij ¼ pj  pi x^ij ¼ pi pi qi ð2:5Þ x^ji  x^ij ¼ i; j

i; j

i

j

i

using (2.1) and (2.4). This identity is a part of the Duality Principle of LP (Dantzig 1959) X X T ¼ min rij xij ¼ max pj qj pj

Xij

s.t:

X

j

xji  xij ¼ qi

j

s.t: pj  pi  rij :

ð2:5aÞ

38

2.3

M. Beckmann

Braess’s Paradox

Expanding a program by simultaneously adding an amount a to supply at some location i and to demand at some location j can reduce total transportation cost T. Proof: Change qi at a supply location qi pj to qi a and at a demand location qj >0 to qj +a to obtain T¼api +api ¼a (pj pi) xji  xij ð2:6Þ 0, q: pi  ¼ i j

b

a

5

Fig. 2.1 Point a¼1, point b¼0, point c¼5, point d¼4

c

1

d

2 The Pure Theory of Spatial Markets

39

^ following holds If ‘‘ qi^:

ð2:6aÞ

j

In view of (2.4b) and rij >0 this cannot happen at locations of positive (excess) demand; it must occur in a location of excess supply. The event (2.6a) will fix the price level pi^ ¼ 0 and make prices uniquely determined. Because of the ‘‘complementary slackness’’ in conditions (2.4) and (2.6) the identity (2.5) of (minimal) transportation cost and priced excess demand remains valid. The inclusion of production cost X

min xij 0

rij xij þ

i;j

X

hi ðzi Þzi ;

ð2:7Þ

i

zi 0

X

xji  xij  qi  zi ;

ð2:7aÞ

j

where zi is the production in location and hi(zi) is the cost function, or more simply, with constant unit cost hi in the cost minimization description of spatial market equilibrium (2.2) is straightforward. The results are included in the next section.

2.4.1

Flexible Demand

As long as demand is given, independent of price, competitive market equilibrium can be described as achieved by aggregate cost minimization. When demand is flexible, viz. price dependent, we must resort to welfare maximization. There are two ways of constructing an appropriate welfare function: either using the inverse of a price dependent excess demand function, or more directly as total utility, in money terms, minus aggregate costs. That utility may be expressed in money units and hence made interpersonally comparable while perhaps objectionable to economic purists, is accepted practice in applied micro-economics. We sketch the first and elaborate the second approach. Let qi ðpÞ

ð2:8Þ

be the excess demand function for location i, assumed to be strictly decreasing with price p. Its inverse exists, say, pi ðqÞ

ð2:8aÞ

40

M. Beckmann

and is also strictly decreasing. The integral Z v i ð qi Þ ¼

qi

pi ðqÞdq

ð2:9Þ

0

can then be shown to represent the sum of consumers’ and producers’ surplus vi when excess demand equals qi. Purists’ objections to the consumers’ surplus notwithstanding, it is this welfare measure max xij  0

X

vi

i

X

! xji  xij



X

rij xij

ð2:10Þ

j

which is maximized with respect to the flows xij in spatial market equilibrium (Samuelson 1952). In fact (2.10) yields x^ij

    ¼ < 0 , pj  pi r ; < ¼ ij X

x^ji  x^ij ¼ qi ðpi Þ:

ð2:4Þ ð2:11Þ

j

In the second approach utility ui(q) of consuming a quantity q in location i of the good under consideration is considered, with the usual assumptions u0i > 0;

u00i  0:

ð2:12Þ

Together with convex costs (say) we consider the welfare function X X   X uj qj  hi ð z i Þ  rij xij : j

i

ð2:13Þ

i;j

Maximizing welfare function with the linear constraints qi ¼ z i þ

X

xji  xij

ð2:1bÞ

j

is a well-behaved concave nonlinear program (NLP) yielding the Kuhn–Tucker conditions in terms of dual variables or prices pi     ¼ 0 < 0 , ui p; qi  ¼ i

ð2:14Þ

    ¼ < 0 , h0i p;  ¼ i

ð2:15Þ

zi

2 The Pure Theory of Spatial Markets

x^ij

    ¼ < 0 , pj  pi 0:  ¼

41

ð2:4Þ

For simplicity and in the tradition of location theory let utility be a square of consumption q and thus demand a linear function, qi ¼ai pi, and costs be linear h(zi)¼bi + hizi     ¼ > 0 , hi p zi  ¼ i

ð2:16Þ

(for standardized quantity and price units). Then (2.14) becomes     ¼ < 0 , a i  qi p qi  ¼ i

ð2:14aÞ

qi ¼ maxð0; ai  pi Þ:

ð2:14bÞ

or simply

Demand is a (piece-wise) linear function of price. Depending on climatic or other regional conditions, or by longstanding local custom, low values ai of utility may prevail which exclude local demand for a good even when its price is low. By contrast in places of poor accessibility even desirable goods of high (local) utility ai may not be consumed due to high prices pi. When production is subject to capacity limits zi  ci say (2.7a), then (2.15) is modified to 8
0;

where

0

MðrÞ ¼

r

mðrÞdr;

ð2:21Þ

0

where m(r) is the density of demand in a ring of width dr at distance r. Thus for uniform population density m mðrÞ ¼ 2pmr

ð2:22Þ

one has MðrÞ ¼ pmr 2 ; pmR2 ¼ F;

ð2:23Þ

If the area which suppliers must serve can be expanded beyond the minimal radius R needed to recover fixed cost, rffiffiffiffiffiffiffi F R¼ pm

ð2:23aÞ

both p and each firm’s profits are raised until free entry reduces the latter by lowering the representative firm’s demand density m so that (2.23a) holds once more.

44

2.6

M. Beckmann

Heterogeneous Products

Let the varieties of a good be identified with the locations of their production i. The set of product varieties is then a subset of all locations i. We assume an additive logarithmic utility function uj ¼

X

aij log qij :

ð2:24Þ

i

We denote consumption of i in location j by qij and standardize X

aij ¼ 1:

ð2:24aÞ

pij qij ¼ yj ;

ð2:25Þ

i

Also, assume a budget constraint X i

where yj is the budget allocated to the class of goods i in location j, where pij are prices. A straightforward calculation for this well-known type of utility function yields the nice demand functions yj : pij

ð2:26Þ

pij ¼ pi þ rij :

ð2:27Þ

aij yj pi þ rij

ð2:26aÞ

qij ¼ aij As before, assume mill pricing so that

Demand functions of the type qij ¼

are close to a gravity model of spatial interaction, except for the addition of mill prices pi to the distant terms rij. Equation (2.26a) shows that sales are approximately proportional to inverse distance. The ratio of sales of two rival product i, k in location j is approximately qij ai rkj ffi  ; qkj ak rij

ð2:28Þ

2 The Pure Theory of Spatial Markets

45

where ‘‘attractions’’ aij and akj are independent of buyer locations j and prices are small compared to distances (transportation costs). Attractions are all the same aij akj, this implies that the closest seller has the dominant market share. If we redefine conventional market areas – the set of points closest to a given supplier – as areas of dominant market share, they will once more cover the entire region as mutually exclusive and exhaustive market areas. When attractions differ, these market areas are still exhaustive and exclusive, but distorted from the case where distance alone matters. The logarithmic utility function leads to a particularly nice and simple resolution of the budget constraint. When the heterogeneous good is inexpensive enough to leave out any budgetary restrictions, additive power functions as utility functions will also generate a gravity distance effect. Only utilities of the entropy type will generate a (negative) exponential distance effect in spatial markets.

2.7

Innovation

Spatial markets are a convenient vehicle to study the regional impact of innovations. In particular they offer a natural classification of the spatially relevant types of innovation.

2.7.1

Demand

An innovation in demand can take the following forms: A known product is introduced in new locations or market areas. Inventive sales managers in search of new outlets will eventually sell refrigerators even to the Eskimos. Secondly, new uses may be found for a product in some locations (perhaps in conjunction with price reductions). Most importantly, new products may be introduced, which of course will have to be advertised. It is a well established practice to test the acceptance of a new product in some test location(s) that are considered to be normal enough to serve as good market predictors.

2.7.2

Supply

New (and better) locations – with better access to labor or other inputs, better climate, etc. – may be discovered for the production of a known good, or better methods may be found in the existing locations. The locations of a firm may also be restructured to generate new varieties of a product. The invention of a new product will always require locational choice. New products with an initially small national demand are best started from the metropolis.

46

M. Beckmann

An innovation improving supply over extended areas was the so-called green revolution, the introduction of higher yielding and better resistant crops. Another important innovation is the discovery of new resource deposits (oil, coal, ores) or sources of labor supply in overlooked settlements.

2.7.3

Distribution

While geographical distances remain unchanged, their impact through transportation and communication cost has been strongly reduced through innovations. Transportation costs have shown a secular trend to fall. This has been due to both technological innovations and to changes in organization (regulation). In our times the greatest changes have occurred in communication, particularly through the internet. As a result market areas no longer need to be contiguous or close to the point of supply. The limitation on demand that is imposed by information deficits about a product has been lifted and this has vastly expanded the potential markets of many products.

References Beckmann MJ (1952) A continuous model of transportation. Econometrica 20:643–660 Beckmann MJ (1976) Spatial price policies revisited. Bell J Econ 7(2):619–630 Beckmann MJ, Puu T (1985) Spatial economics: density, potential and flow. North Holland, Amsterdam Dantzig G (1959) Linear programming. Princeton University Press, Princeton Greenhut ML, Ohta H (1975) Theory of spatial pricing and market areas. Duke University Press, Durham, NC Koopmans TC (1949) Optimum utilization of the transportation systems. Econometrica 17: 136–146 Launhardt W (1886) Mathematische Begru¨ndung derVolkswirtschaftslehre. Wilhelm Engelmann, Leipzig Puu T (1977) A proposed definition of traffic flow in continuous transportation models. Environ Plan 9:559–567 Samuelson PA (1952) Spatial price equilibrium and linear programming. Am Econ Rev 42: 283–303 Von Thu¨nen JH (1826) Der Isolirte Staat. Perthes, Hamburg

Chapter 3

Smith–Ricardo Specialization in the Presence of Tiring Effects Tonu Puu

3.1

Introduction

One of the best selling ideas economics ever had was the Smith–Ricardo paradigm of specialization, division of labor, and comparative advantage. Adam Smith (1776) wrote almost lyrically about the advantages of specialized pin-making. Through dividing the process in many tiny operations, combined with education to very specialized moments, such as polishing the nails, or wrapping them in paper, based on natural talent, Smith reported huge increases in productivity. David Ricardo (1817) then launched his theory of comparative advantage, among nations as well as among individuals, and pointed at the advantage for total productivity through total specialization when each one carried out only one particular activity. As a result, everybody should, for individual and common benefit, specialize in one special activity and be a consumer of all the other activities. The theory blows up traditional ideals of educated humanity, dissipated by for instance Baldassare Castiglione (1507) in ‘‘The Courtier’’, on which most of Western education was based for centuries. If you are a brain surgeon, you should operate brains and not paint or make music yourself, because it is better to go to a concert or a gallery in your free time to enjoy the production of other specialists, who, of course, perform their specialized tasks better. Traditional craftwork, with its alternation between very diverse and sometimes enjoyable operations, is superseded by work at the endless conveyor belt. Some periods of multiple occupation seem unenlightened in the light of the specialization paradigm, and it is not understandable how some periods in history, such as Florence of 1500 or Vienna of 1900, could be so productive, despite notorious violation of the paradigm of comparative advantage. Surely, Alberti would have been more productive if he had concentrated on building churches, or, even better, writing on moral philosophy for which he was educated, rather than

T. Puu CERUM, Umea˚ University, SE-90187 Umea˚, Sweden e-mail: [email protected]

C. Karlsson et al. (eds.), New Directions in Regional Economic Development, Advances in Spatial Science, DOI: 10.1007/978-3-642-01017-0_3, # Springer‐Verlag Berlin Heidelberg 2009

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T. Puu

wasting time on writing treatises on the principles of painting? And, Leonardo should above all have decided to be a draftsman or a scientist? Who knows how ingenious a philosopher Wittgenstein would have been if he had not wasted time on sculpture, and what compositions could Schonberg have achieved if he had not spent time on painting? Yet, it is surprising how productive these periods of non-specialization were. In this brief paper we want to give a partial answer to how such things are possible, by taking into account just one tiny little factor. Even if initially productivity is different among individuals, there is no doubt that repeating the same operation over and over after a while becomes boring and as a consequence productivity differences diminish, in which case it may turn out to be better not to specialize completely. The Smith–Ricardo paradigm is linear by the bulk of mainstream economic theory and it is sufficient to introduce just any tiny tiring effect to blow it up. This is the purpose of this paper. It is not to deny that there is much in the theory of comparative advantage, neither is the intention to claim that tiring is the most significant factor working against it. The paper intends to show how easily any kind of nonlinearity fundamentally alters the conclusions of any linear model.

3.2

The Individual

First, consider one individual worker who divides total working time into n different activities in shares denoted by ti . Total working time for the period we consider is taken as given, and, for convenience, normalized to unity. Hence we also take total leisure time as fixed, but this does not constrain anything, as the substitution of leisure for consumption is not an issue at present. Hence i¼n X

ti ¼ 1:

ð3:1Þ

i¼1

Next denote efficient work done in the ith activity xi and define xi ¼ ai  bi ti ; ti

i ¼ 1; . . . ; n:

ð3:2Þ

The quotient is average productivity, which starts at ai , the initial productivity, but is subject to a linear tiring effect bi ti , the longer the time spent on this particular kind of work. When ti ¼ ai =bi , productivity goes down to zero. If ai =bi 1, this never quite happens, even if all time available is spent on only this particular activity. In the classical (linear) Smith–Ricardo world bi always equals zero. Hence this never happens. The obvious thing to do with (3.2) is to multiply through by ti , to obtain xi ¼ ai ti  bi t2i ;

i ¼ 1; . . . ; n:

ð3:3Þ

3 Smith–Ricardo Specialization in the Presence of Tiring Effects

49

Even if productivity goes down to zero first at ti ¼ ai =bi , nobody ever works more than ti ¼ ð1=2Þai =bi for which production is maximal. This is so because (3.3) is quadratic, so a given labor achievement can be obtained with both an effort smaller or larger than this maximizing effort. If we disregard the pleasure of work, nobody chooses the same achievement with a larger effort. Accordingly, if we solve (3.3) for ti , we get the unique smaller root vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi )ffi u (  2 1 ai 1 u a x i i : ti ¼  t 4 2 bi 2 bi bi Now (3.1) and (3.3) provide n þ 1 equations in 2n variables so that we could in principle eliminate all the time shares ti and any one of the work variables xi . As we do not want to give any particular activity preference, we prefer to state the result as an implicit function (a kind of efficiency frontier): Fðx1 ; x2 ; x3 . . . . . . . . . . . .Þ:

ð3:4Þ

From (3.3) and (3.1) it is obvious that when bi = 0, (3.4) is flat, i.e., a plane. In general, we easily realize that the exact form obtained from the solutions of the quadratic equation (3.3) and summing up to unity in accordance with (3.1), renders a much too complex expression to be of any use as given below: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi )ffi u( i¼n i¼n u  2 X X 1 ai 1 ai xi t ¼ 1:  4 2 i¼1 bi 2 i¼1 bi bi We therefore study the properties of (3.4) for a much simplified case, n ¼ 2 and ai ¼ a; bi ¼ b to be precise. Defining b

B ¼ ð2a  bÞ;

ð3:5Þ

Aðx1  x2 Þ2 þ 2ðx1 þ x2 Þ ¼ B;

ð3:6Þ

A¼

ða  bÞ2

;

we can write,

which is a parabola in terms of coordinates (x1  x2 ) and (x1 þ x2 ), which amounts to a rotation by 45 . In Fig. 3.1 we display a family of curves (3.6), where we fix the initial efficiency to a ¼ 1, and let b the ‘‘tiring coefficient’’, range in the interval [0, 1]. As we see from (3.5), with b ¼ 0; A ¼ 0 (3.6) loses its quadratic term, and the efficiency frontier becomes a straight line. Likewise, with b ¼ 1, A becomes infinite and (3.6) collapses to a segment of the positive diagonal.

50

T. Puu

x2

x1

Fig. 3.1 Efficiency frontiers with different tiring coefficients

There is an additional feature in terms of shading. Through implicit differentiation from (3.6) we obtain dx2 1 þ Aðx1  x2 Þ : ¼ 1  Aðx1  x2 Þ dx1

ð3:7Þ

The rising sections of the efficiency frontiers in Fig. 3.1 do not make any sense, so we have to require dx2 =dx1 0, which, using (3.7) states A2 ðx1  x2 Þ2 < 1:

ð3:8Þ

The dark shaded area in Fig. 3.1 indicates where (3.8) does not hold. Recall that we are dealing with one individual worker whose objective is to choose a mix of two different work activities. Once we also know the wage rates for the two activities, we can find an optimal mix of activities for this individual. Note that, if the tiring coefficients are zero, i.e., bi ¼ b ¼ 0, so that we deal with the straight line, the worker will always choose one of the endpoints of this line, i.e., specialize completely. In the presence of tiring we are dealing with one of the parabolas, and for any wage ratio we can then find a point of tangency with the given wage ratio line. Further note that we assumed no particular advantage in any of the two activities, by assuming not only bi ¼ b, but also ai ¼ a. This is of no importance as long as we deal with the single worker, but, once we are considering division of labor in a

3 Smith–Ricardo Specialization in the Presence of Tiring Effects

51

group of workers, it becomes important to introduce asymmetries. Only then can we evaluate the Smith–Ricardo argument.

3.3 3.3.1

Two Individuals Work Sharing Vs. Specialization

To this end consider two workers combined with two types of labor, a setup international trade specialists would approve of. Let us keep x1 ; x2 as work contributions for the first individual, and use y1 ; y2 for the second. We then have a companion to (3.6): Aðy1  y2 Þ2 þ 2ðy1 þ y2 Þ ¼ B

ð3:9Þ

still keeping symbols A; B as determined from (3.5). Later we assume asymmetry between the individuals. As a preliminary, let us compare two extreme cases: complete specialization vs. no specialization at all, i.e., 50/50 sharing of time for each individual. With complete specialization we will for instance have x2 ¼ y1 ¼ 0, and hence from (3.6), (3.5), and (3.9): x1 ¼ y2 ¼ a  b:

ð3:10Þ

As in the other extreme case, suppose there is no specialization at all. Then, we put x1 ¼ x2 in (3.6) and y1 ¼ y2 in (3.9). Obviously: 1 x1 ¼ x2 ¼ y1 ¼ y2 ¼ a  b: 2

ð3:11Þ

In the non-specialization case we have x1 þ y1 ¼ x2 þ y2 ¼ 2a  b as we see from (3.11), whereas in the specialization case, from (3.10), x1 þ y1 ¼ x2 þ y2 ¼ a  b. Hence, in the presence of tiring, b > 0, two identical individuals produce more of both commodities by sharing their time between activities, rather than specializing. In the sequel we will see to what extent the conclusion holds in the presence of asymmetry.

3.3.2

Maximizing Labor Income

Let us so consider working time optimization for each of the individuals given wage rates for the activities. For the first individual the value of total labor time spent on n activities is, using (3.3): i¼n X i¼1

wi xi ¼

i¼n X i¼1

wi ðai ti  bi t2i Þ:

ð3:12Þ

52

T. Puu

In our setup, wages are, of course, associated with efficient work done, not with time spent. Maximizing (3.12) subject to (3.1) yields wi ðai  2bi ti Þ ¼ l;

ð3:13Þ

where l denotes a Lagrangian multiplier associated with the constraint (3.1). We can easily solve for the time shares:   1 l ai ti ¼  2 bi wi bi

i ¼ 1; . . . ; n:

ð3:14Þ

Finally substituting from (3.14) in (3.1), we can also solve for the Lagrangian: P ai 2  i¼n i¼1 b l ¼ Pi¼n 1 i :

ð3:15Þ

i¼1 bi wi

Substituting back from (3.15) into (3.14), we obtain the time shares of each individual spent on various jobs as expressed alone in the wage rates wi and the coefficients that describe the working faculties in the different occupations ai , bi .

3.4

Two Identical Individuals

We already stated the efficiency frontiers for two identical individuals in (3.6) and (3.9) respectively. Let us now maximize the value of their combined work: w1 ðx1 þ y1 Þ þ w2 ðx2 þ y2 Þ:

ð3:16Þ

By associating Lagrangian multipliers m with (3.6) and v with (3.9) we find w1 ¼ 2mð1 þ Aðx1  x2 ÞÞ ¼ 2vð1 þ Aðy1  y2 ÞÞ

ð3:17Þ

w2 ¼ 2mð1  Aðx1  x2 ÞÞ ¼ 2vð1  Aðy1  y2 ÞÞ:

ð3:18Þ

and

Together with (3.6) and (3.9), (3.17)–(3.18) boil down to m¼v¼

w1 þ w2 4

ð3:19Þ

and x1 ¼ y1 ;

x2 ¼ y2 :

ð3:20Þ

3 Smith–Ricardo Specialization in the Presence of Tiring Effects

53

x2 + y2

x1 + y1

Fig. 3.2 Efficiency frontier for two identical individuals

As the activity mixes for the two individuals are identical, we conclude that the joint efficient labor mix is twice that for each individual. The facts can easily be presented in terms of a diagram of the type well known from international trade theory. In Fig. 3.2 the efficiency frontier for one individual is turned the right way, whereas that for the other is rotated by 180 along with its coordinate system. By letting the rotated system slide down at tangency with the fixed one, we take care of all different wage ratios, and in this sliding motion the origin, marked by black dots, of the movable system describes the total combination of efficient work for the two individuals. The motion of the origin of the second coordinate system then traces the curve which is an exactly scaled up copy (by the factor 2) of the individual efficiency frontier.

3.5

Two Asymmetric Individuals

More interesting is the case where the individuals indeed have a different comparative advantage. We are not going to focus on differences in tiring coefficients in their relation to initial efficiency, so we put the efficient labor done as follows: x1 ¼ kðat1  bt21 Þ;

1 x2 ¼ ðat2  bt22 Þ; k

ð3:21Þ

54

T. Puu

y1 ¼

 1 at1  bt21 ; k

  y2 ¼ k at2  bt22 :

ð3:22Þ

In this way we can keep the same symbols a; b as before and hence A; B as defined in (3.5) above. It is a bit inadequate to use the symbols ti as time shares for both individuals, but as we only want to see the structure of (3.21)–(3.22), we will not make any use of the ti themselves. This structure is that in terms of the variables x1 =k, kx2 or ky1 , y2 =k. Equations (3.21)–(3.22) look exactly like (3.3). Provided k1, the first individual is k2 times more efficient in the first activity (at each level of effort), whereas the second individual is k2 times more efficient in the second activity. Given our above digression, we can hence rephrase (3.6) and (3.9) as x 2 x  1 1  kx2 þ 2 þ kx2 ¼ B k k

ð3:23Þ

  y2  2 y2  A ky1  þ 2 ky1 þ ¼ B: k k

ð3:24Þ

A and

Figure 3.3 shows a case of high asymmetry, k2 ¼ 16. In this figure, we put both efficiency frontiers in the same coordinate system. The picture also displays a wage

x2,y2

x1,y1

Fig. 3.3 The case of two asymmetric individuals

3 Smith–Ricardo Specialization in the Presence of Tiring Effects

55

ratio line (assuming equal wage rates for the two activities), and we see very different choices made by the individuals. However, despite the 16-fold comparative advantage, there is not any complete specialization. Before concentrating on formal detail, we display a companion to Fig. 3.2 for the asymmetric case as seen in Fig. 3.4. Again, we keep one of the efficiency frontiers with its coordinate system fixed, turn the other 180 around, and let it slide down at tangency. We note the total efficiency curve for the two individuals. It is noteworthy that, despite the asymmetry, the total efficiency curve is almost as round as that of Fig. 3.2. We will consider very extreme asymmetries at the end of this paper. So, let us return to formal detail. Again we associate Lagrangian multipliers m; v with the constraints (3.23)–(3.24), assume arbitrary wage rates w1 , w2 and maximize the value of total work effort for the two individuals according to (3.16). The result, corresponding to (3.17)–(3.18) above is x  2m 1  kx2 w1 ¼ 1þA k k

!

 y2  ¼ 2vk 1 þ A ky1  k

! ð3:25Þ

and x  1  kx2 w2 ¼ 2mk 1  A k

!

!  2v y2  1  A ky1  ¼ : k k

x2+y2

x1+y1

Fig. 3.4 Aggregate efficiency frontier for two asymmetric individuals

ð3:26Þ

56

T. Puu

We can eliminatese, n between (3.25) and (3.26), obtaining: x

 1 w1  12  kx2 ¼ ww21 k1 k A w2 þ k 2 1

ð3:27Þ

and 

ky1 

w 2 y2  1 w12  k : ¼ w1 k A w2 þ k 2

ð3:28Þ

In this way we obtain the composite rescaled new variables (x1 =k  kx2 ) and (ky1  y2 =k), as dependent on known coefficients alone, the wage ratio, the asymmetry coefficient, and the rest of the technical coefficients as determined by (3.5). In combination with (3.23)–(3.24), (3.27)–(3.28) enable us to derive the efficiency frontiers for different asymmetry coefficients. The derivation is messy without being instructive, so we let the computer do the job. The result is shown in Fig. 3.5. We see that the higher the asymmetry factor, the more Leontief like the possibility frontier becomes, which means that an optimum close to the ‘‘knee’’ is chosen for almost all wage ratios. However, note that the outmost frontier is obtained for the enormous asymmetry factor of k2 ¼ 625.

x2+y2

x1+y1

Fig. 3.5 Efficiency frontiers for different asymmetry coefficients

3 Smith–Ricardo Specialization in the Presence of Tiring Effects

57

Another fact shown is that the higher the asymmetry, the further out the efficiency frontier is located. This can illustrate Smith’s argument that complementary specialization among manpower enhances overall productivity. Yet, with the tiring factor accounted for here, there is never complete specialization.

References Castiglione B (1507) Il Cortegiano, English translation: The book of the courtier. Penguin, London, 1976 Ricardo D (1817) Principles of political economy and taxation. Everymans Library Reprint, 1912 Smith A (1776) An inquiry into the nature and causes of the wealth of nations. Everymans Library, 1910

Chapter 4

Dynamics of Innovation Fields with Endogenous Heterogeneity of People Masahisa Fujita

4.1

4.1.1

Introduction: Towards the New Economic Geography in the Brain Power Society Welcome to the Brain Power Society

According to Lester Thurow at MIT, advanced countries are shifting from capitalism based on mass production of commodities to the brain power society in which creation of knowledge and information using brain power plays the central role (Thurow 1996). The concept of brain power society is essentially the same as that of ˚ ke Andersson who maintains that advanced countries the C-society advocated by A are leaving the industrial society (with its reliance on simplicity of production and products and the heavy use of natural resources and energy) and entering the C-society with and increasing reliance on creativity, communication capacity, and complexity of products (Andersson 1985). In this paper, the term ‘‘brain ˚ ke Andersson. power society’’ is synonymous with the ‘‘C-society’’ of A The ultimate concern of this paper is the further development of the New Economic Geography (NEG) towards a more comprehensive theory of geographical economics in the age of brain power society, in which the dynamics of the spatial economy arise from the dual linkages in the economic and knowledge fields. Before elaborating this ultimate objective, let me explain briefly what is the socalled the New Economic Geography.

M. Fujita Konan University e-mail: [email protected]

C. Karlsson et al. (eds.), New Directions in Regional Economic Development, Advances in Spatial Science, DOI: 10.1007/978-3-642-01017-0_4, # Springer‐Verlag Berlin Heidelberg 2009

59

60

4.1.2

M. Fujita

The New Economic Geography and Its Future: Incorporating Dual Linkages in Economic and Knowledge Fields

Since about 1990 there has been a renaissance of theoretical and empirical work on economic geography. Among others, the pioneering work of Paul Krugman (1991) on the core–periphery model has triggered a new flow of interesting contributions to economic geography. The work represented by this new school of economics is called the New Economic Geography (NEG).1 The hallmark of the NEG is the presentation of a unified approach to modeling a spatial economy characterized by a large variety of economic agglomeration – one that emphasizes the three-way interaction among increasing returns, transport costs (broadly defined), and the movement of productive factors – in which a general equilibrium model is combined with nonlinear dynamics and an evolutionary approach for equilibrium selection. Figure 4.1 represents the basic conceptual framework of the NEG. The observed spatial configuration of economic activities is considered to be the outcome of a process involving two opposing types of forces, that is, agglomeration (or centripetal) forces and dispersion (or centrifugal) forces.2 As a complicated balance of these two opposing forces, a variety of local agglomeration of economic

Agglomeration forces

Dispersion forces

balance

emergence of local agglomerations and self-organization of the spatial structure slow changes in environments evolution through a sequence of structural changes

Fig. 4.1 The basic framework of the New Economic Geography

1 See Fujita et al. (1999) for a comprehensive manifestation of this approach. See also Fujita and Thisse (2002) and Baldwin et al. (2003) for the recent development of the NEG. For an overview of the NEG, see Fujita and Krugman (2004), Fujita (2005), Fujita and Mori (2005). 2 This hypothesis is not entirely new, of course. For, e.g., Zipf (1949) conjectured that the changing spatial configuration of economic activities was the outcome of the two sets of centripetal (unifying) and centrifugal (diversifying) forces.

4 Dynamics of Innovation Fields with Endogenous Heterogeneity of People

61

activity emerges, and the spatial structure of the entire economy is self-organized. And, with the gradual changes in technological and socioeconomic environments, the spatial system of the economy experiences a sequence of structural changes, evolving towards an increasingly complex system. In this framework, then, the two questions of obvious importance are: Question 1: how to explain the agglomeration forces? Question 2: how to explain the dispersion forces? The answer to Question 2 is rather easy, for the concentration of economic activities at a location will naturally increase factor prices (such as land price and wage rate) and induce congestion effects (such as traffic congestion and air pollution as well as more severe competition among similar firms), which can be readily explained by the traditional economic theory. Thus, the principal concern of the NEG is Question 1, i.e., how to explain the agglomeration forces behind the formation of a large variety of spatial agglomeration such as cities and industrial districts. In most models of the NEG so far, agglomeration forces arise solely from pecuniary externalities through linkage effects among consumers and industries, neglecting all other possible sources of agglomeration economies such as knowledge externalities and information spillovers. This has led to the opinion that the theories of the NEG have been too narrowly focused, ignoring as much of the reality as old trade theory. I fully understand the concern. But, such a narrow focus of the NEG was designed in order to establish a firm micro-foundation of geographical economics based on modern tools of economic theory. It does not necessarily mean that the NEG is limited to such a narrow range of models and issues. On the contrary, its framework is widely open to further development. Indeed, recently many of such possibilities are being explored vigorously by many young scholars.3 That much said, however, I admit that there still remains a big room for further development of the NEG. In particular, there remains one type of agglomeration forces of which micro-foundations have seen little development so far, i.e., the linkages among people through the creation and transfer of knowledge, or in short, the K-linkages. (Hereafter, ‘‘knowledge’’ is defined broadly to include ideas and information.) Traditionally, K-linkage effects have either been called ‘‘knowledge spillovers’’ or ‘‘knowledge externalities’’. However, the term, ‘‘spillovers’’, tends to have a connotation of passive effects. And, the term, ‘‘externalities’’, tends to imply too many different things at once. So, in the remaining discussion, instead of knowledge spillovers or externalities, let me use the term, K-linkages, in order to emphasize that they represent the agglomeration forces resulting from the activities related to both the ‘‘creation of knowledge’’ and the ‘‘transfer of knowledge’’ or ‘‘learning’’ (either in an active way or a passive way). In contrast to the K-linkages, the traditional linkages through the production and transactions of (traditional) goods and services may be

3

See those articles reviewed in Fujita and Mori (2005).

62

M. Fujita

called the E-linkages (where ‘‘E’’ represents the economic activities in the traditional economics). Using such a terminology, we may imagine that the agglomeration forces in the real world arise from the dual effects of E-linkages and K-linkages. In this context, we conjecture that the role of K-linkages has been becoming increasingly more dominant recently. Yet, developing the micro-foundations of K-linkages seems to be the most challenging task, largely left for young scholars in the future. This paper represents my modest efforts with my colleagues towards this objective. Needless to say, there has been a great amount of conceptual studies on knowledge externalities/spillovers in a spatial context, starting with Marshall (1890), and including more recent pioneering work such as Jacobs (1969), Andersson (1985) and Lucas (1988) in an urban context, and Porter (1998) in the context of industrial clusters. Yet, it would be fair to say that there is a lot of room left for advancing the micro-foundations of K-linkages in space. Particularly, in developing the microfoundations of K-linkages, ‘‘creation of knowledge’’ must be clearly distinguished from ‘‘transfer of knowledge’’ or ‘‘learning’’. Furthermore, for the creation of new ideas, cooperation among heterogeneous people is essentially important. Yet, through communication and migration, the degree of heterogeneity of people in a region changes over time. Thus, the nature of K-linkages is essentially dynamic, and hence their full-fledged treatment requires a dynamic framework as elaborated in the next section.

4.1.3

Dynamics of Innovation Fields Through the Endogenous Heterogeneity of Brains

Figure 4.2 represents abstractly the cooperative process of knowledge creation by two persons, i and j, when they meet and collaborate to create new ideas (or new knowledge) together. The left circle, Ki , represents the state of knowledge, or just knowledge, of person i (at the time of meeting), whereas the right circle, Kj represents the knowledge of person j. The overlapping area, Cij , represents their knowledge in common, or just common knowledge,4 whereas the left area, Dij ¼ Ki  Cij , shows the differential knowledge of person i from j, the right area Dji ¼ Kj  Cij the differential knowledge of person j from i. Through mutual communication and discussion based on the common knowledge Cij , the two persons endeavor to develop new ideas by combining their differential knowledge Dij and Dji . This joint process of knowledge creation can

4

Here, ‘‘common knowledge’’ represents simply the short expression of ‘‘the knowledge in common’’ or ‘‘mutual knowledge’’. It is not the term used in game theory.

4 Dynamics of Innovation Fields with Endogenous Heterogeneity of People

Ki Dij

Kj

Cij

Differential

Common

Knowledge

Knowledge of person

63

i

Dji Differential Knowledge of person

j

Fig. 4.2 Cooperative process of knowledge creation

be expected to be most productive when the proportions of the three components, i.e., the common knowledge (Cij ), the differential knowledge of person i (Dij ), and the differential knowledge of person j (Dji ), are well balanced. A sufficient amount of common knowledge is necessary for effective communication between two persons. Furthermore, if one person does not have a sufficient amount of differential knowledge, there is little motivation for the other person to meet and collaborate. In other words, too much common knowledge means little heterogeneity or originality in the collaboration, unable to yield enough synergy. Therefore, in general, for a cooperative process of knowledge creation by a group of people to be productive, both a sufficient heterogeneity and a sufficient common base in their states of knowledge are essential. When such a delicate balance in their states of knowledge holds, an unexpected synergy may be created from their close collaboration. Actually, this observation is not entirely new. We have, e.g., an old Chinese saying, ‘‘San ge chou pi jiang, Di ge Zhuge Liang’’ which roughly means ‘‘With three ordinary persons getting together, splendid ideas will come out’’. However, any nice saying must be taken with caution, for it may imply an antinomy. Concerning the previous Chinese saying, we may continue: ‘‘But, after three ordinary persons meeting for three months, no more splendid idea will come out’’. Likewise, returning to Fig.4.2 even when the two persons have initially a sufficient heterogeneity in their states of knowledge, if they continue a close cooperation in knowledge creation, their heterogeneity may keep shrinking. This is because the very cooperative process of knowledge creation results in the expansion of their common knowledge through both the sharing of newly created ideas and the transfer of differential knowledge to each other. Thus, unless some additional complementary mechanisms are not working, the cooperative process of knowledge creation among the same group of people tends to become less productive eventually.

64

4.2

M. Fujita

The Model

Building upon what has been discussed above, in this section, I present a micromodel of knowledge creation through the interaction of a group of people, which has been developed by Berliant and Fujita (2007).5 In describing the model, the analogy between partner dancing and working jointly to create and exchange knowledge is useful, so we will use terms from these activities interchangeably. We assume that it is not possible for more than two persons to meet or dance at one time, though more than one couple can dance simultaneously. When agents meet, they create new, shared knowledge, thus building up knowledge in common. When agents are not meeting with each other, their knowledge base grows more different. The fastest rate of knowledge creation occurs when common and differential knowledge is in balance.6 Specifically, suppose that there exist N persons in the economy. Consider a given time t, and focus on two persons i and j. And, let in terms of Fig.4.1, ndij ðtÞ be the size of Dij , the differential knowledge of person i from j; ncij ðtÞ be the size of Cij , the common knowledge for person i and j; ndji ðtÞ be the size of Dji , the differential knowledge of person j from i. And let ni ðtÞ ¼ ncij ðtÞ þ ndij ðtÞ;

ð4:1Þ

nj ðtÞ ¼ ncij ðtÞ þ ndji ðtÞ;

ð4:2Þ

so that ni ðtÞ represents the size of Ki , the knowledge of person i at time t; nj ðtÞ the size of Kj , the knowledge of person j at time t. Knowledge is a set of ideas that are possessed by a person at a particular time. However, knowledge is not a static concept. New knowledge can be produced either individually or jointly, and ideas can be shared with others. But all of this activity takes time. Now we describe the components of the rest of the model. To keep the description as simple as possible, we focus on just two agents, i and j. At each time, each faces a decision about whether or not to meet with others. If two agents want to meet at a particular time, a meeting will occur. If an agent decides not to meet with anyone at a given time, then the agent produces separately and also creates new knowledge separately, away from everyone else. If two persons do decide to meet at a given time, then they collaborate to create new knowledge

5

See Berliant and Fujita (2007) for the further elaboration of the following model. For simplicity, we employ a deterministic framework. It seems possible to add stochastic elements to the model, but at the cost of complexity. It should also be possible to employ the law of large numbers to a more basic stochastic framework to obtain equivalent results.

6

4 Dynamics of Innovation Fields with Endogenous Heterogeneity of People

65

together. Here we limit the scope of our analysis to knowledge creation as opposed to knowledge transfer.7 What do the agents know when they face the decision about whether or not to meet a potential partner j at time t? Each person knows both Ki ðtÞ and Kj ðtÞ. In other words, each person is aware of his own knowledge and is also aware of others’ knowledge. Thus, they also know ni ðtÞ, nj ðtÞ, ncij ðtÞ ¼ ncji ðtÞ, ndij ðtÞ, and ndji ðtÞ (for all j 6¼ i) when they decide whether or not to meet at time t. The notation for whether or not a meeting of persons i and j actually occurs at time t is: dij ðtÞ ¼ dji ðtÞ ¼ 1 if a meeting occurs and dij ðtÞ ¼ dji ðtÞ ¼ 0 if no meeting occurs at time t. For convenience, we define dii ðtÞ ¼ 1 when person i works in isolation at time t, and dii ðtÞ ¼ 0 when person i meets with another person at time t. Next, we must specify the dynamics of the knowledge system and the objectives of the people in the model in order to determine whether or not two persons decide to meet at a particular time. The simplest piece of the model to specify is what happens if there is no meeting between person i and anyone else, so i works in isolation. Let aii ðtÞ be the rate of creation of new ideas created by person i in isolation at time t (this means that i meets with itself). Then we assume that aii ðtÞ ¼ ani ðtÞ when dii ðtÞ ¼ 1;

ð4:3Þ

where a is a positive constant. So we assume that if there is no meeting at time t, individual knowledge grows at a rate proportional to the knowledge already acquired by an individual. If a meeting occurs between i and j at time t (dij ðtÞ ¼ 1), then joint knowledge creation occurs, and it is governed by the following dynamics:8 h i13 aij ðtÞ ¼ b ncij ðtÞndij ðtÞndji ðtÞ

when

dij ðtÞ ¼ 1 for j 6¼ i;

ð4:4Þ

where b is a positive constant. So when two people meet, joint knowledge creation occurs at a rate proportional to the normalized product of their knowledge in common, the differential knowledge of i from j, and the differential knowledge of j from i. The rate of creation of new knowledge is highest when the proportion of ideas in common, ideas exclusive to person i, and ideas exclusive to person j are split evenly. Ideas in common are necessary for communication, while ideas exclusive to one person or the other imply more heterogeneity or originality in

7

In an earlier version of this paper, Berliant and Fujita (2004, available at http://econpapers.hhs. se/paper/wpawuwpga/0401004.htm), we have worked out the details of the model with both knowledge creation and transfer when there are only two persons, and found no essential difference in the results. However, in the N person case, it is necessary to keep track of more details of who knows which ideas, and thus the model becomes very complex. This extension is left to future work. 8 See Berliant and Fujita (2007, Sect.4.6) for a more general form of joint knowledge creation.

66

M. Fujita

the collaboration. If one person in the collaboration does not have exclusive ideas, there is no reason for the other person to meet and collaborate. Whether a meeting occurs or not, there is production in each period for both persons. Felicity in that time period is defined to be the quantity of output.9 Define yi ðtÞ to be production output (or felicity) for person i at time t. Normalizing the coefficient of production to be 1, we take yi ðtÞ ¼ ni ðtÞ:

ð4:5Þ

So, y_i ðtÞ ¼ n_ i ðtÞ: By definition, y_i ðtÞ n_ i ðtÞ ¼ yi ðtÞ ni ðtÞ

ð4:6Þ

which represents the rate of growth of income. We now describe the dynamics of the system, dropping the time argument. Let us focus on agent i, as the expressions for the other agents are analogous. y_i ¼ n_ i ¼

N X

dij aij ;

ð4:7Þ

j¼1

n_ cij ¼ dij aij n_ dij ¼

X

for

dik aik

all j 6¼ i;

for

all j 6¼ i:

ð4:8Þ ð4:9Þ

k6¼j

Equation (4.7) means that the increase in the knowledge of person i is the sum of the knowledge created in isolation and the knowledge created jointly with someone else. Equation (4.8) means that the increase in the knowledge in common for persons i and j equals the new knowledge created jointly by them. This is based on our previous assumption that there is no transfer of existing knowledge between agents even when they are meeting together. Finally, (4.9) means that all the knowledge created by person i either in isolation or jointly with persons other than person j becomes a part of the differential knowledge of person i from person j. By definition, it is also the case that N X

dij ¼ 1:

j¼1

9

Given that the focus of this paper is on knowledge creation rather than production, we use the simplest possible form for the production function.

4 Dynamics of Innovation Fields with Endogenous Heterogeneity of People

67

Furthermore, on the equilibrium path it is necessary that dij ¼ dji

for all

i and j:

Concerning the rule used by an agent to choose their best partner, to keep the model tractable in this first analysis, we assume a myopic rule. At each moment of time t, person i would like a meeting with person j when the increase in their rate of output while meeting with j is highest among all potential partners, including himself. Note that we use the increase in the rate of output y_i ðtÞ rather than the rate of output yi ðtÞ since in a continuous time model, the rate of output at time t is unaffected by the decision made at time t about whether to meet. As we are attempting to model close interactions within groups, we assume that at each time, the myopic persons interacting choose a core configuration. In order to analyze our dynamic system, we first divide all of our equations by the total number of ideas possessed by i and j: nij ¼ ndij þ ndji þ ncij

ð4:10Þ

and define new variables mcij  mcji ¼ mdij ¼

ndij n

; ij

ncij nij

¼

mdji ¼

ncji nij ndji nij

; :

By definition, mdij represents the percentage of ideas exclusive to person i among all the ideas known by person i or person j. Similarly, mcij represents the ideas known in common by persons i and j among all the ideas known by the pair. From (4.10), we obtain 1 ¼ mdij þ mdji þ mcij :

ð4:11Þ

Then, using (4.7)–(4.9) and ( 4.11), we can rewrite the income growth rate, (4.6), as follows:10 y_i n_ i ¼ ¼ dii a þ y i ni

10

X j6¼i

dij

h i13 b ð1  mdij  mdji Þmdij mdji 1  mdji

;

For details of the analyses in the rest of this paper, see Berliant and Fujita (2007).

ð4:12Þ

68

M. Fujita

where h   i h  i13 m_ dij ¼ að1  mdij Þ dii 1  mdji  djj mdij  dij mdij b 1  mdij  mdji mdij mdji    X b1  md  md md md 13 ik ki ik ki d d þ 1  mij 1  mji dik d 1  m ki k6¼i;j h  i13   X b 1  mdjk  mdkj mdjk mdkj  1  mdij mdij djk for i; j ¼ 1; 2; . . . ; N : 1  mdkj k6¼i;j ð4:13Þ At time t, since yi ðtÞ is a state-variable, maximizing y_i ðtÞ is equivalent to maximizing the growth rate, y_ i ðtÞ=yi ðtÞ. Hence, at each moment of time, the equilibrium values of dij (i; j ¼ 1; 2; . . . ; N) are to be determined as the core of the game in which each agent wishes to maximize the growth rate of income given by (4.12). Thus, the dynamics of the system are described in terms of mdij (i; j ¼ 1; 2; . . . ; N) only.

4.3 4.3.1

Equilibrium Dynamics The General Framework

The model with only two people is very limited. Either two people are meeting or they are each working in isolation. With more people, the dancers can be partitioned into many pairs of dance partners. Within each pair, the two dancers are working together, but pairs of partners are working simultaneously. This creates more possibilities in our model, as the knowledge created within a dance pair is not known to other pairs. Thus, knowledge differentiation can evolve between different pairs of dance partners. Furthermore, the option of switching partners is now available. We limit ourselves to the case where N is divisible by 4. This is a square dance on the vertices of the Hilbert cube. When the population is not divisible by 4, our most useful tool, symmetry, cannot be used to examine dynamics. Although this may seem restrictive, when N is large, asymmetries apply only to a small fraction of the population, and thus become negligible. In the general case, we impose the assumption of pairwise symmetric initial heterogeneity conditions for all agents. The initial state of knowledge is symmetric among the dancers, and given by ncij ð0Þ ¼ nc ð0Þ

for all i 6¼ j;

ð4:14Þ

ndij ð0Þ ¼ nd ð0Þ

for all i 6¼ j:

ð4:15Þ

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At the initial state, each pair of dancers has the same number of ideas, nc ð0Þ, in common. Moreover, for any pair of dancers, the number of ideas that one dancer knows but the other does not know is the same and equal to nd ð0Þ. Given that the initial state of knowledge is symmetric among the four dancers, it turns out that the equilibrium configuration at any time also maintains the basic symmetry among the dancers. When all dancers are pairwise symmetric to each other, that is, when mdij ¼ mdji

for all i 6¼ j

ð4:16Þ

the income growth rate (4.12) is simplified as X y_i n_ i ¼ ¼ dii a þ dij gðmdij Þ yi ni j6¼i

ð4:17Þ

and the dynamics (4.13) can be rewritten as m_ dij 1  mdij

h   i ¼ a dii 1  mdij  djj mdij  dij mdij gðmdij Þ  X X þ 1  mdij dik gðmdik Þ  mdij djk gðmdjk Þ; k6¼i; j

ð4:18Þ

k6¼i; j

where the function gðmÞ is defined as

gðmÞ ¼ b



1 m  m 2 3 1 1m 1m

ð4:19Þ

which represents the growth rate when the two persons meet. Figure 4.3 illustrates the graph of the function gðmÞ as a bold line for b ¼ 1. Differentiating gðmÞ yields, we can readily see that

 5 2   Thus, gðmÞ is strictly quasi-concave on 0; 12 , achieving its maximal value at mB ¼ 25; we call the latter the ‘‘Bliss Point’’. It is the point where the rate of increase in income or utility is maximized for each person. Next, taking the case of N ¼ 4, we illustrate the possible equilibrium configurations, noting that the equilibrium configuration can vary with time. Figure 4.4 gives the possibilities at any fixed time for N ¼ 4. Given that the initial state of knowledge is symmetric among the four dancers, as noted above, the equilibrium configuration at any time also maintains the basic symmetry among dancers. g0 ðmÞ

> 0
a for any possible dance pairs consisting of i and j, no person wishes to dance alone at the start. However, since the value of gðmdij ð0ÞÞ is the same for all possible pairs, all forms of (b-1) to (d) in Fig. 4.4 are possible equilibrium dance configurations at the start. To determine which one of them will actually take place on the equilibrium path, we must consider the second derivative of income for all persons. In general, consider any time at which all persons have the same composition of knowledge: mdij ¼ md

for all i 6¼ j;

ð4:22Þ

where gðmd Þ > a: Focus on person i; the equations for other persons are analogous. Since person i does not wish to dance alone, it follows that dii ¼ 0

and

X

dij ¼ 1:

j6¼i

Substituting (4.22) and (4.23) into (4.17) yields y_i ¼ gðmd Þ: yi

ð4:23Þ

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Likewise, substituting (4.22) and (4.23) into (4.18) and arranging terms gives     m_ dij ¼ ð1  md Þgðmd Þ 1  2md  1  md dij :

ð4:24Þ

_ above is independent of the values of dij Since the income growth rate y=y (j 6¼ i), in order to examine what values of dij (j 6¼ i) person i wishes to choose, we must consider the time derivative of y_i =yi . From this second-order condition, we can show that each person, say i, chooses the optimal strategy such that dij ¼

1 N1

for all j 6¼ i:

ð4:25Þ

The vector of optimal strategies is the same for all persons. Thus, all persons agree to a square dance in which each person rotates through all N  1 possible partners while sharing the time equally. The intuition behind this result is as follows. The condition md < 2=5  mB means that the dancers have relatively too many ideas in common, and thus they wish to acquire ideas that are different from those of each possible partner as fast as possible. That is, when mJ < mdij ¼ md < mB in Fig. 4.3, each dancer wishes to move the knowledge composition mdij to the right as quickly as possible, thus increasing the growth rate gðmdij Þ as fast as possible. This means that when mJ < md ð0Þ ¼ mdji ð0Þ < 2=5 ð¼ mB Þ for all i 6¼ j, on the equilibrium path, the square dance with dij ¼ 1=ðN  1Þ for all i 6¼ j takes place at the start. Then, since the symmetric condition (4.22) holds thenceforth, the same square dance will continues as long as mJ < md < 2=5 ð¼ mB Þ. The dynamics of this square dance are as follows. Setting mdij ¼ md and dij ¼ 1=ðN  1Þ in (4.24), we obtain m_ d ¼ ð1  md Þgðmd Þ

ðN  2Þ  ð2N  3Þmd : N1

ð4:26Þ

Setting m_ d ¼ 0 and considering that md < 1, we obtain the sink point md ¼

N2 : 2N  3

ð4:27Þ

Surprisingly, when N ¼ 4, md ¼ 2=5 ¼ mB . The value of m_ d is positive when m < mB ¼ 2=5, and zero if md ¼ 2=5. Hence, beginning at any point md ð0Þ ¼ 2=5, the system moves to the right, eventually settling at the bliss point mB . Since the right hand side of (4.27) is increasing in N, d

md ¼

N2 > 2=5  mB 2N  3

when N > 4:

ð4:28Þ

Hence, when N > 4 and N is divisible by 4, beginning at any point m < md ð0Þ < 2=5, the system moves to the right and reaches mB ¼ 2=5 in finite J

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time. When N agents reach the bliss point mB , they break into groups of 4 to maintain heterogeneity at the bliss point. Next, when 0  md ð0Þ < mJ , it is obvious that the four persons work alone until they reach mJ . Then they follow the path explained above, eventually reaching mB . Case 2: ^ mB < md ð0Þ  m Next, let us consider the dynamics of the system when it begins to the right of ^ < mI .11 The equilibrium process takes the following mB ¼ 2=5 but to the left of m three phases. Phase 1: Since the initial state reflects a higher degree of heterogeneity than the bliss point, the dancers want to increase the knowledge they have in common as fast as possible, which leads to couple dances. Thus, person i wishes to choose any partner, say k, and set dik ¼ 1, whereas dij ¼ 0 for all j 6¼ k. The situation is the same for all dancers. Hence, without loss of generality, we can assume that N persons agree at time 0 to form the following combination of partnerships: P1  ff1; 2g; f3; 4g; f5; 6g; . . . ; fN  1; Ngg

ð4:29Þ

and initiate pairwise dancing such that dij ¼ dji ¼ 1

for fi; jg 2 P1 ;

dij ¼ dji ¼ 0

for fi; jg 2 = P1 :

ð4:30Þ

The same pairwise dance, however, cannot continue too long by the following reason. On one hand, the proportion of differential knowledge for each couple, say f1; 2g, decreases with time, making the partnership less productive eventually. On the other hand, the proportion of the differential knowledge increases for any pair of persons, say f1; 3g, who are not dancing together. Thus, eventually, the shadow partnership f1; 3g 2 = P1 becomes more productive than the actual partnership f1; 2g 2 P. Thus, there exists a switching time t0 at which each dancer switches to a new partner. Phase 2: One example of new equilibrium partnerships at the switching time t0 is given by P2  ff1; 3g; f2; 4g; f5; 7g; f6; 8g; . . . ; fN  3; N  1g; fN  2; Ngg

ð4:31Þ

meaning that the first four persons form a group and exchange partners, the next four persons form another group and switch partners, and so on. (There exist many other possibilities for equilibrium partnerships to be chosen by N dancers at time t0 . It turns out, however, that the essential characteristics of equilibrium dynamics are ^ see Berliant and Fujita (2007). For the determination m,

11

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not affected by the choice at time t0 . Hence, let us assume that N persons agree to choose the new partnerships P2 at time t0 .) It turns out, however, that these new partnerships last only for a limited time. To examine this point, let us notice that in the dance form P2 , each group of four persons is isolated from everyone else. Thus, in the sequel, we focus on the dynamics of a four-person group, 1, 2, 3 and 4. Under the partnership P2 , since md12 ðtÞ is increasing with time while md13 ðtÞ is decreasing, there exists a time t00 at which md12 ðtÞ and md13 ðtÞ become the same, md12 ðt00 Þ ¼ md13 ðt00 ÞmB

ð4:32Þ

which can be shown to occur in the left of the bliss point mB . Thus, if partnerships f1; 3g and f2; 4g were maintained beyond time t00 , then it would follow from (4.20) that gðmd12 ðtÞÞ > gðmd13 ðtÞÞ for t > t00 : This implies that the same partnerships cannot be continued beyond t00 . To see what form of dance will take place after t00 , first note that dancers cannot go back to the previous form of partnerships f1; 2g and f3; 4g. If they did so, then the proportion of the knowledge in common for the actual partners f1; 2g would increase, while the proportion of the differential knowledge for the shadow partnership f3; 4g would increase. This means that the following relationship, md12 ðtÞ < md ðt00 Þ < md13 ðtÞ < mB holds immediately after t00 , and thus gðmd12 ðtÞÞ < gðmd13 ðtÞÞ which contradicts the assumption that f1; 2g is the actual partnership. Furthermore, relation (4.32) implies that under any possible partnership, the following inequality gðmd13 ðtÞÞ > gðmd14 ðtÞÞ holds immediately after t00 . Thus, immediately after time t00 , the equilibrium dance cannot include partnerships f1; 4g and f2; 3g. Hence, provided that gð1=3Þ > a, we can see from Fig.4.4 that the only possible equilibrium configuration immediately after t00 is a square dance in the form (c-1) involving a rapid rotation of non-diagonal partnerships, f1; 2g, f1; 3g, f2; 4g and f3; 4g. That is, for dancer 1, d11 ¼ 0 and d1j ¼ 12 if j ¼ 2 or 3, d14 ¼ 0. Analogous expressions hold for the other dancers. Phase 3: The dynamics for this square dance under the form (c-1) are as follows. We set mdij  md

for fi; jg 2 P2 :

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Then, since conditions (4.22) and (4.23) hold also in the present context, setting dij ¼ 1=2 in (4.24), we get m_ d ¼ ð1  md Þgðmd Þ

1  3md ; 2

which is negative when md > 13, and zero if md ¼ 13. Thus, beginning at any point md ðt00 Þ > 13, the system moves to the left, eventually settling at md ¼ 13. Case 3: ^ < md ð0Þ  mI m ^ < md ð0Þ  mI . As in Case 2, dancers are Next suppose md ð0Þ is such that m more heterogeneous than at the bliss point, so they would like to increase the knowledge they hold in common through couple dancing, for example using configuration (b-1) in Fig. 4.4. The initial phase of Case 3 is the same as the initial phase of Case 2. However, since gðmd12 ðtÞÞ > gðmd13 ðtÞÞ for all t before md12 ðtÞ reaches mJ , whereas gðmd12 ðtÞÞ > a > gðmd13 ðtÞÞ when md12 ðtÞ reaches mJ . So each dancer keeps their original partner as the system climbs up to B and on to J. When the system reaches md ðtÞ ¼ mJ , each dancer uses fractional dij to attain mJ by switching between working in isolation and dancing with their original partner. Case 4: mI < md ð0Þ  1=2 Finally, suppose md ð0Þ > mI . Then, gðmd ð0ÞÞa, and hence there is no reason for anyone to form a partnership. Thus, each person dances alone forever, and eventually reaches md ¼ 1=2. Compiling all four cases, we obtain the result summarized in Fig. 4.5. There are important remarks to be made about the result. First, the sink point changes discontinuously with changes in the initial conditions. Second, from each set of initial conditions, the N persons eventually divide into many separate groups between which no interaction occurs. Thus, from an initial state that is symmetric, we obtain an equilibrium path featuring asymmetry. Third, concerning the welfare properties of the equilibrium path, the most surprising result is with Case 1. That is, whenever md ð0Þ < mB , the equilibrium path either approaches (when N ¼ 4) or reaches in finite time (when N > 4) the most productive state, mB . Clearly, initial heterogeneity plays an important role in the efficiency properties of the equilibrium path. What distinguishes Case 1, aside from a relatively homogeneous beginning, is that the dancers can switch partners rapidly enough to increase heterogeneity while at the same time maximizing the increase in output. That is because each agent spends 1=ðN  1Þ of the time dancing with any particular agent, and

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ðN  2Þ=ðN  1Þ of the time dancing with others. This is what leads to the most productive state.12 Bearing in mind the limitations of the model, it may have empirical relevance. The main result may explain the agglomeration of a large number of small firms in Higashi Osaka or in Ota ward in Tokyo, each specializing in different but related manufacturing services. Another example is the third Italy, which produces a large variety of differentiated products. Yet another example is the restaurant industry in Berkeley, California. In each case, tacit knowledge accumulated within firms plays a central role in operation of the firms.

4.4

Conclusion

We have presented a micro-model of knowledge creation through the interaction of a group of people. Our model incorporates two key aspects of the cooperative process of knowledge creation: (1) heterogeneity of people in their state of knowledge is essential for successful cooperation in the joint creation of new ideas, while (2) the very process of cooperative knowledge creation affects the heterogeneity of people through the accumulation of knowledge in common. The model features myopic agents in a pure externality model of interaction. Surprisingly, in the general case for a large set of initial conditions we find that the equilibrium process of knowledge creation may converge to the most productive state, where the population splits into smaller groups of optimal size; close interaction takes place within each group only. This optimal size is larger as the heterogeneity of knowledge is more important in the knowledge production process. Equilibrium paths are found analytically, and they are a discontinuous function of initial heterogeneity. However, what we have done so far is, in effect, to open Pandora’s box, scattering around a great number of new problems to be investigated further. Indeed, to take our model more realistic and interesting, we must extend it by considering/introducing various new elements such as knowledge transfer, knowledge structures and hierarchies, side payments and the markets for ideas, foresights and strategic behavior, and uncertainty and stochastic elements. In particular, we must return to our original motivation for this model, as stated in the introduction. That is, location seems to be an essential feature of knowledge creation and transfer, so regions and migration are important, along with urban economic concepts more generally. Thus, incorporating locations/regions in our model, we may be able to move one step closer to our ultimate objective of developing a comprehensive 12

Here, it is natural ask why the optimal group size in knowledge production is four. Actually, using a more general functional form of joint knowledge production, Berliant and Fujita (2007) shown that when differential knowledge is relatively more important than common knowledge in knowledge production, the optimal group size is larger.

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theory of geographical economics in the brain power society, in which the dual linkages in the economic and knowledge fields work in unison. As the model becomes more realistic and hence more complex, however, its analytical tractability reaches the limit soon. Eventually, thus, we must appeal to computer simulations. In particular, the evolutionary process of knowledge creation and transfer may be simulated with the help of multi-agent-based simulation. Acknowledgments The author is grateful to the three anonymous referees and to David Batten, ˚ ke Andersson and other participants in the workshop on ‘‘Innovation, Dynamic Regions and A Regional Dynamics’’ for valuable comments on the earlier drafts of this paper. The author is also grateful for Grants Aid for Scientific Research Grants S 13851002 and A 18203016 from the Japanese Ministry of Education and Science.

References ˚ E (1985) Kreativitet: storstadens framtid. Prisma, Stockholm Andersson A Baldwin R, Forslid R, Martin P, Ottaviano G, Robert-Nicoud F (2003) Economic geography and public policy. Princeton University Press, Princeton Berliant M, Fujita M (2007) Knowledge creation as a square dance on the Hilbert cube. Institute of Economic Research, Kyoto University, Kyoto (mimeo) Fujita M (2005) Spatial economics. Edward Elgar, Cheltenham Fujita M, Krugman P (2004) The new economic geography: past, present and the future. Pap Reg Sci 83:149–164 Fujita M, Mori T (2005) Frontiers of the new economic geography. Pap Reg Sci 84(3):377–405 Fujita M, Thisse J-F (2002) Economics of agglomeration: cities, industrial location, and regional growth. Cambridge University Press, Cambridge Fujita M, Krugman P, Venables AJ (1999) The spatial economy: cities, regions and international trade. MIT, Cambridge, MA Jacobs J (1969) The economy of cities. Random House, New York Krugman P (1991) Increasing returns and economic geography. J Polit Econ 99:483–499 Lucas RE Jr (1988) On the mechanics of economic development. J Monet Econ 22:2–42 Marshall A (1890) Principles of economics. Macmillan, London Porter ME (1998) On competition. A Harvard business review book. Harvard Business School Press, Boston, MA Thurow LC (1996) The future of capitalism. Leighco, New York Zipf G (1949) Human behavior and the principle of least effort. Addison-Wesley, New York

Chapter 5

Economics of Creativity ˚ ke E. Andersson A

5.1

Division of Labor by Comparative Advantage or Creativity

Most of us have got an education adapted to the demands for specialized labor emanating in industry or public administration. Most of the jobs have been decided according to the basic principle of division of labour, generating productivity of the work. According to this principle the worker should be specialized to perform certain highly specialized tasks without any greater space for improvisation or change of work routines. Adam Smith (1776, 1904) argued strongly in favour of a far-going division of labor (or specialization of the workforce) as a way of achieving growth of productivity. However, Adam Smith clearly saw the potential conflict between creativity and productivity by division of labor and specialization of the work force: In the progress of the division of labor, the employment of the far greater part of those who live by labor, that is, of the great body of the people, comes to be confined to a few very simple operations; frequently to one or two. But the understandings of the greater part of man are necessarily formed by their ordinary employments. The man whose whole life is spent in performing a few simple operations, of which the effects to are, perhaps, always the same, or very nearly the same, has no occasion to exert his understanding, or to exercise his invention in finding out expedience for removing difficulties which never occur. He naturally loses, therefore, the habit of such exertion and generally becomes as stupid and ignorant as it is possible for a human creature to become. (Wealth of Nations, II)

The industrial society became based on a far-going division of labor and a hierarchical organization of the firms. Research and development became a sort of tinkering, oriented to improvement of the techniques for producing a given set of goods. Creativity was looked upon as a social side-activity for artists, scientists and inventors. The first stage of an upgrading of creativity was to occur during the Second World War, when decision-makers realized that at least chemists and physicists ˚ .E. Andersson A Jo¨nko¨ping International Business School, Jo¨nko¨ping University e-mail: [email protected]

C. Karlsson et al. (eds.), New Directions in Regional Economic Development, Advances in Spatial Science, DOI: 10.1007/978-3-642-01017-0_5, # Springer‐Verlag Berlin Heidelberg 2009

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80

were of value in military projects. The largest example was the Manhattan project, within which scientists were organized into secret research groups with a mission to transform the knowledge of theoretical physics into an atom bomb (Fermi 1954/1994). On the basis of this experiment in organized creativity American think tanks became a way of improving the cooperation between creative scientific research and the development and innovation of new products in the post-war American industry. A real integration of creative research and technological development was, however, not realized before the end of industrialism in USA and Western Europe. In the early 1970s Daniel Bell (1973) formulated a scenario describing a new postindustrial society. It was based on the observation that manufacturing industry in USA and Western Europe had already seen its employment stagnating and even declining. It became obvious that the highly industrialized societies could no longer expect an increasing employment in the production of material goods. Many of the analysts of the 1970s expected service industries to become the new guarantee of full employment. Few analysts expected creativity in science, technological research and development, design, entertainment and arts to become an important factor explaining growth of real income, employment and general welfare in the postindustrial society. Real developments in the structures of some regions, e.g. San Francisco Bay with Silicon Valley, Route 128 around Boston, Massachusetts and Cambridge, UK, saw a new type of interaction between creative scientists and industry, indicating a new role for creativity in the economic system. In recent decades the role of creativity as a factor of economic development has been realized in somewhat surprising directions. First, there has been a rapid increase in resources allocated to scientific research. The number of science articles published has been increasing at approximately 7% annually since 1975 (Andersson and Persson 1993). Second, industrial research and development (R&D) has become a strategic factor of growth policies among firms and governments of OECD-countries since the 1960s. This development has triggered numerous scientific papers on the interdependencies between R&D and economic growth (see, e.g. Uzawa 1965; Shell 1966; Romer 1986). Third, there has been a remarkable growth of the entertainment and arts activities, called Creative Industries by Richard Caves (2000). According to recent statistics consumption of such goods and services has risen to more than 15% of total household consumption in Sweden.

5.2

Mechanisms of Creativity

Creativity is a process based on a capacity. As a process it is dynamic, because creativity always means the emergence of something genuinely new. Discoveries and inventions are outcomes of a creative process. Discovery is based on a capacity to find patterns in a seemingly chaotic world. The real creative capacity lies in the

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Fig. 5.1 This implies that the brain has the tendency to be anchored in the original perception and needs a certain excess supply of information before it can give up the initial interpretation in favor of a new. There is certain stabilization in the already known. Expressed differently, creativity requires a certain degree of instability of the brain. Such instability is evidently there in all of us

ability to comprehend and explain the mechanisms generating such patterns. The detection of a hidden pattern and its transformation into something meaningful is often something suddenly occurring in the brain. The mathematician I. N. Stewart and the psychologist P.L. Peregoy (1983) have, shown by a series of experiments, how the brain can discover a hidden structure. With this experiment they can support the claim that the brain ought to be represented as a non-linear dynamical system. Using Fig. 5.1 they were able to show that the perception of a man is suddenly changed into a clear perception of a woman after three to six steps from left to right and the perception of a woman is suddenly transformed into a perceived man, when starting from the right and moving three to five steps to the left. Inventions and discoveries are different names of the created ideas. An invention mostly starts by perceiving a structure and later suddenly realizing that below this

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surface structure there is a more important deep structure that can be used in the formation of a new principle of composition to be used as an instrument of generating inventions. Margaret Boden (1990) has proposed a subdivision of creativity into different classes. The first class of creativity implies the invention of a completely new principle of construction, composition or set of concepts, providing a new structuring of some problem area. This type of creativity is fundamental or infrastructural. The other type of creativity is built on variations of themes given by a given creative infrastructure. A few examples suffice to clarify the differences. Schoenberg was the creator of the most important principles of composition of 12-tone music and would consequently be seen as the creator of the infrastructure of modernist music. In contrast Anton Webern and Alban Berg would give examples of variational creativity in their application and further development of the original new principles of composition as created by Schoenberg. When applied to painting, the same principle would imply that Cezanne is the infrastructurally creative artist within modernist painting, while Braque and Picasso would be the most important painters in terms of variational creativity on this basis. In science an example is Inequalities by Hardy et al. (1934). Reformulating many mathematical equations as inequations they formed a basis for much of the developments in mathematical programming developed and innovated in the 1940s. In this context George Dantzig and Harold Kuhn with their formulations of linear and non-linear programming would be examples of variational creators. It ought to be stressed that there is no obvious qualitative distinction between infrastructural and variational creators, except in terms of the potential of further developments on the basis of the infrastructural creators.

5.2.1

Creative Capacity: Acquired or Inherited?

Are all people born creative? There are certain indicators that creativity is not a genetic deviation from the normal but rather a general human capacity. One indicator is the development of the capacity to speak. Already in small children completely new spoken sentences are created. Even the smallest child can create completely new linguistic constructions in their communication with other children and adults. Sometimes they even seem surprised at their own linguistic discoveries and inventions. The capacity of linguistic creation seems to develop by social interaction throughout the life span. The concentration of musical and pictorial creativity onto a minority of the population might just be a consequence of too little of daily training during the early years of childhood. Most of the creative musicians and other artists have had the benefit of an education in the arts from their earliest years. Surprisingly many artists have grown up in an environment rich in artistic activities. Simonton (1984) has used extensive empirical material to show that the early exposure to scientifically or artistically creative personalities is of importance for creativity of young people.

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The formal schooling of children does not generally compensate for the lack of artistic and other creative inspiration in the homes of children. In contrast most educational systems in the old and new industrialized countries have been oriented on diffusion of already well established knowledge later to become useful in manufacturing firms or bureaucracies. This implies that schools have primarily been oriented to the development of discipline and adaptation and to the need for cooperation in groups with specified problems to be solved as rapidly as possible. The education before the university level is rarely oriented to formulation of problems independently and to the generation of different ways of solving such problems. Rather, most education is oriented to learning techniques of how to solve already formulated problems in a way, pleasing to the teacher. The learning of already developed techniques has been favored at the expense of a loss of creativity already during the elementary school years. Gudmund Smith (1990) has in his studies of the psychology of creativity found that the development of creativity during the years of childhood and adolescence follows a typically cyclical pattern. During some of these cyclical periods learning is favored and absorption of education is easy, while in other periods creativity develops rapidly. The ages of development of creativity seem to be between 5 and 7 years, 10–12 years and 17–19 years of age. In most industrial countries the latter two creativity peaks seem to be used by the schools for intensive teaching and examinations, curbing the development of creativity. Smith has even claimed that a school where development of creativity has a priority might need to be free of fixed curricula.

5.3

Creative Personalities

The transformation from an industrial society towards a society based on the exploitation of knowledge, creativity in the arts, design, and entertainment and with an increasing complexity of products will need a better development of as well as use of human creativity. Finding and supporting people, suitable for creative work has become much more important than during the industrial era. Gudmund Smith (1990, 1995) has oriented some of the research of his team towards investigations of creative personality traits. Some of the results can be summarized. First, a typical characteristic of a creative personality is a capacity to formulate and energetically work on the solution to the formulated problem. Sometimes the problem is not conceived as especially interesting by anyone else and is often looked upon as somewhat strange or even bizarre by others. Second, a part of a creative personality is a subjective and quite an emotional relation to the problem which is developed during the period of problem solving. The solution to such an independently formulated problem is often not obviously profitable for anyone. Third, another personality trait is an orientation towards aesthetic solutions to the generated problem.

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Fourth, a general characteristic of creative personalities, according to Smith, is the oceanic capacity. This is a capacity to get a feeling of almost infinite possibilities, when a new creative solution turns out to be correct. This implies an instant and yet sustainable reward of greater importance than external rewards in the form of money or fame. Fifth, creative persons tend to be victims of angst, which according to Smith is the natural companion of creative activities. Sixth, creative persons tend to have – in comparison with the non-creative – a strong interest of their childhood. They often think about it and it is prevalent in their dreams which are more frequent than among non-creative persons. One of the surprising properties of these dreams is that they are described in the interviews of creative persons as dreams in intensive colors. For these and possibly other reasons there is a tendency among creative persons to combine childish behavioral traits with a capacity to concentrate and be quite serious in the process of formulating and solving problems. It does not seem to be the case that very goal-oriented, wealthy homes are the best breeding grounds for the development of creativity among children. Remarkably often creative persons seem to have come from disadvantaged homes.

5.4

Different Capacities of the Creative Mind

In his book How to Solve It, the mathematician George Polya (1945) claims that the most important approach to creative problem formulation and solving is by heuristics or the use of proper analogies: ‘‘Analogy pervades all our thinking, our everyday speech and our trivial conclusions as well as our ways of expression and the highest scientific achievements’’ (Polya 1945, p.37). This is obvious in mathematics but seems to be of relevance also in creative writing and composing. Belonging to some style or genre of literature essentially means that a certain degree of similarity of composition exists. Such formulations are often analogous at least in terms of deep structure. Production requires predictability and structural stability of the process in order to be efficient. Creation is an almost contrary process. The creator has to accept fundamental uncertainty and its companion, structural instability. This implies that creativity can only be achieved by individuals, who have accepted a career with an embedded uncertainty of production and the corresponding uncertainties of income and wealth.

5.5

The Pecuniary Rewards of Creativity

In the scientific world, income is normally secured for the creators by a combination of subsidies and payment for other work than creation of scientific research. In universities much of the salaried time is used for elementary teaching, administration

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and other non-creative activities. The financial rewards for creativity are primarily determined by decisions in public or private funds based on earlier research records and an estimate of the likelihood of success as evaluated by some more or less credible peer group. In the R&D world the financial rewards are calculated with methods similar to the ones used in the evaluation of the returns to material investments, i.e. an estimate is made of the expected net present value and risk. Because of the public nature of knowledge the risk is very large and different procedures to protect the inventions are necessary. The common procedure of protecting a new material product is patenting that exists in all countries prone to imitate new knowledge. Patent rights are regulated by international treaties and give the property right to the proceeds from the new product for a time period of 20 years. However, in reality the rights can normally be executed for approximately 15 years. Because of the delays in production, rights are executed after the patenting has been granted. In the arts world there is a situation somewhat similar to the scientific world. Composers and other creative musicians are often hired to do non-creative work such as teachers, administrators or regular employees of subsidized orchestras. Painters and authors can rarely live from their creative work and have to live from incomes as teachers, postmen and other non-creative jobs. Economies of scale are of great importance in the entertainment world. Making a film normally requires 200–400 man-years and large amounts of studio equipment and other material capital with large fixed costs as a consequence. This has led to a number of organizational responses, such as conflicts about quality and economic rewards among composers and script writers, reliance on performance stars, spatial concentration of production and syndication of the outputs.

5.6

Variable Probabilities and the Importance of Stars

In industrial R&D the probability of success of a particular project has been estimated to be in the range of 7–12%. This means that the majority of projects will be financial fiascoes. To compensate for the losses, most of the industrial research and development costs are borne by large firms in a limited number of manufacturing sectors. These firms are large enough to run a substantial number of parallel R&D projects to compensate for the low success probabilities of most of these projects. The substantial returns of a few of these projects must then compensate for the losses of most of the projects. This is partially true for entertainment firms, such as Disney, Sony or MTG. While most painters and authors are struggling in the first hand market to achieve a reputation a few, often dead colleagues, have become important suppliers in the second hand market of originals and reproductions. Many art and entertainment goods – books, magazines, movies or amusement games – are only sold to final users as copies and the markets for these reproductions are quite different from the markets for originals. Most reproduction

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processes – apart from forgery and other hand-copying – are multi-stage processes with complicated rules of interaction between stages. One example is the music industry (see Table 5.1). There are distinct probabilities of success in the interaction between agents within and between the different stages of such a production and reproduction process and associated problems of negotiating the reward structure. Assuming the probability of success to be the same everywhere and equal to 50%, the probability of success for the whole 4 by 2 process is (1/2)8, which is approximately equal to 0.4%. In this case, the popular music publisher would accordingly need to judge thousands of music proposals from unknown creative music composers to be reasonably sure of a success in the market. Raising the probability of success within and between stages to 90% would lead to a probability of success of the whole four–stage process to 43%. There have consequently been efforts to design individual and institutionalized procedures to increase these probabilities within and between all stages. It is for example often the case that artists compose music and write lyrics themselves. Publishing and recording can be vertically integrated and the owners of record companies can also own television stations, and so on. In film production, these problems are further reinforced by the complexity of production of film negatives (Vogel 1998). Composers and directors often have their contract income based on revenues and therefore they tend to be oriented to the maximization of quality and quantity with potentially detrimental consequences for the profitability of the whole process. Economic efficiency in music and film making would gain from contracts based on profit-sharing for the creators. However, there are several problems associated with profit-sharing that are especially relevant in the complex structures of modern music and film-making. Substantial parts of the fixed costs are unknown to the creators and can easily be redistributed between different products (and their creators). The heterogeneity of arts and entertainment products associated with the dependency of consumer taste on the individual characteristics of a few star performers is especially important in this context. Certain consumers may have a strong preference for individual performers, such as the pianist Glenn Gould, the singer Ella Fitzgerald or the actor Julia Roberts. Such stars do in fact have an almost monopolistic negotiating position at each first recording of a piece of music or a film Table 5.1 The music industry as a multi-stage production process Stage 1 Composition of music Artist’s first performance: innovation (including lyrics): creation Stage 2 Music publisher: production Diffusion to reproducers Stage 3 Recording on CDs and Diffusion to radio and TV stations, DVDs: production and record distributors Stage 4 Purchases by consumers: Collection of royalty incomes by ASCAP, BMI, diffusion SESAC, etc., for distribution between upstream agents

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manuscript. In a way the appearance of such an artist increases all the probabilities of success discussed above and all of the agents have to yield to this fact. The complex production technology of most reproductive art and entertainment goods leads to high fixed costs of production and globally concentrated industries. The film industry is one such globally concentrated industry. Most countries rely on imports of films from the global centers of production and especially from Hollywood. This is a consequence of the complexity of production, which causes high fixed and irreversible costs for each film. These scale economies are further reinforced by the low probability of success of each individual film. The organizational result has been an increase in the size of firms, which makes it possible to diversify production in order to reduce the risk of bankruptcy. Table 5.2 gives the size of film production in a number of countries, measured as the number of film negatives produced from 1991 to 1995. The rank size distribution of film production in different nations is as follow Film production ¼ e7:22 ðRankÞ1:3 ;

R2 ¼ 0:95:

An alternative approximation form of the distribution is Film production ¼ eð5:90:12ðRankÞÞ ;

R2 ¼ 0:95:

These equations imply that the distribution is highly skewed, which is also indicated by the fact that the mean of the number of films produced is more than twice as large as the median production. Vogel (1998) collected financial data for the production of films in the United States from 1976 to 1996. While some of these films were profitable, others suffered disastrous losses. The mean cost of production was US $34 million with a standard deviation of US $23 million, while the mean revenue was US $91 million with very large standard deviation of US $81 million. There was no correlation between revenues and costs. Table 5.2 Production of film negatives in the top ten countries in the period 1991–1995 Rank Country Number of film negatives 1 India 838 2 United States 420 3 Hong Kong 315 4 Japan 251 5 Thailand 194 6 China 154 7 France 141 8 Italy 96 9 Brazil 86 10 United Kingdom 78 Source: UNESCO (1998), World Culture Report

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Using regression analysis, we estimated the effect of top-ranking directors or actors on assessed revenue. The result is as follows: ln ðRevenueÞ ¼ 2:88 þ 1:41T;

ðn ¼ 23Þ;

in which T=1 if a top-ranked director or actor is involved in the making of the film (otherwise T=0). The t-value of the slope parameter estimate is 2.3, indicating that the estimated value is significantly different from zero at the 5% significance level. The regression equation implies that a Hollywood-produced film without a topranking director or actor can be expected to generate US $18 million in revenue, while the revenue figure for a film with a top-ranking director or actor is US $73 million. For production costs, there is no corresponding statistically significant ‘‘celebrity impact’’. This impact gives these artists a strong bargaining position, which should enable them to obtain substantial shares of revenues or profits. The contract variations are almost endless, but it is not unusual that the leading actor, actress, and the director together obtain more than 10% of the total revenue when the total exceeds US $150 million.

5.6.1

Lining up Behind Giants

Most labor markets are similar to markets for standardized goods. The price of the good itself and the prices of substitutes and complements determine the supply. Similarly, different prices determine the demand and the supply and demand simultaneously determine the equilibrium price and quantity. In the labor markets there are deviations from this simple competitive principle. Some occupations require many years of education and training and the movement toward equilibrium is consequently slow. Institutional safety constraints regulate other types of labor, as for instance airline pilots or medical doctors, which therefore constrain the supply. For some occupations, unionization works as a barrier to entry, which prevents the attainment of a competitive equilibrium. These factors to some extent are also relevant for artists. However, more important are the combined effects of the number of gatekeepers that block entry and advancement and the uncertain success of the final, creative product. Market success depends on the impact of the most visible artist who is involved in production. Because of the intangibility of created ideas, when innovated as a piece of music or a new film, expectations are of great importance for the demand on the day of the premiere. Expectations of a rewarding experience derive from the probabilities of success, as the potential audience perceives them. These perceptions in turn depend on the rank of the artist among the group of comparable artists. There is in most artistic and entertainment occupations a continuous inflow of new entrants, owing to the attractiveness of many artistic careers to young people. Most of these new entrants fail when attempting to get on

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the first rung of the career ladder (i.e. through the first ‘‘gate’’), and the probability of failure becomes greater at each further step on the career ladder to stardom. Let us assume that the probability of advancing from one rung of the ladder to the next is 5%. The probability of succeeding to the nth level is then 0.05n. If there are five rungs, the probability of reaching the fifth level is 3 in 10 million. If we instead assume that a person has talent enough to have a probability of 20% to climb each rung of the ladder, the probability will equal 0.32 in 1,000 attempts to reach the top. If we assume that there are one million aspiring young entertainers and there is a probability of 10% (i.e. probability is 0.1) to reach local recognition, there will be 100,000 local successes. If there is an additional 10% probability to reach regional recognition, it means that 10,000 will continue to that level in their career. Let us assume that the probability is 20% that they will reach national recognition, given that they are already regionally recognized, then that would imply that 2,000 will reach that stage of their career. To reach recognition on a continental scale might have a very low probability of, say, 1%, so that only 20 will reach that level of recognition and finally maybe only five will have a substantial global impact. There are many ways to measure the impact of an artist. In science, it is common to use global citations in scientific journals to measure the impact of a scientist on the public (in this case, other scientists). To an artist, recognition by other artists is often pleasing, but of little importance in the markets for artistic products. We therefore need some other, a more general measure of impact. We have chosen to use the number of Google (an internet search engine) hits as such a general measure of the impact of various kinds of artists. Tables 5.3, 5.4 and 5.5 reveal the impact of different creative artists, as measured by Google hits in early 2005. The average year of birth of the top ten composers is 1789. This implies that the average age of the top ten compositions is almost two centuries. This is also reflected in the current programming strategies among concert houses and symphony orchestras. The importance of the English language for global success is clear from these rankings. Six out of the top ten Nobel laureates have English as their mother tongue. No such language effect is discernible for the other art forms (except for films). Table 5.3 Top ten composers of classical music Rank Composer Year of birth 1 J.S. Bach 1685 2 L. van Beethoven 1770 3 W.A. Mozart 1756 4 G. Verdi 1813 5 F. Schubert 1797 6 P. Tchaikovsky 1840 7 J. Brahms 1833 8 D. Shostakovich 1906 9 F. Chopin 1810 10 A. Vivaldi 1678 Sources: Larousse Encyclopedia of Music and Google, January 2005

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90 Table 5.4 Top ten laureates in literature Rank Nobel laureate 1 J.P. Sartre 2 T.S. Eliot 3 B. Russell 4 W.B. Yeats 5 G.B. Shaw 6 T. Mann 7 S. Beckett 8 A. Camus 9 W. Faulkner 10 A. Gide Source: Google, January, 2005

Table 5.5 Top ten jazz musicians Rank Musician Year of birth 1 M. Davis 1926 2 C. Parker 1920 3 L. Armstrong 1900 4 B.B. King 1925 5 B. Webster 1909 6 L. Young 1909 7 King Oliver 1885 8 E. Fitzgerald 1919 9 D. Ellington 1899 10 B. Holiday 1915 Source: Larousse Encyclopedia of Music and Google, January 2005

The average year of birth of the top ten jazz musicians is 1910. All except one have passed away and can only be heard on recordings. One way of analyzing the citation rates of the ranking lists of artists is by using the following equation: Citations ¼ eðabðRankÞÞ : We have used least-squares regression analysis to estimate the parameters a and b. The parameter estimate b refers to the percentage decline in the number of citations of the artists when their ranking is increased by one unit. The estimated equation for the 40 highest-ranked composers is Citations (composersÞ ¼ eð7:50:07ðRankÞÞ ;

R2 ¼ 0:98:

Increasing the number of observations does not influence the equation to any considerable degree. The estimated equations for the other groups of artists are as follows: Citations ðNobel laureatesÞ ¼ eð5:570:10ðRankÞÞ ;

R2 ¼ 0:98;

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R2 ¼ 0:96:

These equations indicate exponential decline of rank-ordered citation rates and are remarkably good at accounting for the variability in the number of citations. A conversion of these citation rates into probabilities of recognition gives a similar rapid decline of recognition as we move down the rankings of the artists. The estimates also show that jazz musicians and Nobel laureates have greater estimated b coefficients in absolute values, which possibly reflect the lower age of their works. The average birth year of the top ten creative artists varies considerably among the different categories, as shown in the above tables. In literature and music there are incredible numbers of ‘‘giants’’ who implicitly compete with new entrants aspiring for positions of global fame. A young painter, poet or composer therefore has to compete for recognition with artists who died a long time ago. This competition with the dead generates incentives for creative artists to develop new styles, niches or even completely new rules of composition. The extreme durability of great art is an advantage to the general public but an obstacle to recognition among all aspiring artists. The skewed distribution of recognition among creative artists leads to a correspondingly skewed distribution of revenues, which inevitably leads to a skewed distribution of artists’ material assets and incomes. By way of example, assume that the price of a painting by the highest-ranked artist is $100 million. If the price distribution corresponds to an estimated citation function, this would imply that an artist at global rank 100 would receive $33,000 per painting, while the painter who is ranked as number 150 in our global ranking would receive only $614 for a painting. The top ten would generate most of the total wealth derived from the sale of paintings in these circumstances, as long as the supplied quantities do not increase dramatically with increasing rank number (i.e. decreasing number of citations). Our example conforms in its general pattern to the markets for paintings and compositions in classical music, but it does not conform to the markets for films and popular music, where the rankings change rapidly. However, even in these more changeable markets a similar pattern persists at each short period of time. During their much shorter stable ranking periods, the rent and income distribution should be expected to be extremely skewed in favour of only the top-ranking segment or sometimes even just one giant.

5.7

Syndication

A special form of vertical and horizontal integration – syndication – is typical of arts and entertainment industries (Werbach 2000). The basic preconditions for economic advantage of syndication are the following: 1. The product must have the property of a public good, i.e. it should be possible to be used by many at the same time or consecutively, i.e. the same unit of

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2. 3. 4.

5.

6.

a product can generate utility to many users. This is typical of information and knowledge. A concert by the Vienna Philharmonics on n radio and television stations does not decrease the quantity or quality to the listeners of the concert, even if n goes towards infinity. However, aggregate utility and thus aggregate willingness to pay increases with an increasing n and thus the potential revenue is an increasing function of the number of radio and TV stations allowed to relay the concert. The product must be based on information only so that Internet can be used for transmission of the product. The product must be modular, i.e. capable of being cut into pieces – modules – and reassembled together with other modules. The product must be easily adaptable to different consumer groups. For example, the puns and jokes of an entertainment program should be capable of translation. Language free jokes as in the old Chaplin or Mr Bean movies are ideal from this point of view. Transaction costs (other than transport costs) should be limited to allow for syndication. A radio or TV program that only contains music could easily be syndicated, even globally, as there are small language and culture barriers to be overcome in the transfer of the program from country of origin to a country of destination. Syndicating a movie is more costly. It might require dubbing and cutting to suit a specific public. Sometimes a syndicated TV program needs to become a part of some coherent programming strategy, which gives rise to to adaptation costs. Distributors must be independent of each other. If distributors can organize themselves in some cartel or resale network, advantages of syndication would drop. Either the number of paying distributors would drop or the revenue from each distributor would be constrained to be below the resale price within the cartel or resale network. With Internet distribution these resale prices could approach zero if there are inefficient copyright rules and regulations. Essentially syndication contains the following agents: Agents

Example

Creator

Author

Producer

Scriptwriter and innovation team

Syndicator

TV program syndicator

Distributors

TV stations

Consumers

TV audiences

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Integration by Syndication

Examples of syndicated entertainment products are Robinson, Jeopardy, the Oprah Winfrey and Jerry Springer shows, sports arrangements like Olympic Games and other global championships. Examples of syndicated arts products are films by independent filmmakers (e.g. Wim Wenders or Ingmar Bergman), classical music programs on radio and TV, novels suitable for conversion into film and photographic art. With the development of the size and quality of internet, syndication advantages will determine production–distribution system for entertainment and arts.

5.7.2

Global Creative Networks or Big Is Interactive

With the growing efficiency of communication of new ideas, there is an obvious increase in the economic advantages of interaction among creators of arts, entertainment and science. Assuming the value of a creation to a creator living in region, i.e. to be dependent on the interact ion with other creators, living in regions j (=1,. . .,n), we have the following optimal interaction problem: max vðiÞ ¼ SpðIði; jÞÞQðiÞ  Scðdði; jÞÞIði; jÞ; where v(i) is the profits (or recognition) accruing to the creator of region i, p(I(i, j) is price (unit value) of interaction with creators of region j, Q(i) is the predetermined level of creative activity in region i, and c(d(i, j) is unit cost of interacting from region i with region j. The p-functions are assumed to be concave and differentiable everywhere (at least twice), while the unit cost of interaction is a given to be constant for any pair of regions. The conditions of optimal interactions are thus: dp/dI(i, j) ¼ c(d(i, j)/Q(i); for all interacting pairs of regions. The implications are the following: l l

l

Interactions would increase with increasing impact of synergies upon creativity. Interactions would increase with decreases in the transactions, transport and communication costs. Interactions would be larger for creative activities operating at a large scale.

In an earlier paper by Andersson and Persson (1993) it was shown that under an assumption of a Cobb–Douglas production function, the interactions would follow a gravity equation.

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5.8

Conclusions

There has been a slow and steady transformation of the advanced market economies from a focus on productivity towards a focus on creativity and innovation. This refocusing means a greater importance of economic organization based on synergy and interactions than on division of labor and occupational specialization. A creative focus implies a change in the working of the labor market. Because of the great uncertainties in creative multi-stage production systems, there are great advantages of employing internationally renowned creators. These can often demand substantial ‘‘celebrity rents’’, leading to highly skewed income and wealth distributions. The large uncertainties also cause an increase in the optimal scale of production. This is further reinforced by the increasing possibilities of syndication of the created products. Syndication essentially means that the same idea can be sold to many users in separated markets after adaptation to the specific user preferences. This has been used since long in the news media and among consultants, who have developed production processes, repackaging and users adapting the creative ideas of scientists. Syndication advantages have increased by orders of magnitude with the increasing efficiency of Internet. The advantages of creative synergy will increase the tendency to interact globally among scientists and artists. Optimal global interaction conditions are deduced. They indicate that interactions should be driven to the point where the unit cost of interaction divided by the scale of operations equals the marginal increase in the value of the created idea (eventually innovated as a product).

References ˚ E, Persson Olle (1993) Networking scientists. Ann Reg Sci 27:11–21 Andersson A Bell Daniel (1973) Coming of post-industrial society: a venture in social forecasting. Harvard University Press, Cambridge, MA Boden M (1990) The creative mind. Weidenfeld/Abacus & Basic Books, London Caves RE (2000) Creative industries contracts between art and commerce. Harvard University Press, Boston Fermi L (1954/1994) Atoms in the family: my life with Enrico Fermi. University of Chicago Press, Chicago Hardy GH, Littlewood JE, Po´lya G (1934) Inequalities. Cambridge University Press, Cambridge Polya G (1945) How to solve it: a new aspect of mathematical method. Princeton University Press, Princeton Romer PM (1986) Increasing returns and long-run growth. J Polit Econ 94(5):1002–1037 Shell K (1966) Towards a theory of inventive activity and capital accumulation. Am Econ Rev 56 (2):62–58 Simonton DK (1984) Genius, creativity and leadership – historiometric inquiries. Harvard University Press, Cambridge, MA Smith Adam (1904) An inquiry into the nature and causes of the wealth of nations, 5th edn. Methuen and Co. Ltd, London (edited by Edwin Cannan)

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Smith GJW (1990) The Creative Process: A functional model based on empirical studies from early childhood up to middle age. International Universities Press, Madision, Connecticut Stewart IN, Peregoy PL (1983) Catastrophe theory modeling in psychology. Psychol Bull 94 (2):336–362 Uzawa H (1965) Optimum technical change in an aggregative model of economic growth. Int Econ Rev 6(1):18–31 Vogel HL (1998) Entertainment industry economics: a guide for financial analysis. Cambridge University Press, Cambridge Werbach K (2000) Syndication: a new model for business relationships in the Internet Era. Harv Bus Rev 78(3):84–93

Chapter 6

Simple Memes and Complex Cultural Dynamics David Batten and Roger Bradbury

Abstract Regions and their policies are built on many things, such as ideas, actions, habits, skills, inventions, songs and stories, to name a few. This paper views all of these as selfish Darwinian entities – memes – that, like genes, interact and replicate in complex ways with humans to shape our culture. Perniciously, simple memes can exploit our limited capacity to deal collectively with complex problems. Whether good or bad, a single, omnipotent meme can dominate a local region of meme-space. Most arguments in this paper originated at a workshop on ‘‘Memes as Complex Systems’’ held in Canberra from 13–17 August, 2004 and funded by CSIRO’s Centre for Complex Systems Science (see Batten et al. 2007).

6.1

Introduction

Public policy sets the framework for the conduct of human affairs. Whether grandly enshrined in law and treaty, or more humbly promulgated as municipal regulation, the intent is always the same – the civilising of interactions between and among individuals, communities, regions and nations. Irrespective of whether policy concerns basic human needs like food, shelter and sex, or the arcana of intellectual property rights arising from new ideas, actions and artefacts, it always deals with the nature of Homo sapiens – that peculiar animal imbued with culture. Can we develop a science of good public policy? Such a question has been at the centre of the public policy literature for at least three centuries. However, this literature has been heavily influenced by certain notions from economic theory, like restoring the Pareto-efficiency of the competitive mechanism or achieving Paretooptimality through planning. Static notions are clearly irrelevant when a society is

D. Batten (*) The Temaplan Group and CSIRO, Melbourne, Australia e-mail: [email protected]

C. Karlsson et al. (eds.), New Directions in Regional Economic Development, Advances in Spatial Science, DOI: 10.1007/978-3-642-01017-0_6, # Springer‐Verlag Berlin Heidelberg 2009

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far-from-equilibrium. Furthermore, the public policy literature pays scant attention to two other crucial factors: l

l

The public policy ‘‘arena’’ has a very slowly-changing dimension, founded upon the co-evolution of tangible and intangible cultural capital (Batten 1993). This slowly-changing arena, on which the faster games of Homo sapiens are played, places constraints on society’s ability to achieve ‘‘good’’ public policy.

In order to understand what makes good policy, and what makes some policies fail while others succeed, this paper suggests that we must embed it in a theory of human culture that is consistent with evolution. Dennett (2000) calls this ‘‘minimal Darwinism’’. Because culture is the playground of other evolving entities besides humans, any proper understanding of policy must be built along Darwinian lines. We need, in fact, to understand how policy depends on the complex interplay between humans, genes and memes. The contention in this paper is that a science of co-evolving ideas, habits, actions and artefacts – in fact, all the elements of human culture – could be built on Dawkins’ notion of the meme (Dawkins 1989) using the analytical tools of complex systems (Anderson et al. 1988; Epstein and Axtell 1996; Holland 1998). Genes appear to be selfish (Dawkins 1989). That is, the interests of the genes and the interests of the organisms in which they live may not always coincide. Although genes are not conscious, purposeful agents, blind natural selection makes them behave as if purposeful. This Darwinian idea of purpose or self-interest is only a metaphor, of course, ensuring that genes will grow and spread blindly through the world. Genes that encourage parents to take (sometimes fatal) risks to protect their young, for example, serve the interest of the genes, not the organism, by increasing the chance that copies of the genes survive (in the offspring) even as they decrease the chance that the parent survives. Genes treat organisms as vehicles to protect them from the vagaries of the environment and increase their chances of survival. Dawkins extended this notion of vehicle to things that organisms create in their environment (Dawkins 1982), whether beaver dams or human culture. He coined the term memes to describe how human ideas, objects and artefacts can be thought of as agents evolving separately from their human hosts, owing their existence as entities to contingent facts about brains and their interactions. Their dynamics are governed by the principles of Universal Darwinism (Dennett 1995). This is the idea that if the conditions for Darwinian evolution are met then it will, indeed, must occur. The conditions are that the entities should exhibit heredity or replication (be copied from one generation to the next), variation (an abundance of different elements, because copying is not perfect), and natural selection (some of the exhibited variation is associated with conditions of existence). Genes, based on DNA and RNA, fulfil these conditions, and so life has evolved. But other entities in the world – prions (Aguzzi and Haass 2003), the computer programs of artificial life (Langton 1995), and memes (Blackmore 1999) – also fulfil the conditions of Universal Darwinism. Thus they must evolve and they must act as if they are selfish. They will differ from each other and from genes in how they evolve, but that they evolve and have interests that appear to be selfish can hardly be in doubt.

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The crucial idea about memes – what makes them uniquely Darwinian – is that if a meme can get itself copied it will. The new Oxford English Dictionary defines meme (n. Biol.): ‘‘An element of a culture that may be considered to be passed on by non-genetic means, esp. imitation’’. When we imitate someone else, something gets passed on. Copying and imitation come naturally to human beings. In fact, what makes humans different from other animals is our ability to imitate (Blackmore 1999). That which gets passed on can be passed on again, and again, and thus takes on a life of its own. And that is what makes the meme a replicator and gives it replicative power. Education and imitation are ways of transmitting ideas, habits and behaviours from one vehicle to another. It has been said that a chicken is just an egg’s way of making another egg. As Dennett suggests, perhaps a scholar is just a library’s way of making another library (Dennett 1995). A meme’s existence depends on physical embodiment in one medium or another. Like genes, memes are potentially immortal. But, also like genes, they depend on a continuous chain of physical vehicles for their existence. Books, buildings and music are relatively permanent, as are inscriptions on monuments. But unless all of these are under the protection of human conservators, they tend to dissolve over time. Manfred Eigen makes a similar point: Consider, for instance, one of Mozart’s compositions, one that is retained stably in our concert repertoire. The reason for its retention is not that the notes of this work are printed in a particular durable ink. The persistence with which a Mozart symphony reappears in our concert programmes is solely a consequence of its high selection value. In order for this to retain its effect, the work must be played again and again, the public must take note of it, and it must be continually re-evaluated in competition with other compositions. (Eigen 1992)

6.2

Cui Bono?

Lawyers often ask (in Latin), Cui Bono? Who benefits from this matter? – A question that strikes at the heart of important policy issues. In the case of evolutionary theory, by and large the fate of a body and the fate of its genes are closely linked. But when push comes to shove, the evidence suggests that what’s good for the genes determines what the future will hold. A ‘‘gene’s-eye-point-of-view’’ explains things in terms of the complex interactions between long-range, largescale ecological facts, long-term historical facts, and local, molecular-level facts (Dawkins 1982). Many people feel threatened by this gene’s-eye-point-of-view. They want to be in charge of their own destinies – or at least feel that they are. After all, none of us want our interests playing second fiddle to something else! But soon the threat may come more from non-genetic sources. Memes are outpacing biological change. Cultural evolution operates many orders of magnitude faster than genetic evolution, so it may not be long before what’s good for the memes may determine what the future will hold, partly at the expense of our genes. What better example is there of this phenomenon than the Internet – a meme itself and a lightning-fast transmitter of memes.

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If memes, as well as genes, build humans and their culture to further their own interests, then even bigger questions loom. Where is human purpose or free will in this description (Hull 2000; Dennett 2003; Blackmore 2003). Modern genetics have undermined the belief that humans are born with the freedom to shape their individual destinies. Scientists recognize that genes shape our minds as well as our bodies. Evolutionary psychologists place personal qualities – like altruism and aggression – squarely in the Darwinian camp of random mutation and natural selection (Dennett 2003). If memes have a hand in shaping our minds as well, then who is really in charge – ourselves or our memes? Can humans possibly survive as the ruling vehicles in the face of such a complex mix of memetic influences operating at vastly different speeds? There are no simple answers to these questions. You may be appalled by the idea of your brain being: a sort of dung heap in which the larvae of other people’s ideas renew themselves, before sending out copies of themselves in an informational diaspora. (Dennett 1995)

It does seem to rob your mind of its importance as both author and critic. Most of us would like to think of ourselves as godlike creators of ideas, manipulating and controlling them as our whim dictates. But, even with the most masterful and creative minds, this is seldom, if ever, the reality. As Mozart observed of his own ‘‘brainchildren’’: When I feel well and in a good humour, or when I am taking a drive or walking after a good meal, or in the night when I cannot sleep, thoughts crowd into my mind as easily as you would wish. Whence and how do they come? I do not know and I have nothing to do with it. Those which please me I keep in my head and hum them; at least others have told me that I do so. (Dennett 1995)

6.3

Genes, Memes and Replicators

One challenge for understanding genes and memes is how to transfer some of the deep knowledge and understanding of genetics to the domain of memes without imposing the particulars of genetics that are the result of the way living things are built. In fact, the field of memetics has probably been held back by attempts to map memetic to genetic phenomena too precisely (Hull 2000). Perhaps a more useful framework for memetics may be found in complex systems science. It provides a rich array of theory and practice for gaining insight into the emergent properties of systems whose dynamics range from adaptive to evolutionary in the strict Darwinian sense (Holland 1998). Embryonic and cultural development can be looked upon partly as the evolution of cooperation (Axelrod 1984). Axelrod’s computer tournaments among different strategies in ceaseless games of the Prisoner’s Dilemma serve as useful metaphors for the way we can think of animals, plants, and even genes (Dawkins 1989). It is natural to ask whether his optimistic conclusions – about the emergent success of nice, forgiving, non-envious strategies like Tit for Tat – also apply in the world of

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nature. Dawkins’ answer is yes, of course. So long as the shadow of the future is long, and games are the non-zero sum variety, embryonic development can be viewed as a cooperative venture – jointly run by thousands of genes together. In natural selection, genes are selected for their capacity to flourish in the environment in which they find themselves. We usually think of this environment as the outside world, that world of predators and climate. But, from the gene’s-eye-point-of-view, possibly the most important part of its environment is all the other genes that it encounters. A gene encounters other genes mostly in the cells of the successive individual bodies in which it finds itself. Each gene is selected for its capacity to cooperate successfully with the population of other genes that it is likely to meet in other bodies. Could the same be true of memes? From the meme’s-eye-point-of-view, possibly the most important part of its environment is all the other memes that it encounters. Memes encounter one another mostly in the brains of the successive individual bodies in which they find themselves. Could it be that each meme is selected for its capacity to interact successfully with the population of other memes that it is likely to meet? Then, doing well in such environments would correspond to collaborating with these other memes. Another interesting parallel between genes and memes is their informational nature. As an evolutionary unit, a long-lived gene is not any particular physical structure but the textual, archival information that is copied on down the generations. This textual replicator is widely distributed in space among individuals, and widely distributed in time over many generations. The population of genes is not just the temporary collection that happens to come together in the cells of any particular body, but the set of all genes in the population of inter-breeding individuals – the gene-pool. Just as genes propagate themselves in the gene-pool by passing archival information on from body to body via sperm or eggs, memes propagate themselves in the meme-pool by passing information on from brain to brain via an imitative process. The above gives rise to a technical question: how do memes and genes interact to create the vehicles that allow them to replicate? Perhaps it is through the emergence of autonomous structures by modularisation and hierarchical organization. Perhaps self-organization may be a key architect. These are well-described complexity phenomena in other problem domains. We shall steer around this Scylla and Charybdis for the moment, charting a more pragmatic course by examining some particulars of how public policy as a purposeful domain of ideas may be driven by memes.

6.4

Vignette Number One

Consider, first, international development aid. It is an area where public policy has failed spectacularly, and in a way that continues to confound conventional explanations. Despite transfers of vast amounts of money over many decades from the rich world to the poor, poor countries are getting poorer while the rich get richer.

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Memetics allows us to propose a testable scientific explanation for this continuing policy failure. Our model suggests that aid is an expression of a biologically-based altruistic drive modulated in Western culture by two sets of memes entrenched at least since the Enlightenment: ideas of universal human values and ideas of progress and technological control. Together these encourage simplistic intervention in the complex system that is some other (non-Western) culture – where such memes are not strongly established. But these memes never need to become established in poor countries for them to continue to prosper in rich ones. All that is needed is that memes emanating from the poor countries reinforce those in the rich. Simple memes carried in pictures of starving children – regardless of their truthvalue – created in poor countries replicate well in rich countries. Simplicity serves the interests of this new cluster of memes, and encourages continued simplistic intervention, regardless of the actual effects – good or bad – of the aid. We may predict confidently that development aid will continue to be a naı¨ve and failing intervention in a complex system, and that it will continue to fail for so long as the interests of these simplistic clusters – meme complexes – are satisfied.

6.5

Vignette Number Two

The War on Terror is another case where public policy is in trouble. Historically tested policy settings failed to either anticipate or cope with this new international security emergency. The intelligence and security services of key members of ‘‘the coalition of the willing’’ (USA, UK and Australia) have each been reviewed to discover why the West was caught napping. The usual bureaucratic reasons – lack of cooperation and coordination – and the usual policy nostrums – creation of new organisational structures – have been aired. But a memetic perspective offers a different insight, as well as new directions. We suggest that fundamentalist Islamic terrorism is an emergent property of the complex adaptive system that is our strongly-interacting cultural world. It was created from the historical interplay of humans and memes. It was never entirely predictable that it would come into being, for the emergent properties of complex adaptive systems are not generally predictable, even in principle (Anderson et al. 1988). And in that sense, the intelligence services did not fail, in failing to predict it. But now that it exists, it can be understood, and that understanding can guide policy. And the key lies in understanding the balance of interests among the memes in play in the brains of terrorists. Clearly, terrorism memes have found a very successful strategy for their replication and spread, especially by associating with powerful and long-lived religious memes and by using new channels such as television and the internet to spread from brain to brain. We predict that no policy based only on stopping acts of terrorism or locking up or killing terrorists can be successful, since the terrorism memes are not affected by this. However a policy to change the selective pressure on terrorism

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memes – perhaps on their linkage with religious memes – could drastically reduce the spread of these memes. The global outbreak of terrorism could collapse or, more likely, evolve to something like local gangsterism (as in the Irish situation) that can be handled by normal policing. And this could occur despite the absence of any other social or political reforms, such as democratisation or market reforms.

6.6

Vignette Number Three

Our third example concerns recreational drug use. Many such drugs are intensely pleasurable to human beings. Some of these drugs are harmful to health. But drug policy often creates social and economic problems that are, arguably, out of all proportion to that harm. These include the huge black market, the cost of policing, the size of prison populations, the criminalisation of children, and the financing of terrorism and organised crime. Existing models of the drug user – sick person, social victim, and sinner – fail to adequately describe the situation or produce better policy. We suggest that drug policy is stymied because simple memes – such as ‘‘drugs are bad’’ – are often more successful than complex ones, and successful memes attract other memes – ideas of criminality, morality, sociality and so on – to form even more powerful complexes. The interests of this meme complex – or memeplex (Aguzzi and Haass 2003) – are served by the actions of all human players in our society – licit and illicit, users and non-users, victims and victors – and it has become so powerful that it excludes all other competing memes. But complexity theory allows us to imagine a richer universe of policy possibilities built on biological predispositions and pharmacological effects, and to see the current policy as a deep basin of attraction in a policy landscape. We know, for example, that Amazonian Indians using the drug ayahuasca have a core meme: drug use is dangerous but can lead to spiritual experiences. This allows the memes associated with ritual, social control, art and creativity to form memeplexes not found in other societies. Exploration of the policy landscape in the vicinity of such memes could provide the opportunity for the evolution of memes that are both successful as memes and beneficial to their human hosts.

6.7

Vignette Number Four

Our final example focuses on American economic policy, where fiscal irresponsibility seems to be politically seductive. Once again, the world’s largest debtor is touting huge tax cuts as stimulants to economic growth and massive increases to defence spending (Davies 2004). In the early 1980s, the Reagan administration did the same. Its reasoning was simple: middle-class Americans are overregulated and overtaxed, groaning under the weight of Big Government. Reagan illustrated his

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point with striking images such as welfare queens driving Cadillacs and huge rooms full of bureaucrats each taking care of a single Indian (Rivlin 2004). Yet all these images were fantasies and Reagan’s stance highly questionable. Middle-class Americans pay lower taxes than residents of other advanced nations and most of those taxes go to pay for their own social programs. Why did Reagan build his political stance on a compelling mythology so far from reality? Was he simply too frightened to do the necessary? Raising taxes and cutting spending are extremely painful. A political leader needs to be convinced that the pain is worth it. A memetic perspective, however, offers a different explanation. First, we must ask where ideas about economics come from? Most come from economists. But not all economists are alike. Krugman (1994) notes that the genus includes two radically different species: professors and policy entrepreneurs. A professor writes for other professors. Lurking behind his words – no matter how simple – are concepts too complicated for a broad audience to understand. On the other hand, a policy entrepreneur writes and speaks in simple terms, largely for that broader audience. In the late 1970s, a powerful group of policy entrepreneurs – the ‘‘supply-siders’’ – came upon the political scene. Mostly journalists and political staffers, they shunned demand-side policies and proclaimed that sharp tax cuts will produce a huge surge in economic growth. Reagan loved this meme and based his campaign on these supply-side cranks. Supply-side memes flourished and spread. ‘‘Voodoo economics’’ ruled the American roost for the next twelve years, despite consistently failing to live up to its various promises. Today, other misguided policy entrepreneurs prevail. Some understand even less about the economy than supply-siders. Is there a memetic version of Gresham’s Law at work, in which bad ideas tend to drive out good ones? Whether they are good or bad, simple memes propagate more effectively than complicated ones. They can be copied faithfully by politicians and populations alike. One attractively packaged meme can become omnipotent, dominating any local region of political meme-space.

6.8

Concluding Remarks

Our four vignettes have several features in common. First, each shows that memes can exploit our limited capacity to deal collectively with complex problems. They undermine our efforts to grapple with the complexity of the situation. Simple memes propagate better for the mechanical reason that something simple can be copied with greater fidelity than something complicated. And our own complexitybased work suggests that there is room for only one powerful meme in any local region of meme-space – and such memes will usually be simple. Thus Universal Darwinism presents a special challenge to public policy, since policies are built from ideas in action. We need to imagine a more complex and organic process where humans and their cultures have limited agency. We need to

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understand how policy is constructed by and for often short-lived, relatively simple memes, each with their own selfish interests, within a complex framework of culture built by relatively longer-lived genes and memes, again each with their own selfish interests. In short, we need to bring memes and complex systems into the arena of public policy – whether regional, national or international. Then, perhaps, our limited abilities to address complex problems collectively might improve.

References Aguzzi A, Haass C (2003) Science 302:814–818 Anderson PW, Arrow KJ, Pines D (eds) (1988) The economy as an evolving complex system. Addison Wesley, New York Axelrod R (1984) The evolution of cooperation. Basic Books, New York Batten D (1993) Pap Reg Sci 72:103–112 Blackmore S (1999) The meme machine. Oxford University Press, Oxford Blackmore S (2003) Consciousness: an introduction. Hodder and Stoughton, London Davies P (2004) Foreign Policy 144:36–38 Dawkins R (1982) The extended phenotype: the gene as the unit of selection. Freeman, Oxford Dawkins R (1989) The selfish gene. Oxford University Press, Oxford Revised edition with additional material Dennett DC (1995) Darwin’s dangerous idea: evolution and the meanings of life. Simon and Schuster, New York Dennett DC (2000) In: Aunger R (ed) Darwinizing culture: the status of memetics as a science. Oxford University Press, Oxford, pp vii–ix Dennett DC (2003) Freedom evolves. Viking Books, New York Eigen M (1992) Steps towards life. Oxford University Press, Oxford Epstein JM, Axtell RL (1996) Growing artificial societies: social science from the bottom up. Brookings Institution, Washington Holland JH (1998) Emergence: from chaos to order. Oxford University Press, Oxford Hull DL (2000) In: Aunger R (ed) Darwinizing culture: the status of memetics as a science. Oxford University Press, Oxford, pp 43–67 Krugman P (1994) Peddling prosperity: economic sense and nonsense in the age of diminished expectations. Norton, New York Langton G (1995) Artificial life: an overview. MIT, Cambridge, MA Rivlin M (2004) Foreign Policy 144:45–46

Chapter 7

The Fashioning of Dynamic Competitive Advantage of Entrepreneurial Cities: Role of Social and Political Entrepreneurship Lata Chatterjee and T. R. Lakshmanan

7.1

Introduction and Overview

There has been a major change, over the last three decades, in the functions, policy mechanisms, and the spatial forms of many urban regions in the highly industrialized countries in North America and Europe. These transformations reflect these cities’ roles as key actors and sites of change in the contemporaneous process of globalization, and the constituent economic, social and spatial restructuring. The term ‘‘Entrepreneurial City’’ pertains to this emerging urban entity. Lakshmanan and Chatterjee (2003, 2004, 2006; Chatterjee and Lakshmanan 2005a, b) have argued that a variety of change processes have converged in recent years to create a new global environment in which three types of change agents have collaborated to effectuate a major economic and spatial evolution in the form of a global production system and the rise of the entrepreneurial city (Fig. 7.1). Such change processes comprise of three types: (a) multiplicity of knowledge-rich material (transportation, communications and production) technologies and infrastructures which have made economically feasible production systems spanning the globe; (b) the advent of neoliberal ideologies which have spawned many nonmaterial (institutional and organizational) technologies and infrastructure which have dropped institutional barriers to and promoted freer cross-border flows of goods, services, finance and knowledge; and (c) secular economic changes such as the rise of quality competition and demand for variety, and the weakening of earlier macroeconomic management apparatus (e.g., Keynesian). These change processes collectively facilitate a global ‘‘space of flows’’ of goods, services, capital, knowledge and technology, and enable a globally distributed production system. In effect, these three classes of change forces create a new context or stage or arena for action by the economic, political, and social actors of the emerging global system. L. Chatterjee (*) Boston University e-mail: [email protected]

C. Karlsson et al. (eds.), New Directions in Regional Economic Development, Advances in Spatial Science, DOI: 10.1007/978-3-642-01017-0_7, # Springer‐Verlag Berlin Heidelberg 2009

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Change Agents

Material Technologies (Knowledge-rich Transport Communications & Production Technologies)

Economic and Spatial Evolution

A. Global Network Corporations, Dynamic Small and Medium size (SME) Enterprises Non-Material Technologies & Infrastructures(Neo-liberal Ideologies, Open Trade Regimes, Logistical and Financial Innovations, Entrepreneurship as a pervasive model)

Weakening of the Earlier “Economic Regime” (Rise of customized production and quality competition & demand for variety; the weakening of the National Keynesian apparatus)

Outcomes

B. Public Sector Entrepreneurial Agents C. Social Sector Entrepreneurial Agents

A. Global Transformation Global organization of production systems (economic volatility) B. Rise of Dynamic “Learning Regions”

Rise of the Entrepreneurial City (Emphasis on Wealth Creation) A. The production of Urban Dynamic Competitiveness B. Innovations in Governance in Policies in Institutions C. De-emphasis of Redistributive Functions

Fig. 7.1 Convergent forces leading to the rise of the entrepreneurial city

In this new global context or arena, the relevant socioeconomic actors come from three interdependent and complementary sectors – market, government and social sectors – and have become major agents of change, shaping the structure, geography and composition of the world economy and its component urban regions. In the market sector, the global network corporations utilize their economies of scale in knowledge, and the economies of scope of their corporate (finance, marketing, etc.) networks, and take advantage of spatial differences in the costs of labor and of other factors by creating and maintaining production units around the world; and small and medium enterprises (SMEs) create and commercialize new knowledge in dynamic urban regions. The second class of change agents, in the public sector, embraces national, regional, and urban levels of governance of the global economy and the constituent urban regions. These agents’ roles, as elaborated later, vary with the level. At the national level, these actors: l

l l

1

Have a market enabling role – enabling efficient factor, asset, and product markets and upholding legal and commercial institutions underpinning these markets. Promote national interests at international negotiations (WTO, IMF, etc.). Have a responsibility to minimize the social disruptions of structural change. At the regional and urban levels, the public actors design and implement economic policies in support of the economic goals of the regional and urban constituents they represent.1

At the supranational level, a variety of entities have arisen to deal with some types of market or extra-market failures in cross-border activities of global network corporations. These are of several types: formally constituted supra-national bodies like the European Union, resource providers (World Bank. IMF), rule or standard setters (World Trade Organization), and a focal point for information assembly, research, exchange of views, etc.

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The third class of change agent is the actor in the social sector, comprising of nonprofit organizations and nongovernmental organizations, and has been a part of governance of democratic capitalist societies for over a century. These social sector organizations (engaging in economic, social and environmental issues) have several functions: policy activism identifying unmet goals and demanding new policies, supplementing and facilitating markets for targeted services, promotion of increased transparency in governance, and engagement in socioeconomic coordination jointly with the public and the private sector agents. The joint outcome of the entrepreneurial actions over the last quarter century of these economic, political, and social agents has been a major worldwide economic and spatial restructuring – marked by a globally organized production system (and value chain), competition between local clusters and dynamic urban regions and the rise of the Entrepreneurial City, engaged in intense interurban global competition (Fig. 7.1). As globalization creates a new geography of competitive advantage and restructured webs of power, an urban region’s successful participation in this global division of labor depends upon its ability to promote economic and extra-economic capacity in that region, that supports sustainable endogenous urban development. Such capacity derives from the acquisition and maintenance by the city of dynamic competitive advantage or structural competitiveness. As elaborated below, such cities which socially create dynamic competitive advantage develop configurations of institutions and practices, which provide a favorable environment for firms or networks of firms to compete entrepreneurially in the global economy (Lundwall and Johnson 1994; Jessop 1997; Lakshmanan and Chatterjee 2003). These cities introduce new economic, social, and political innovations to enhance productivity and other attributes governing the dynamic competitive advantage of local and mobile capital. The appellation ‘‘entrepreneurial’’ is appropriate for this type of city, which exhibits the various traits associated with the entrepreneurship – discovery, risk taking, and many types of innovations – artifacts, processes, organizations, etc. (Hebert and Link 1982; Kirzner 1973; Knight 1921; Schumpeter 1928, 1939, 1961). The objective of this paper is to describe briefly the process by which entrepreneurial cities fashion or socially create their dynamic competitive advantage, which underlies their ability to function and thrive in the new global economy. We argue that three autonomous and interdependent urban sectors – economic, political and social – are involved in the joint production and maintenance of urban dynamic competitive advantage. The common attributes among entrepreneurs in all three sectors are their ability to anticipate change, assess social needs based on perceptions and identify innovative solutions to urban opportunities and problems, undertake risks and take proactive actions to intuited opportunities. Entrepreneurial actors from the three sectors have, however, different motivations, attributes, activities, resources, instruments, and networks, but they coordinate their actions to produce a mutually beneficial joint outcome (Chatterjee and Lakshmanan 2005a, b). To address the complex problems of urban adjustment and reinvention in a volatile global environment, entrepreneurial actors from these three sectors are jointly fashioning new economic and political capacities which lead to the city’s structural competitiveness and support of its wealth creation role. These capacities

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include: new economic roles and functions; an economic ambience comprising of an enterprise culture, permanent innovation, and labor market flexibility aided by human capital investments; a mix of strategic vision and performance-oriented activities; and institutional innovations. At the same time, there is a shift from the mode of (hierarchical) government to governance in these interrelationships between the urban public, social, and public sector actors. Governance pertains to any mode of coordination of interdependent activities. In the urban case this leads to an interorganizational coordination of mat‘erially interdependent but formally autonomous organizations, each of which controls important resources in order to secure a joint beneficial outcome. In short, urban Economic/Political/Social entrepreneurs are fashioning a multisectoral model of urban governance with self-organization of intersectoral coordination and exploitation of complementarities among the three sectors to create a competitive urban milieux. In view of space limitations, this paper focuses on the motivations, activities and networks of just two – economic and social – urban entrepreneurial agents as they collaborate and fashion dynamic urban competitiveness. This paper is part of a trilogy. In two earlier papers we focused on the complementarities between public and economic entrepreneurs (Lakshmanan and Chatterjee 2004) and social and economic entrepreneurs (Chatterjee and Lakshmanan 2005a) in entrepreneurial cities. The central argument in this, and the related papers, is that entrepreneurs in each of the three sectors – private, public and civil – play complementary roles in urban development and redevelopment. They play complementary roles because of a set of universal attributes common to them and a set of singular attributes that reflect their varying institutional contexts. Section 7.2 of the paper surveys the motivations, attributes, and activities of urban social entrepreneurs and how they deploy their networks and other resources to contribute to and collaborate on urban development and regeneration. Section 7.3 describes the evolution of urban public sector actors from government to governance and the development of new modalities of economic coordination between economic and social entrepreneurial agents. Section 7.4 analyses the complementarities and innovations in economic coordination between urban economic and social entrepreneurs, with illustrations from the experience of Chicago, Boston, New York, Tulsa and Santa Fe. Section 7.5 concludes the paper.

7.2

Social Entrepreneurs in Urban Development and Regeneration

All economic, political and social activity occurs in a place; space and time are critical elements in the entrepreneurial decision process. Urban entrepreneurs take three interrelated but distinct decisions – what to produce, where to produce and when to produce. The difference between success and failure in the innovative efforts of entrepreneurs of what to produce depends on their correct assessment of the locational and timing decisions. History is replete with examples of entrepreneurs who failed as

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they were ahead of their time. Failure also results from the choice of inappropriate locations and successful entrepreneurs have good intuition of where to apply their innovations. The literature on locational issues in entrepreneurial decision making has focused on the behavior of market actors, and on the impacts of such activity on the regional economy (e.g., Stohr 1989; Malecki 1994; Acs et al. 2002). In this paper we extend the discussion of urban entrepreneurship to the roles of nonmarket – civil society and public sector – entrepreneurs and the manner in which all three types of actors influence each other in their entrepreneurial decisions in urban space. Urban social entrepreneurs (SEs) respond to existing urban conditions and use innovative strategies to change social realities, and the urban environmental context broadly defined, for the collective good. First, social entrepreneurs take a strategic view – visualizing and judging the potential of urban localities and aiming to bring about urban transformation. SEs alter perceptions that members of civil society, the public sector and the business community have of development potentials of urban localities. Second, they focus on improved service delivery in specific localities and of the role that improved service delivery plays in promoting development potentials of those localities. Thus, SEs recognize an urban social need and relevant innovative solutions, promote and market their ideas changing the perceptions and attitudes among public, private and social sector actors, and marshal personal and community resources through institutional innovation, risk taking, and performance-oriented implementation. Social entrepreneurs have generated, for well over a century, a variety of innovative solutions (that have improved the life chances and mobility of urban residents and fostered social change in urban areas) and have been primarily viewed as humanitarians. Such late nineteenth and early twentieth century social innovators as Jane Addams (Settlement House, Housing), Horace Mann (promotion of public education), Chamberlin (first public provision urban water, sewer, and power provision in Birmingham, UK), Olmstead (urban parks and environment), and Patrick Geddes (English New Towns) are viewed as visionaries. Given the laissez faire ambience of those times (and the recent resurgence of neo-liberal views), it is not surprising that the economic, political and societal impacts of the reforms of SEs, and their role as entrepreneurs of altered urban realities, were less emphasized. Consequently, most of the literature on SEs, then and now, discusses the actions of individual social entrepreneurs or their motivations, and their achievements (Bornstein 2004). The translation of innovative ideas of a few SEs or visionaries into socioeconomic change in urban space is, however, contingent. Not all visionaries with creative ideas are social entrepreneurs. Their ideas about new urban development orientations and supportive institutions need to be promoted and marketed in an ambience where agents of change confront defenders of the status quo.2 When we say the ‘‘time for an idea has come’’ what we really imply is that there is some

2

We can draw a parallel here between the differences between invention and innovation in the economic domain and the ideas of visionaries and implementation of their ideas in the social domain.

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individual or a collective which has successfully implemented a radical idea – promoting an idea, overcoming social resistance, taking risk, investing money and nonmonetary resources and other elements of entrepreneurial behavior. Thus, SEs change the behavior of other decision makers in society, by building on the ideas and research of those before them, and by their own truly original ideas. They make societal change possible through their innovative solutions and persistent strategies. Their efforts input into the perceptions and behavior of other individuals, and collectives at large, and spread through the body politic as the desirable and the doable. A major impact that SEs have are in creative institution building for service provision. The success of SEs depends on their possessing two critical, and related, attributes – namely their capacity to network and to understand the social milieu in which their innovative actions have to be embedded. Networks are ‘‘Interconnected dyadic relationships where the nodes maybe roles, individuals or organizations.’’ Networking remains crucial since entrepreneurship is a continuous process of innovative activity requiring information on new opportunities and constraints. While entrepreneurship is a continuous process, entrepreneurs are not continuously entrepreneurial. They act in the entrepreneurial role when new opportunities for intervention are identified. Networking provides information about risks, uncertainties, peer evaluation for successful venture creation and growth (see Table 7.1). It allows successful entrepreneurs to mobilize social resources and increase their stock of social capital. Networks are socially embedded relationships and network ties can be of three types – information networks, exchange networks and networks of influence. All three types of networks are common to entrepreneurs in general, though the relative weighting of these network ties varies by the type of entrepreneurial activity. Information networks are critical for SEs since they lack monetary resources (relative to business and public entrepreneurs) to implement their ideas. They need to convince a larger citizenry of the benefit and feasibility of their innovative solutions to alter existing urban realities. Their networks primarily work through informal channels of mentoring and social contact and their ability to effect change is based on people power rather than on monetary power even though access to resources are facilitative. One of the many constraints faced by SEs is the lack of funding for innovative projects. Networks of influence and exchange with public and business entrepreneurs are also critical for the success of SEs and their networks extend to entrepreneurs of the other two sectors. These networks are addressed in Sect. 7.4, while discussing complementarities between SEs and Political Entrepreneurs. SEs focus on institutional development, primarily through establishing innovative start ups or radical modification of existing not-for-profit institutions. The activities of SE are implemented through nongovernmental organizations that start small and are community based. However, these start ups have the potential to grow in their original location and, through processes of diffusion, spread to other cities in the nation and internationally. Thus there are both innovative and imitative entrepreneurs in urban transformation through SEs. The success of innovative SEs

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Table 7.1 Urban, political and social entrepreneurs: attributes, motivations, composition and networks Political entrepreneurs (PE) Social entrepreneurs (SE) Attributes and Seek political payoffs. Risk takers Aim is social value creation to motivations with strategic vision, detecting improve quality of life; potentials in localities; flexible combine innovations, and patient in order to change resourcefulness and attitudes of different types of opportunity to transform people; PEs can influence attributes of urban space and to generative allocations of urban improve social efficiency and entrepreneurial resources; (NY equality; focus on COMPSTAT), PEs can also accountability to urban cause rent seeking activities constituencies Activities Change reward structures (e.g., Problem identification, rules for entrepreneurial consciousness raising; large activities); leverage social, input of vision, determination, public funds from private and community support but limited civil society sectors; allocate money; demanders of new revenues for innovative policies; focus on risk reduction solutions; knowledge transfers; and on (change of urban various partnerships with SEs location values); providers of and EEs; co-production of targeted services; consensus urban development with building; communicate to and economic and social sectors. gain support from clients Fosters opportunities and removes barriers for SEs and EEs Composition Political and elected leaders, Not-for-profit, nonprofit, private administrators, special voluntary sector commissions, etc. Networks Node in the flow of knowledge Networks and connectivity within, linking SEs and EEs; node for and between communities resource transfers through Networks based on ‘‘trust’’ with grants, loans, loan guarantees, PEs and EEs; leverages fostering SE and EE networks, community and market power and their cooperation for locality improvement

is interlinked through knowledge networks with imitative entrepreneurs often brokered through the public sector or other resource rich SEs such as private foundations. Institution building is the major instrument in their efforts to bring about change in perceptions of what and how of social innovations. Endogenous growth theory posits the perpetuation of positive trends through dynamic knowledge accumulation. For urban welfare, this implies endogenous growth in a neighborhood will be sustained if the community continues to innovate and to make it more attractive for propulsive firms, diverse service providers and optimistic people to locate there, either through immigration or retention of local residents. SEs perform a catalytic role in propelling growth through stemming a downward spiral resulting from processes of deskilling, widening social pathologies, loss of confidence in the area, and disinvestments by public and private

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sectors – by propelling quality of life improvements, they are instrumental in causing an upward spiral through positive feedbacks. Successful urban transformation occurs only when there are economies of scope in which a variety of social entrepreneurs focus on complementary areas of activities such as housing, employment generation, crime reduction, environmental improvement through parks and recreation facilities, establishment of health clinics, drug rehab centers and so on in a specific location. While entrepreneurial communities do not simultaneously start all these innovative activities, activities confined to one or two types of service modifications will not be able to bring about urban regeneration in a neighborhood or city. Communities need to be viewed through an evolutionary prism. The combination of economies of scale and scope allow communities to move into a self sustaining path of a virtuous cycle over time as noted earlier. The more rapid the spread of complementary innovative activities, the more dynamic the neighborhood becomes.3

7.2.1

Public Entrepreneurs’ Roles in Urban Development/Regeneration

The recognition of the failure of the invisible hand of the market in the urban economy brought into focus the role of the public sector in urban public goods provision. Concepts of market failure, negative externalities and the theory of public goods provided a theoretical grounding for the involvement of the public sector in urban service provision. However, the involvement of the public sector in urban service provision (infrastructure, health, police, indigent shelter and like activities) began in late nineteenth century – under pressure from the social entrepreneurial agents such as Jane Addams, Joseph Chamberlain, and Patrick Geddes, whose interests in urban development and regeneration predated those of the public sector actors. The acceptance of the distributive role of service delivery, where the market failed to provide services particularly to indigent peoples and poor neighborhoods, fostered the professional development of urban planning and management. Thus the public sector became involved in service delivery as producers of distributive and redistributive services – e.g., housing, education, etc. (Lakshmanan and Chatterjee 2003). However, not all agents in the public sector are entrepreneurial. Most are engaged in routine activities. Public entrepreneurs (PE) are creative individuals who find innovative solutions to problems. They possess a special set of attributes discussed below. 3

When social sector agents make mistakes in judgment about resources available from governmental and nongovernmental sources, overestimate the forces of change, underestimate local urban dynamics and so on, they fail to stimulate change through new institutional development. Institutions die in their nascent stage and their efforts are lost or are adopted and modified by SE at a later time.

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Schumpeter (1984) claimed self interested motivated actors in both the public and the market spheres, were thus analogous. Just as market agents are primarily motivated by profit making considerations when producing specific goods, public officials in favoring projects are primarily motivated by the desire to get elected, for enjoying political power and prestige and the other perks of office. Business entrepreneurs create products that benefit society in the long run but these are instrumental and subservient to their profit making logic. Similar to the firms’ production of socially useful goods, the public sectors’ production of socially useful services are also instrumental means for attaining self-interested goals. Society benefits from innovations in both these sectors. Downs in the Economic Theory of Democracy (1997) further elaborates on these ideas. We can argue that both these types of entrepreneurs engage in strategic action in the sense described by Habermas (1984) in which actions are taken for self interested goals. Increase in societal resources and social welfare is a byproduct of the ambitious, self-interested actions of innovative individuals in both sectors. While this is a rather sweeping generalization and there are examples of socially well meaning entrepreneurs in both sectors who have taken innovative decisions guided primarily by their social conscience, rather than self interest, they are by and large exceptions to prove the rule.4 William Baumol (1990) pointed out that the productive contribution of society’s entrepreneurial activities varies much because of differences in the society’s incentive structures and the resultant allocation of social resources between productive activities (such as innovation) and largely unproductive activities (such as rent seeking behavior or organized crime). He argued that it was not the total pool of available entrepreneurial talent that differentiated between entrepreneurial societies in space and time. The allocation of entrepreneurial resources was heavily influenced by the relative payoffs society offers to different activities arguing ‘‘what is required in society is the adjustment of rules of the game to induce a more felicitous allocation of entrepreneurial resources’’ (William Baumol 1990, p.894).5 We extend Baumol’s central thesis to: 1. The role of public entrepreneurs in urban areas 2. Introduce spatial variables explicitly in entrepreneurial behavior Public policies can influence the allocation of entrepreneurship more effectively in urban areas than it can influence its supply. There are examples of productive and unproductive entrepreneurship in cities. For example, the selective use of arson for land clearance in urban sites and the resulting acquisition of capital from insurance claims may be entrepreneurial in nature but private gains are made at the cost of social loss. Gains from drug trafficking, pimping and other forms of social pathologies

4

Downs’ arguments in the Economic Theory of Democracy provide an insightful discussion of this issue.

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can be entrepreneurial but it is of the unproductive type. It is not uncommon to find corrupt officials and police aiding such non-beneficial entrepreneurial activities in the market with a partnership between creative corrupt officials in the public sector and illegal entrepreneurs in the private sector. In depressed, underserved urban areas entrepreneurial talents can be devoted to unproductive uses due to economic and socio-political rewards for such activities. Nevertheless, there are numerous creative individuals in local, state and federal governments who desire positive urban change for efficiency and equity reasons. PEs are able to design innovative approaches to deal with unproductive allocation of resources in urban space. With regulatory and financial powers at their command, they have the ability to change the allocation of entrepreneurial resources in urban space from destructive to constructive purposes through innovative policies. They can stop parasitical and destructive actions through changes in and the creative enforcement of rules and regulations, thereby, altering the reward structures. They can implement their creative and innovative ideas using fiscal resources, channeling general revenues, targeting of seed monies, leveraging limited city funds to change the reward structures in favor of productive activities.

7.3

Complementarities Between Urban Social and Political Entrepreneurs

Table 7.1 compares and contrasts the characteristics, objectives, composition, activities, and the networks used by contemporary urban social and political entrepreneurial agents. Urban social entrepreneurs are motivated by several objectives: social value creation; the improvement of urban quality of life; the transformation of the attributes of urban space in order to enhance urban efficiency and equality; and enhancement of the accountability of different urban constituencies. By contrast, political entrepreneurs (PEs) in urban areas seek political payoffs and direct their actions accordingly. They are risk takers with strategic views of the urban area and development potentials of localities; they are flexible and patient in order to market their ideas about these potentials and change relevant attitudes of other social, political, and economic actors. PEs can either cause rent-seeking activities or influence generative allocations of urban entrepreneurial resources [in the William Baumol (1990) sense], as noted in the NY COMPSTAT case described below. In terms of activities, urban SEs identify urban problems and opportunities, engage in consciousness raising, and demand new urban policies; they offer large input of vision, determination, and community support, but limited money; SEs engage in consensus building, communicating and gaining support from clients; SEs provide services targeted to residents and localities and reduce risks in specific localities (thereby enhancing development potential in those localities). Political entrepreneurs (PEs) change reward structures (e.g., rules for entrepreneurial activities), and foster

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opportunities and removes barriers for SEs and EEs; they leverage social and public funds from public, private and civil society sectors; allocate revenues for innovative solutions; engage in knowledge transfers, and various partnerships with SEs and EEs; PEs engage in co production of urban development with economic and social sector entrepreneurial actors. Both SEs and PEs build, maintain, and use respective networks in their activities to advance their objectives. The networks urban SEs use in interacting with PEs is based on ‘‘trust.’’ They are often deployed to leverage community power for locality improvement. Political entrepreneurial actors serve as nodes in the flow of knowledge linking PEs and SEs, and for resource transfers through grants, loans and loan guarantees, and for fostering SE and EE networks and their cooperation. We argue that entrepreneurial activities of both the public and civil society sectors are complementary even though the social and public entrepreneurs have different bottom lines. It is the convergence of these dual bottom lines (in actuality three bottom lines if we include the economic entrepreneurs) in a specific urban location that confers on that a location dynamic competitiveness and helps promote endogenous growth in the entrepreneurial city. While all types of entrepreneurs act in environments of uncertainty and ambiguity, PEs are interested in reducing development uncertainties through place specific investments in infrastructure, housing, transport and the like. PEs often partner with SEs in provision of services, since SEs are commonly early movers in sectors such as initiating health clinics, crime watch programs, drug rehabilitation, and housing for the homeless. Creating transitional housing for the homeless, and drug rehab programs have place-based benefits which are equity motivated, but also generate development payoffs and efficiency gains. By reducing uncertainty and risk in certain urban localities, the activities of SEs can attract economic entrepreneurs to locate physically their ventures in (prior) risky areas (through reduction of transaction costs). Dynamic efficiencies can be realized when creative ways of achieving equity are realized through entrepreneurial skills. As areas begin to change their attributes, alert economic entrepreneurs, with ability to use existing information on activities of SEs and PEs, perform arbitrage – thus linking all three types of entrepreneurs in urban regeneration. Such innovations usually involve the creation of new institutions or radical transformation of existing ones. For example New York city developed a crime reduction management system called COMPSTAT, which helped to reduce crime dramatically, combining innovative computer technology developed by the private sector to identify and target high crime areas with a new style of police management. The city of Chicago is an entrepreneurial city rich in all three classes of entrepreneurs. For example, the mayor’s office in cooperation with city departments of Environment, Planning, Housing, and Transportation designed and implemented The Chicago Brownfield Initiative in 1993 with an investment of $2 million (using general obligation bonds). The City then leveraged these funds to acquire $74 million of Section 108 funds from the Federal governments HUD program. Section 108 is a loan guarantee program of CDBG (Community Development Block Grants). With increasing shift from the goods producing city to a service

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oriented city, (common in advanced industrial countries), large areas of redundant, often abandoned industrial sites occur in close proximity to waterfronts, railroad depots and the like. These sites are often environmentally polluted and unproductive sites. The Chicago Brownfield Initiative recycled neglected properties and transformed blighted land with new construction of industrial parks, green spaces, affordable housing. It created 3,000 jobs and increased the tax base by more than $1 million annually. From the initial five brownfield sites the program expanded to 30 sites through leveraging USEPA money to acquire private investments for development. Chicago’s Rooftop Garden is another example of (development enhancing) creative environmental conservation, that saves $3,600 annually in energy costs of one building, improves air quality, absorbs rain water, decreases stormwater runoff and provides an urban park in a congested site. Initially, the program started on the rooftop of City Hall, an 11 storey building with a garden on 29,300 sq.feet containing 20,000 plants of 150 varieties. As of June 2004, there are more than 80 municipal buildings and countless private roof top gardens in Chicago. Since there is a 50 F difference between a garden roof and a black roof, a roof garden reduces the urban heat island effect. Roof top gardens require innovative construction technologies and the use of new materials to save roofs from water damage and to bear the load of soil and moisture. Thus the public sector had an important role in transferring knowledge to the new adoptees of the roof top garden idea, which required a combination of public, social, and private entrepreneurship. There are other examples of urban public entrepreneurship in the USA that has been stimulated by the Ford Foundation (a nonprofit Social Entrepreneur) through its Innovation in American Government program. Innovation awards honor public sector innovations from the Federal, State and local sectors. Five such innovative programs of intersector partnership and co production of services, area improvement and locality competitiveness for new economic growth in urban areas are illustrated: l

l

l

l

Santa Fe Affordable Housing Roundtable builds affordable housing for low income households through a public–private partnership of local governments, nonprofit agencies, builders and lenders. San Diego’s Single Room Occupancy Residential Hotel program promoted the development of low cost, permanent, private rental units through preservation, rehabilitation and construction incentives. The Quincy (Boston) Model Domestic Abuse program helps battered women through a two pronged effort that sanctions abusers and provides a wide variety of services to the abused. Seattle instituted a Community Voice Mail program where clients have personal telephone numbers and access codes to receive messages. Using private or public phones they can connect to potential employers, landlords and social service providers.

Tulsa, Oklahoma received an Innovation award for their interdisciplinary Sexual Assault Nurse Examiners Program that combined police, health and legal agencies

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with forensic nurses to provide treatment and follow up care. Twenty communities adopted/adapted this model within two years and at least three states had developed legislation or administrative procedures based on their program. The Ford Foundation conducted a survey of the 95 awards to urban innovators and found that over 85% of these innovations had been replicated and hundreds of communities had adapted these innovative programs to their own communities. Many of the Innovations awards provided the basis of legislation – for example the Quincy program was a model for the Federal Violence Against Women Act (1993), Seattle’s program was replicated in 15 cities and formed the basis of the Community Technology Institute which helps launch and support similar new initiatives. Over 75 cities in eight states had replicated the San Diego program. These illustrations of complementarities, partnership and joint production of urban regeneration and development among entrepreneurial actors in the urban political and social sectors highlight the importance of knowledge transmission, of network activity, and partnership between political entrepreneurs (city hall departments and city hall) and a variety of social entrepreneurs. Key elements of such partnerships are networking, information flows and mentoring potential adoptees. The consequence of such activities is the creation of urban competitiveness understood in Schumpeterian terms as possessing a ‘‘structural’’ or ‘‘systemic’’ character.

7.4

Concluding Comments

This paper argues that an entrepreneurial city functions and thrives in the global economy by creating and maintaining a favorable environment and bundles of institutions and practices, which enables the different enterprises in that city to compete entrepreneurially in the global economy. In this environment, entrepreneurial actors from the market, public, and social sectors – materially interdependent but formally autonomous organizations each of which controls important resources – coordinate their actions to secure a joint beneficial outcome in that city. They jointly fashion new economic and political capacities which support the city’s structural competitiveness. These capacities include: new economic roles and functions, an economic ambience comprising of an enterprise culture, permanent innovation, a mix of strategic vision and performance-oriented activities, and institutional innovations. Entrepreneurial actors from the three sectors, while each having different motivations, activities, resources, and networks, exploit their complementarities to capture the ‘‘added value’’ of interorganizational coordination and the creation of a competitive urban milieux (Chatterjee and Lakshmanan 2005a, b). This paper has focused on the role of two classes of urban entrepreneurial agents – political and social – in the above process of adaptation and reinvention of the city in order to be dynamically competitive in the evolving global economy. It has characterized the objectives, activities, and networks of these two sectors and how they engage in positive coordination in the larger economy of interfirm

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networks, multilateral partnerships between the three (public, social, and market) sectoral organizations, and joint production of urban regeneration and development. These ideas are illustrated from the recent urban experience of cities such as Chicago, Boston, New York, Tulsa, and Santa Fe.

References Acs ZJ, de Groot HLF, Nijkamp P (eds) (2002) The emergence of the knowledge economy. Springer, New York William Baumol J (1990) Entrepreneurship: productive, unproductive, and destructive. J Polit Econ 98:893–921 Bornstein D (2004) How to change the world: social entrepreneurs and the power of new ideas. Oxford University Press, New York Chatterjee L, Lakshmanan TR (2005a) The dual bottom line: complementarities between urban social and economic entrepreneurs. Paper presented at the Tinbergen Conference, George Mason University, July 10–11 Chatterjee L, Lakshmanan TR (2005b) Urban social and political entrepreneurship: attributes and complementarities. Paper presented at the Special Workshop at Jo¨nko¨ping International Business School, Jo¨nko¨ping, Sweden, June 16–18 Downs A (1997) Economic theory of democracy. Harper and Row, New York Habermas J (1984) Reason and rationalization of society. Theory of Communicative Action, vol1. Beacon, Boston (English translation by Thomas McCarthy) Hebert RF, Link AN (1982) The entrepreneur: mainstream views and radical critique. Praeger, New York Jessop B (1997) The entrepreneurial city: reimaging localities, redesigning economic governance, or restucturing capital. In: Jewson N, Macgregor S (eds) Transforming cities. Routledge, London, pp 29–41 Kirzner I (1973) Competition and entrepreneurship. University of Chicago Press, Chicago Knight F (1921) Risk, uncertainty, and profit. Houghton Mifflin, New York Lakshmanan TR, Chatterjee L (2003) The entrepreneurial city and the global economy. Paper presented at the International Workshop on Modern Entrepreneurship, Regional Development and Policy, The Tinbergen Institute, Amsterdam, May 23–24 Lakshmanan TR, Chatterjee L (2004) Entrepreneurship and innovation-led regional growth: the case of the entrepreneurial urban place. Paper presented at the 51st North American Regional Science International, Seattle, November 11–13 Lakshmanan TR, Chatterjee L (2006) The entrepreneurial city in the global marketplace. Int J Entrepreneurship Innov Manage 6(3):155–172 Lundwall BA, Johnson B (1994) The learning economy. J Ind Stud 1(2):23–42 Malecki E (1994) Entrepreneurship in regional and local development. Int Reg Sci Rev 16 (1–2):119–154 Schumpeter JA (1928) The instability of capitalism. Econ J 38:361–86 Schumpeter JA (1939) Business cycles. McGraw-Hill, New York Schumpeter JA (1961) The theory of economic development. Oxford University Press, New York Schumpeter JA (1984) Capitalism, socialism and democracy. Harper Collins, New York Stohr W (1989) Local development strategies to meet local crisis. Entrepreneurship Reg Dev 1(3):293–300

Chapter 8

The Social Capital of Regional Dynamics: A Policy Perspective Hans Westlund

Parts of this paper are also published in Westlund H (2006) Social capital in the knowledge economy: theory, and empirics from the United States, Japan and Sweden. Springer, Berlin, Heidelberg, New York.

8.1

Introduction

Creating something new, improving the quality and characteristics of existing products or producing things in a more cost efficient manner are three of the ways responsible for economic growth. Of these three, it is only the last one that can be considered connected to neoclassical theory, in the form of optimum combination of the given production factors under a given technology. A change in technology, as well as the sources to the other two ways contributing to the economic growth do not occur through variations in the quantities of production factors, but by the setting up of new production functions through different types of innovations, or what Schumpeter (1934, 1950) denominated as new combinations of production factors. In this expression also lies an understanding of the heterogeneity of the concepts of labor and capital and the possibility of combining an infinite number of labor and capital in an infinite number of combinations. Thus, studying innovations and economic change requires other approaches than those of traditional mainstream economics. In the last few decades, a number of such approaches have emerged: clusters and innovation systems being the most well-known. Even if the approaches often are connected to Marshall’s (1880/1920) notion of industrial districts, their theoretical base lies outside traditional economics. Also, these alternative approaches to a large

H. Westlund ¨ stersund, Sweden National Institute for Working Life, Studentplan 1, SE-831 40 O e-mail: [email protected]

C. Karlsson et al. (eds.), New Directions in Regional Economic Development, Advances in Spatial Science, DOI: 10.1007/978-3-642-01017-0_8, # Springer‐Verlag Berlin Heidelberg 2009

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extent lack the rigor of formal theory and can be considered as a conceptual framework in their early stages of development (Fischer and Fro¨hlich 2001). However, important contributions to the formalization of these approaches are those by Krugman (1991, 1995). Within the discipline of economics, the concept mostly connected to the new approaches is that of externalities. The concept of externalities dates back to Marshall (1880/1920) and has been considered one of the most intangible and hard-formalized in the economic literature (Scitovsky 1954). Sraffa (1926) considered externalities as the only source of increasing returns under perfect competition. Based on Scitovsky’s (1954) classification of externalities into pecuniary and technological, Johansson (2004) has made a fundamental distinction between firms’ intra-market and extra-market externalities. Intra-market externalities are mediated through the formation of prices, while extra-market externalities consist of links, agreements, networks and other arrangements of club type, and also information and knowledge spillovers.1 The two types of externalities have an impact on different activities of a firm. While, according to Johansson (2004) intra-market externalities affect the firm’s transaction costs and productivity, extra-market externalities affect the firm’s access to information and knowledge spillovers, i.e., its innovation potential. In both cases, space forms an important restriction implying agglomeration economies. Both intra- and extra-market externalities are distance-sensitive (as most contact-intense activities are) and space forms a restriction for the spatial reach of such externalities as transportation across space is connected with a cost. This paper focuses on the second type of externalities, the extra-market externalities, and how they contribute to the emergence of new combinations of production factors in a regional context. More precisely, it investigates the role of social capital for regional dynamics and what role policies can play in these processes. Social capital is here defined as social, non-formalized networks that are used by the networks’ nodes/actors to distribute norms, values, preferences and other social attributes and characteristics. An important feature of this definition is that it distinguishes between the networks and the norms, etc., that are distributed in the networks. Social capital can be seen as a type of infrastructure with nodes and links. The nodes consist of individuals and organizations, which establish links between each other. The creation of links is governed by the individuals’ and/or organizations’ norms, preferences and attitudes, which can prevent emergence of links between individuals or organizations as well. In the links, different types of information and knowledge are distributed between the nodes. From an infrastructure perspective, this distribution of information and knowledge can be compared with traffic in the transport infrastructure. The impact of social capital on society depends on both its quality and quantity. The norms, preferences and attitudes of the nodes, and thereby the kind of information and knowledge being distributed in

1

Information and knowledge spillovers are by Fujita and Thisse (2002) denominated communication externalities.

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the links, are at least as important as the number of links. ‘‘Strong’’ social capital can thus have preservative as well as progressive effects, depending on its qualitative characteristics.2 A starting point for this paper is that it is necessary to distinguish between a general, social capital on societal level and social capital specified for the needs of organizations (groups, firms, public sector bodies). In the latter case, social capital has characteristics of sunk costs, i.e., it often cannot be used for other purposes than it was formed for and that it might become useless or even detrimental when the organization changes its activities. Analogous with this, we can make a distinction between public social networks, which in principle everyone with certain skills have access to, and private networks, formally or informally controlled by certain groups. Section 8.2 analyzes the changes of innovation activity over time, from early industrialism to the global knowledge economy, how the relations between the actors of today’s innovation systems have developed and the role of social networks for innovations. Section 8.3 discusses the different kinds of networks built by the three constructers of social networks: the individual, the organizations and the (public and civic) society. Section 8.4 analyzes the role of public policy in building social capital for innovations and growth. Section 8.5 contains some concluding remarks and suggestions for future research.

8.2 8.2.1

Innovations and Social Capital New and Old Concepts

A number of concepts have been formulated to describe and analyze the proximityor link-based interaction between individual firms and other actors producing externalities. Industrial districts – the term coined already by Marshall – are normally defined as spatial agglomerations of SMEs in one or a few complementary industries (Paniccia 2002). In particular, the term has been used for agglomerations of SMEs in Italy. Cluster, a concept with a number of slightly different interpretations, has received, through Michael Porter’s book The Competitive Advantage of Nations (1990), an enormous amount of attention in both research and policy circles. Clusters are often defined as spatially delimited industrial systems regardless of the size of the enterprise (Paniccia 2002), but it should be noted that Porter (1990) has also considered clusters as being functional industrial systems without a proximity dimension (Malmberg 2002). Another ambiguity is that much of the cluster literature, based on Porter (1990) treats clusters as a purely a spatial concentration of related firms (see, e.g., Enright 1998), while Porter (1998, 2000) later explicitly includes public institutions, such as government educational institutions and support services, in the definition of clusters. The vast popularity of the 2

See Westlund (2004) for a more extended discussion.

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concept, not least in industrial policies, has resulted in ‘‘cluster’’ becoming a possible denomination of almost any agglomeration of economic activity. Even if clusters are thus sometimes regarded as consisting of firms as well as public institutions, both the cluster and the industrial district approach have their main focus on inter-firm relations. While the terms industrial districts and clusters have mainly been used for local and regional relations between firms, the concept of innovation systems was originally formulated for systems at a national level and denoted not only interfirm relations but also links between firms and government, firms and research institutions or between all three of them. It was used for the first time by Freeman (1987) in his analysis of the economic development of Japan after World War II, where government, especially the Ministry of Industry and Trade (MITI) played a crucial role. Leading scholars of this tradition (Lundwall 1992; Nelson 1993) have regarded the nation as the evident level of analysis as ‘‘. . . the policies and programs of national government, the laws of a nation, and the existence of a common language and shared culture define an inside and outside that can broadly affect how technical advance proceeds’’ (Nelson 1993, p.16). In the last decade the concept of regional innovation systems (RIS) has yielded a rapidly increasing literature (see, e.g., Cooke 1992, 2001, 2003; De la Mothe and Paquet 1998; Asheim and Gertler 2004; Doloreux and Parto 2004, etc.). The regional approach on innovation systems, according to Doloreux and Parto (2004) is a normative and descriptive approach, which is based on two main bodies. The first is the national innovation systems approach, based on evolutionary, nonequilibrium theories and in which innovation is a result of processes both internal and external to the firm. These processes are not only technical and economic but also social. Learning, through interaction, is a key concept in the innovation processes. The second body of literature is that of regional milieu, embeddedness and the role of proximity. According to its analysts the concept of regional innovation systems has increasingly become an all-embracing term for firms’ interaction with each other and other actors at regional level. A fourth concept, strongly linked to the abovementioned is that of triple helix, which: . . . is a spiral model of innovation that captures multiple reciprocal relationships at different points in the process of knowledge capitalization. . . . . . The triple helix denotes the university–industry–government relationship as one of relatively equal, yet interdependent, institutional spheres which overlap and take the role of the other. (Etzkowitz 2002, p. 2)

It is no coincidence that university is the actor named first. According to Etzcowitz, an important difference between the innovation system and triple helix approaches is that the former has its focus on the firm and views innovation as primarily occurring within the firm. In contrast, the view of the triple helix approach is that ‘‘Innovation is increasingly likely to come from outside of the individual firm or even from another institutional sphere such as the university. . .’’ (Etzkowitz 2002, p. 1). Triple helix processes are possible at regional, national as well as multinational level.

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The four approaches, very briefly summed up above, have one thing clearly in common: the focus on interaction where firms are involved. Apart from that, the approaches show differences between each other but also between different interpretations of the same approach, when it concerns, e.g., spatial level, included actors, their size and sectoral scope. The industrial district approach is the most limited as it only comprises interaction at local level between SMEs in one or a few closely related industries. The different interpretations of clusters – from pure industrial districts with only firms involved, to non-spatial, sectoral systems of innovation with several types of actors – is an illustration of the concept’s popularity, but also of the concept’s weakness as an analytical tool (Markusen 1999). Similar criticism has been raised against the regional innovation systems concept (Doloreux and Parto 2004), which, as shown, has also been considered as a still wider concept than the cluster. Finally, the triple helix approach is a more delimited normative approach which not only states that three types of actors should interact but also that their activities partly overlap. Moreover, triple helix’ prime focus is not on the firm’s knowledge input and innovation process but on the interaction as such and how it transforms the actors. Although not always explicitly expressed, the four approaches also have something else in common, namely their acknowledgment of externalities in the form of transfer of (tacit) knowledge or knowledge spillovers, emergence of new knowledge and (collective) learning as a primary outcome of the interaction. It is in these knowledge creating and transfer processes that social capital constitutes a ubiquitous but multifaceted factor. The ‘‘right’’ social capital facilitates or even spurs these spillovers, learning and innovation processes, whereas ‘‘wrong’’ social capital is like sand in a complicated machinery.

8.2.2

From the Lonely Genius to Innovation Nodes

The theories of (national and regional) innovation systems, clusters, industrial districts and triple helix have in common the focus on interaction between a number of key actors. The industrial district approach, as well as many other cluster approaches, concentrates solely on firms’ interaction, while other cluster approaches, the innovation systems approaches and the triple helix approach underline the interaction between at least two of, but often the three key actors of innovation: companies, public sector bodies and universities. However, this view of innovation, as a result of interaction of actors with different tasks and different principles of production and exchange (see below), is a relatively new standpoint. Historically, innovation activities seem to have had quite other characteristics than the complex systems of today. The history of technology and economic applications of technology is full of examples of individual inventors that came up with path-breaking prototypes and methods, which rapidly were commercialized into successful products. Even if we perhaps can find some examples of lonely great geniuses in the computer industry, there is no doubt that the individual inventor belonged to a certain economic era; an

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early industrial era that lasted until about World War I. Most of the world-leading corporations of today stem from that era – an era where a single innovator could build up a company from a prototype or a method. The interwar years can be viewed as a transition period from ‘‘individual inventor capitalism’’ to ‘‘corporate innovation capitalism’’. Figure 8.1 shows that albeit the gap between patents assigned to corporations and patents assigned to individuals in the USA increased slowly after 1900, the former increased rapidly after 1945, while the number of patents issued to individuals remained practically constant during the rest of the century. After World War II innovation activities seemed to have entered a new stage. With a larger public sector after the war and raised demand for, among others, military security and transportation infrastructure, governments of the developed world began to act as a qualified customer of private corporations. The most far-reaching example of this is probably what was denominated the space- and military industrial complex in the USA which had its counterparts in other countries. Some Swedish examples of this symbiosis between government and state-owned companies on the one hand and private companies on the other are: Vattenfall (hydroelectricity) and Asea (today ABB, generators and other electrical equipment); Televerket (former state telephone monopoly, now TeliaSonera) and Ericsson (switchboards and other telephone equipment); the state railways and Asea (engines) and the air force and SAAB (combat aircrafts). The common denominator in these so-called ‘‘development couples’’ was a state monopoly (complete or partial) that through its safe position could make long-term, costly R&D investment and act as qualified customer for the (at that time Swedish-owned) private companies (So¨rlin and To¨rnqvist 2000). Another example of intimate collaboration between government and private companies is the Japanese system after World War II; the system of collaboration for which the concept of innovation system was coined. Freeman (1987) noted important differences between the Japanese national system of innovation and

Fig. 8.1 Patenting in the United States 1900–2001 Source: Suarez-Villa 2004; U.S. Patent and Trademark Office

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industrial policies in other countries. Like Johnson (1982) and Lakshmanan (1994) he stressed the role of MITI, the Ministry of Industry and Trade, in identifying strategic future key technologies and actively promoting company R&D in theses technologies. Even if there were important differences between the American, the Swedish and the Japanese innovation systems during this period, they had in common the intimate cooperation between government and industry in certain key technologies. Government was a qualified customer with strong resources for R&D, which was performed in cooperation with private companies. Only certain, special fields of university research were involved in this cooperation. The innovation systems of late industrialism were mainly a system with two actors: government and private companies. The industrial crisis of the 1970s can also be considered a crisis for ‘‘corporate innovation capitalism’’. According to Fig.8.1, the number of patents assigned to corporations in the USA diminished between 1975 and 1990. Thereafter, a still much sharper increase in the number of corporations’ patents took place. It is possible to interpret this ‘‘patent explosion’’ as a new stage of innovation activity, connected to the theories of knowledge society (Andersson and Stro¨mquist 1988), Mode-2 society (Gibbons et al. 1994; Nowotny et al. 2001) and triple helix (Etzkowitz and Leydesdorff 1996). In spite of different perspectives and focuses, the three theories have in common a stress of the new role of knowledge and knowledge-producing organizations in society.3 Knowledge has been transformed from one of several resources in production to ‘‘the predominant part in the creation of wealth (. . .) in all manner of economic activity’’ (DTI 1998). While the main value of the typical manufacturing firm resided in its physical capital, the value of a knowledge intense firm is in its intellectual property. Whereas the manufacturing firm sells tangible products for consumption or refinement, the knowledge intense firm’s products consist of R&D products, including patents, with a potential for being commercialized and profitable. Innovations, defined as new combinations of production factors have become the core of knowledge society. However, the innovation activities of the knowledge society differ fundamentally from those of the early industrial period. Innovation activity in the knowledge society is a collective process in which people and organizations have to cooperate. This is the circumstance brought up in the ‘‘macro’’ theories of innovation systems with three actors and that of triple helix. On micro level, innovation activity in the knowledge society seems to require a permanent flow of new information and knowledge, which in practice means a flow and exchange of people in the innumerable innovation processes of everyday (see, e.g., Kobayashi and Takebayashi 2000). As been pointed out by many scholars this gives the great cities a special role as knowledge and innovation nodes. Their size

3 Here it should be noted that we in line with North (1990) make a distinction between organizations (firms, governmental organizations, universities and NGOs) and institutions (laws and regulations, formal and informal rules of the game).

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creates a diversity that makes specialized supply and demand – and new combinations of both categories – possible. This means that the knowledge economy not only creates another type of innovation than the late industrial society but also changes the spatial allocation of production factors. As the great cities become centers for the increasingly important production factor, i.e., human capital, they also emerge as stabilizing factors in the global economy where knowledge is footloose but human beings and the organizations of knowledge are more strongly rooted. To partly quote Markusen (1996), the great cities are sticky, innovation nodes in a space where information and knowledge ‘‘slip’’ around.

8.2.3

Why Care About Social Links?

From what is said above it seems as innovation over time has become an increasingly complex process. It is an exaggeration to say that innovation in the early industrial period was only a process of merging technology and capital in the form of providing the innovator with financial resources to start production. Access to capital, finding the right customer and getting the innovation accepted was also essential in the nineteenth century.4 However, in the knowledge society, innovation activities can be divided in a large number of stages from basic research, via, e.g., development, testing, licensing, marketing and sales to final use, each of them requiring a certain partner for example financing. One way to express the differences between innovation activities in the two periods is to say that they differ substantially in the number of actors involved, in the number of links between them, and in the amount of knowledge and information being distributed between the actors. The emergence of spatial clusters and regional innovation systems can be viewed as an expression of the intra- and extra-market externalities and their distance-dependency. Following Johansson (2004), it can be assumed that knowledge transfers take place through two types of processes: 1. Deliberate, formalized transaction-links, agreements, networks and other clublike arrangements between firms and between firms and other actors. 2. Unintended knowledge spillovers between firms or between firms and other actors, caused by non-formalized interactions. These kinds of interactions consist of: l

4

Vertical technical/economic interactions between firms and their suppliers and/or customers.

The problems with getting an innovation accepted can be exemplified with John Ericsson’s steam fire-engine, which was successfully demonstrated in London in the year 1829 and caused such an anxiety in ‘‘The London Fire Brigade’’, practically a guild with monopoly on fire-fighting, that it was rejected by the committee in charge (Goldkuhl 1961).

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Spin-offs of new firms from existing ones and turnover and exchange on the labor market. Horizontal interaction in the form of informal exchange of information and knowledge in the (local/regional) civil society, between individuals connected to firms or other actors.

In both these two types of processes, social links and the norms and values connected to them play an important role. The reason is simply that good social relations facilitate knowledge transfers while lack of relations or bad relations do not. Regions have different prerequisites to deal with this circumstance. Diversified metropolitan regions consist of a number of co-located sectoral clusters that often do not have more in common than the use of the regional infrastructure and certain regional markets. Apart from that, each cluster has its own links, those external to the firm but internal to the cluster, and those between the clustered firms and the rest of the world. The relations of each cluster are formed in accordance with the stages of innovation, types of production, positions in the product life cycles, etc. In this way a metropolitan region can accommodate competitive clusters in both expanding and declining sectors. If small regions contain any clusters, it is with few exceptions only one cluster. Regardless of the sector of the cluster – expanding or declining – the small regions’ development is highly dependent on the quality of the cluster’s social relations. Well functioning internal and external social relations facilitate acquiring of knowledge and information about changes in demand, new methods, etc., as well as credits. Smaller regions, being dependent on one cluster are in general more vulnerable and more dependent on good relations between all relevant regional actors.5 Who are these actors that build and maintain this essential social capital? This question is dealt with in the next section.

8.3

Social Capital on Three Levels

The theories of innovation systems and triple helix concentrate on the interplay between different types of organizations. However, organizations represent only one of three levels in which social capital can be analyzed. Individuals build organizations and together those levels form a society. This section discusses the social capital built by the actors on the three levels and how these forms of social capital are based on the fundamental needs and aims of these actors.

5

In a study of determinants of economic growth in the Swedish municipalities, Eliasson et al. (2005) found that the importance of business-related social capital decreased with municipality size.

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Organizations and Their Social Capital

As analyses and policies for innovations are primarily focused on organizations it might be appropriate to start the discussion on this level. According to the policies based on modern innovation theories, the three types of organizations – firms, universities and government – should cooperate in order to meet the needs of the knowledge economy. Universities should provide the knowledge, government provides favorable institutions and development resources and firms provide resources and know-how for commercialization. However, a dilemma is that the three actor blocs are based on different principles of exchange, which are reflected in different rules of the game. Following Polanyi (1944) it can be argued that a firm bases its activities on a market principle where profit is a necessary ingredient. For public government, which has the power to collect individuals’ and organizations’ resources and redistribute them, the basic principle is redistribution. The third type of organization, the academy (or university), is for its part historically predominated by a third principle, viz. reciprocity – a mutual exchange of knowledge and ideas. Academy-produced knowledge is by tradition neither sold on a market nor taken from one actor and given to another, but exchanged and valued by equals (peers) without any losses. It goes without saying that organizations with such principal differences build social capital with very dissimilar networks, which connect different types of actors and are based on different norms and attitudes. The activities of the firm are executed with the aim of making profit. The firm builds technical and economic links internally and to external actors. These links are established and maintained if they are assessed to bring net revenues. The social networks of a firm are based on more compound motives. Creation and maintenance of social links that the firm makes deliberate investment in – e.g. corporate culture, personal customer relations, etc. – are in principle controlled by the same net revenue principle as economic links.6 But many social networks are unintended by-products of other interactions (Putnam 1993). Thus, many social links of the firm are by-products of its economic networks. To the extent to which human beings are involved, social links/relations develop as a consequence of the economic links. Consequently, a firm makes certain deliberate investment in social networks, but many of the social networks of a firm are by-products of technical and economic networks. Accordingly, companies’ social networks have two sources: deliberate, formal investment decisions by management on different levels, in accordance with the firm’s basic mission, and spontaneous, informal investment decisions by individuals, originally connected through the economic links, based on a volition to interact, to socialize. The volition to interact is connected to the ‘‘affinity’’ – here defined as attraction, liking or feeling of kinship – between the actors (cf. Johansson 6

However, according to modern managerial theory of the firm, managers might have personal goals that include other things than profits, i.e., managers might benefit from social capital independently of their firms’ profits.

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and Westin 1994). For a social link to be established, the nodes/actors should have something in common (e.g., some norms, values or preferences, cultural similarities, or some minimum degree of mutual trust). Moreover, economic interaction can to some extent be governed by the ease of formation of social capital between actors. Rauch (1996, 1999, 2001) has underlined the role of social capital and networks for international trade and for example shown the significant impact of common language/colonial ties on trade between countries. The second type of organization, public government, is run by political objectives, but a fundamental need for public government is to legitimize itself. For this reason it builds social links to the citizens and organizations of society, beside the necessary economic and technical networks it needs to develop in order to fulfill its objectives. As in the case of the firm, public government’s activities also create ‘‘uncontrolled’’ social networks as by-products. However, as the basic mission of public government is to redistribute the resources of society, both the intended and unintended social networks of government, and the norms and values distributed in them, fulfill other objectives than the social networks of the firm. The third type of organization is the academy. In spite of the fact that it is financed in a number of different ways, it has an international, joint identity with missions, objectives and norms. This academy-internal social capital is an important reason behind the academy’s relative independence vis-a`-vis other actors in society. It is on the other hand a potential obstacle to collaboration with organizations having other missions and social capitals. These three types of organizations build social capital deliberately and contribute to unintended, spontaneous social capital-building as well. Table 8.1 describes the different component parts of organizations’ social capital. Depending on the organization’s mission, certain norms, values and attitudes are developed, which in their turn govern the extension and allocation of the organization’s internal and external links. Analogous to the increased complexity of innovation activities over time, discussed in Sect. 8.2, it can be argued that organizations’ social capital has Table 8.1 Social capital of organizations broken down into different component parts Organization-internal The organization’s external social capital social capital Activity-related Environment-related Market-related Links/relations filled Links/relations to Links/relations to the General relations to the with attitudes, norms, suppliers, customers, local/regional anonymous mass of traditions, etc., that are clients, partners in environment, to (actual and potential) expressed in the form cooperation and organizations of the customers and clients, of internal ‘‘spirit’’; development two other types, (non- built through climate for activity-related links to) marketing, customer/ cooperation; and other organizations of client clubs, programs, methods for codifying the same type etc., and expressed in, knowledge, product e.g., trademarks development, conflict resolution, etc.

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become more and more complex. The assembly line–the archetypical symbol for manufacturing industrialism – required few social skills of its workers, not even a common language. In contrast, work in a consultancy company of today requires ability to cooperate, build networks and even certain attitudes. People without this social competence do not get access to the social capital of these companies of the knowledge economy. In the period when public government was small, it was held together by a strong social capital, expressed in the ideology of the public official, standing above the interest groups of society. The increased involvement of government in different areas of society has made its mission much more complex and consequently also the economic, technical and social networks of government and the values distributed within the social networks have become more composite. The same can be said about the academy. As long as the university employed small elite of researchers and students, it was easy to keep its identity, values and networks. With increased resources and increased demands from the resource-providers, university’s tasks have multiplied, and also its networks. Thus, the fact that the three types of organizations discussed are based on different missions result in different forms of social capital. These forms are an outcome of both intended and unintended investment. Over time, along with the evolvement of a knowledge society from an industrial society, these forms of social capital of organizations have become more complex. Without considering the different missions and the differences in social capital of the three types of organizations, modern innovation policies prescribe that they should interact and create innovations. The problem is described in Table 8.2. The traditional activities of the types of organizations are market with an O. The consequence of innovation policies is that actors of the three organizational types partly should expand their activities to the fields traditionally upheld by the other types of actors. A successful fulfillment of these expectations demands new strategies for combining the organization’s core activity, O, with the new activities (o) that with few exceptions has not been involved with previously. The theories behind the modern innovation policies are most likely based on empirical observations of an expansion of the organizations’ activities outside their traditional fields. There is, for instance, some evidence that government in many countries is acting less redistributional and more growth-oriented. Universities are increasingly facing a situation where they either have to cut down or act more entrepreneurial. As the knowledge economy expands, companies get stronger incentives to collaborate with universities. However, the traditional Table 8.2 The traditional activity of the three types of organization O, and the activities expected by modern innovation policies (o) Activity Type of organization University Government Firm Education and research O (o) (o) Public infrastructure and service (o) O (o) Product development and production for profit (o) (o) O

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norms, values and networks, i.e., the existing social capital of each of the type of organization, are formed in accordance with their traditional activity and not changed from one year to another. Thereby, the established social capital of the organizations constitutes intangible obstacles to the implementation of the modern innovation policies.

8.3.2

Social Capital of the Individual

The needs of an individual are other than those of an organization. A primary need of a human being is some form of safety. This affects many acts: work is not only an activity for a pay but also something that colleagues contribute to achieve a high degree of social safety; socialize with friends, and raise a family. In short: individuals construct social capital with relatives, friends and workmates on a day-to-day basis. Relations are built; values and norms are formed to create the necessary stability and safety in a world of uncertainty. The social capital formed by individuals at their workplace falls under the category of spontaneously created organizational social capital. This social capital is not controlled by the organization, but as it is built on workplace relations it has, in varying degree an (positive or negative) impact on the organization’s innovation potential. From a traditional view of economics, it is harder to find any arguments for the impact on innovations of social capital individuals build on their leisure time. By definition, working time is production but leisure time is consumption and for that reason there are more reasons to expect innovations occurring on working time. However, as stated in the introduction of this paper, traditional economics may not be the best tool for analyzing innovations and economic transformation. Moreover, it can be hypothesized that the sharp dichotomy between production time and consumption time (work and leisure) of the industrial economy increasingly is being dissolved in the knowledge economy. Informal discussions, information exchange, evaluations, negotiations, etc., connected to production activities are going on during peoples’ leisure time. This would mean that individuals’ social activities during their leisure time contribute to the forming of a place surplus (Bolton 2002; Westlund and Bolton 2003) which indirectly may have an impact on the development on innovations, their commercialization and diffusion. Concerning the individual’s social capital, we also should note that some of the social capital created by groups of individuals indeed is destructive for innovations and growth. One obvious example is the social capital of individuals in criminal gangs. Another example is, what is referred to ‘‘unemployment cultures’’ in deindustrialized or low developed areas. Both are examples of social capital that have emerged from fundamental needs of safety and which in the given situation are experienced as positive for the concerned individuals – without contributing to positive innovations or economic growth.

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Society’s Social Capital

We have established that social capital is built by organizations and individuals, i.e., units with some kind of autonomous decision power. In organizations, public government is also included. However, society in its general meaning, consisting of all individuals and organizations, has no decision power of its own. In what sense is it then possible to talk about society’s social capital? The answer is that society’s social capital can be described as the lowest common denominator of all the networks, norms and values existing among all individuals and organizations in society. Thus, a society with many separate networks and few common norms and values can be characterized as a very heterogeneous society with a ‘‘weak’’ social capital, while a society with few and overlapping networks and many common norms and values can be characterized as a very homogeneous society with a ‘‘strong’’ social capital. But – it is not necessarily that ‘‘weak social capital’’ is always ‘‘bad’’ and ‘‘strong social capital’’ is always ‘‘good’’. One example of a strong social capital on societal level is the Swedish ‘‘local industrial community spirit’’ (bruksanda) which is characterized by small and midsized places with one dominant manufacturing industry during the industrial epoch. A spirit of common interest, formed through demands and counter-demands, resulted in the local factory taking responsibility for the welfare of their employees and their families in exchange for the loyalty of the families to the local factory. Other enterprises, apart from the necessary local service businesses, were potential competitors for the labor force which were regarded as unnecessary. As a consequence, entrepreneurship and establishment of new enterprises were not supported by the norms and values of the local industrial community spirit. The factory and the workers opposed consciously or subconsciously the emergence of new economic actors. During Sweden’s late industrial era, the local industrial community spirit was a local expression of the ideology behind the successful Swedish Model of stable growth and national understanding. On the other hand, during the structural adjustment since the 1970s, this spirit has been a critical problem for these communities. When the context changed, the communities needed actors to renew both the local industry structure and the local social capital. However, to a large extent, the local industrial community spirit blocked the emergence of such renewers. The Swedish Model, of which bruksandan was one component part had its great days from the 1930s to the 1960s. Since then, Sweden has become globalized and the knowledge society has replaced the manufacturing-industrial society. Sweden has also become much more diversified in a number of respects, not least concerning lifestyles. There is a huge new formation and inflow of social capital, among youth, immigrants and people in new professions. In this respect, there is certainly no shortage of social capital in Sweden. However, on societal level, be it a city, region of the whole nation, the social capital is ‘‘weakened’’, with less common denominators than during the days of the Swedish Model. This conclusion is well in line with Putnam’s (2000, 2001) results that the social capital in the USA – and probably also other parts of the developed world – is

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‘‘weakened’’. But Putnam’s measurements of social capital in the USA are per se a good reason to question his earlier claims (Putnam 1993) of a general correlation between social capital on societal level and economic development. In spite of several decades of weakened social capital (in Putnam’s measures), the USA experienced a remarkably strong economic growth in the 1990s (when the knowledge economy expanded) – a circumstance that stands in complete contrast to what Putnam (1993) found in his study of Italy up to the 1970s (i.e., the industrial epoch). The reason is probably that Putnam’s measures of social capital are focused on the homogeneity of society. Measured in this way, the American regions that in the 1990s scored highest were homogeneous, stagnating, depopulous regions with limited immigration. Consequently, expanding metropolises like Los Angeles showed a very low social capital in his measures. A reasonable hypothesis could be that the homogeneous social capital that Putnam (1993, 2000) focuses on, in general stood in a positive, mutual, selfreinforcing relationship with economic growth during the late industrial period, which in most developed countries lasted up to the 1970s but in Japan lasted until about 1990. During this period, economic growth was built on mass production based on improvement of old innovations through increased capital intensity of production, without any need for new, path-breaking innovations. The decline of industrial society and emergence of knowledge society has changed these conditions dramatically. Computerization and other applications of digital technology have together with other emerging technologies brought innovations back as an essential ingredient for growth. In other words: new combinations of production factors have once again emerged as important – and a social capital that facilitates and promotes these new combinations is needed. In this way, the formation of social capital, the forces for continuity and for change of the content of social capital are processes that evolve in response to the changing societal conditions. It can be assumed that the quantity of ‘‘new combinations’’ is dependent on the quantity and quality of production factors, including the bearers of human capital. This would mean that societies with a certain grade of diversity would promote new combinations. A social capital of certain degree of heterogeneity would in that case be best suited for the current stage of knowledge society. As metropolitan regions often are the most diversified, this can explain why they normally are the centers of growth in the knowledge economy. However, diversity without coherent forces would end up in anarchy. Other characteristics, such as mutual tolerance, are needed to utilize diversity. This line of reasoning corresponds to that of Florida (2002, 2005). Albeit Florida (2002, 2005) avoids using the term social capital – in order to distance himself from Putnam (1993, 2000) – Florida’s contributions center on the role of social norms and values, the networks that are based on them and their impact on regional dynamics. However, Putnam’s and Florida’s theories on the homogeneous social capital and on the importance of diversity and tolerance for regions’ growth respectively, have in common that a large number of links in the cause-and-effect-chain are only assumed but not investigated. Moreover, a weakness in both Putnam’s and Florida’s hypotheses is that they only deal with the social

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capital of civil society. The social networks and norms of companies and the other actors of clusters and innovation systems are remarkably absent in their hypotheses.

8.4 8.4.1

Public Policies for Economic and Social Innovations Policies on Different Spatial Levels

In Sect. 8.2, innovations were treated solely from an economic perspective. However, analogous to Chatterjee and Lakshmanan’s contribution to this volume where they distinguish between economic, social and political entrepreneurship, we can also make a difference between economic, social and political innovations. In the remainder of this paper we deal with the first two types of innovation: economic and social. In the former section, three sources of social capital were discerned: the individuals, the organizations and society. If social capital has come to play an increasingly important role for innovations as the innovation system has become more complex, there are obvious reasons to ask what public policies can do to contribute forming social capital with as advantageous characteristics as possible for innovations and growth. Starting with the social capital of individuals, it can be argued that the individual as member of a family, neighborhood and leisure clubs in general gets connected to and forms his/her own links to get connected to social networks in accordance with his/her basic preferences. From the perspective of innovations and growth there are often no motives for public policies improving the social capital of individuals. However, there are many examples of social networks and values that diminish the potential for innovations and growth. Networks based on ethnicity, religion, neighborhood, etc., may on the one hand act as critical support structures for its members’ economic activities. Businesses based on ethnicity can often exploit certain niches and have often low transaction costs. On the other hand, these networks with their particular norms and values may simultaneously lead to lock-ins in low-productive activities and non-efficient utilizing of resources. Thus, there may be good arguments for policies aiming at creating new links, improving access for individuals in certain groups, to new networks. When it comes to organizations, we have already shown that organizations are the prime builders and maintainers of their own social capital. Concerning public sector organizations’ social capital, it is self-evident that it is governed by public policies. Regarding the social capital of firms and other organizations independent of government, the influence of public policies is much smaller, but laws and regulations affect the activities of organizations, their social capital-building included. From the perspective of innovations and growth, what would be the motives for public policies aimed at influencing organizations’ social capital? The answer lies in the increased complexity of innovation processes, discussed above. Innovation is

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no longer dependent on only combination of production factors but also on actors. The role of policies in general terms is to facilitate actors’ interaction – individuals’ and organizations’ interaction with government included. The only question is: how can this interaction be facilitated? On a national level, the role of governmental policies is mainly indirect. Government can establish good relations with national organizations and leading individuals; change and adapt laws, impose regulations and taxes; create platforms and gather actors, etc., thereby contributing to improvements of the ‘‘innovation climate’’. More direct effects on innovations might come out of direct initiatives and projects with selected actors.7 On the other hand, such direct initiatives from above have a higher risk for failure, due to lack of information and (tacit) knowledge, possessed by other actors. On regional and local level, government can play a much more direct role in getting the actors of innovation together and promoting good relations between them, i.e., to ‘‘create’’ and support clusters. However, a problem is that the leading actors of today not necessarily are those of tomorrow. Companies that have their expansion phase behind them might also have their most innovative phase behind them. The same holds for established, large organizations. Thus, governmental innovation policies at regional and local levels might easily become a victim of path dependencies and promote a social capital that opposes new innovations. Although Schumpeter did not use the concept of path dependencies, he was clearly aware of the problem when he described the problem of social environment’s dislike of changes which might go as far as ‘‘. . . social ostracism and finally to physical prevention or to direct attack’’ (Schumpeter 1934, p.87). Schumpeter’s arguments were of course based on the fact that innovations often bring creative destruction that strikes certain actors. This circumstance makes governmental innovation policies at regional and local level more complicated than normally are taken into account. However, a perhaps bigger problem with current policies for economic and social innovations is the abovementioned fact that the three types of actors have different missions, different core-activities and consequently different norms and values. Innovation policies are normally based on the assumption that the actors of the desired cooperation have a common denominator large enough to motivate investing resources in long-term cooperation. Investment of these resources may in itself be seen as a proof of that common denominator and innovation policies seem to be built on the assumption that the projects strengthen this common denominator automatically. The issue of the cooperating actors’ social relations and norms and values are normally not considered in innovation and cluster policies. Instead, these issues are mainly paid attention to in social and welfare (i.e., redistribution) policies. This problem is illustrated by three Swedish examples.

7 The post-war Japanese National System of Innovation was according to Johnson (1982) and Freeman (1987) a successful example of national innovation policies with such direct effects on innovations.

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Three Swedish Examples

VINNOVA, the Swedish agency for innovation systems, was launched in 2001 and has an annual budget of approximately $140,000,000. A substantial part of the ¨ XT, resources are used in different R&D programs. One of these programs, VINNVA offers 50% support to selected long-term (10 years) regional innovation projects. Among the selection criteria are a couple of factors that can be connected to relations and values: the regional leadership should support renewal and there should exist a shared vision. A fundamental idea for the program is triple helix-cooperation between the three actors: business, government and academy. However, the problems connected to the different missions, norms and values of the three actors are not considered. In the projects that so far have been supported, the perceived common denominator is reflected in the actors’ investment of own resources. Apart from investment in the projects’ own trademarks and information, there are very few features of relation-building and other activities that can be compared with individual firms’ investment in corporate culture. One exception is the project for the biotech cluster in Uppsala, which contains ideas about pub evenings for actors in the biotech sector. The program is simply based on the idea that the common denominator exists, that it is sufficiently strong in itself and that there is no need for particular investment in social relations, joint norms and values for the actors in the projects. A second example, a Metropolitan Policy Program for deprived urban neighborhoods in the three biggest cities was launched by the Swedish Government in 1998. The overall goals of the policy are to increase the prospects of the Swedish metropolitan regions for long-term sustainable growth, primarily by contributing to new job opportunities, and to stop social, ethnic and discriminatory segregation. In order to achieve these goals, it could be expected that actions for local innovations and entrepreneurship would be taken. However, hardly any such projects have been started. Instead a large number of other projects in a number of areas were launched. A substantial part of the projects was concentrated on issues related to social capital, aiming at strengthening the cohesion of the neighborhood by changing attitudes and building links between different groups and individuals. Instead of building social capital connected to production, the metropolitan policy has focused on social capital connected to consumption, i.e., peoples’ leisure, living and culture. Instead of building links between the deprived neighborhoods and their inhabitants and the rest of the metropolitan regions, activities were mainly concentrated on the pure local neighborhoods. The third example is the National Delegation for Regional Cooperation on Higher Education, active from 2002 to 2004. The delegation gave economic and supervisory support to projects where universities, public sector bodies and companies collaborated on developing new education projects, adapted to the regional labor market, and to a more general ‘‘platform-building’’ for possible future collaboration between the three actors. Even if social capital-building was not an explicit aim for the delegation, the official evaluation of the delegation concluded that the

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delegation in practice supported the forming of new social capital through creating new relations between regional actors. The evaluation found that this implicit aim was successfully fulfilled in many projects, but that in many cases it was highly uncertain whether the collaboration would continue when the project grants were finished (Westlund et al. 2005). The three examples show the dilemma of current sectoral policies: on the one hand growth policies through cluster and innovation policies, without understanding of the role of social networks, norms and values; on the other hand policies aiming at growth and social equalization through building local social networks and joint norms and values, without understanding the role of innovation, entrepreneurship and the intraregional labor market; and the support for short-term projects without any strategy as to how to develop the newly established networks of collaboration.

8.5

Concluding Remarks

Innovation has become an increasingly complex process with an increasing number of interacting actors involved. One of the things that facilitate this interaction is positive social relations between the actors. In the wake of the emergence of the knowledge economy, new theories, as those of clusters and regional innovation systems, have stressed region as the spatial level where innovation processes take place. The actors of the economy mainly form their social capital themselves. Whereas most actors solely act in accordance with their own needs, government is the only actor that must take the ‘‘public interest’’ into consideration. This means that governmental policies have a central role in the forming and reforming of regions’ social capital. This circumstance has so far mainly had an impact on social and welfare policies, but very little influence on policies for economic transformation and growth. Thus, research on the social capital of the actors’ of innovation would shed new light on critical aspects of these processes. One of these critical aspects is that the three actor blocs of innovation systems and triple helix have different missions and base their activities on different principles. The fact that government already is launching policies for clusters, innovation systems and triple helix, is in itself a strong argument in favor of conducting further research in the area.

Acknowledgments The author has benefited from comments from Martin Andersson, Kiyoshi Kobayashi, Takashi Omori and three anonymous referees.

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Markusen A (1996) Sticky places in slippery space: a typology of industrial districts. Econ Geogr 72:293–313 Markusen A (1999) Fuzzy concepts, scanty evidence, policy distance: the case for rigour and policy relevance in critical regional studies. Reg Stud 33:869–884 Marshall A (1880/1920) Principles of economics: an introductory volume, 8th edn. Macmillan, London Nelson R (ed) (1993) National innovation systems: a comparative analysis. Oxford University Press, Oxford North D (1990) Institutions, institutional change and economic performance. Cambridge University Press, Cambridge Nowotny H, Scott P, Gibbons M (2001) Re-thinking science: knowledge and the public in an age of uncertainty. Polity, Cambridge Paniccia I (2002) Industrial districts: evolution and competitiveness in italian firms. Edward Elgar, Cheltenham Polanyi K (1944) The great transformation. Beacon, Boston Porter M (1990) The competitive advantage of nations. Macmillan, Basingstoke Porter M (1998) Clusters and the new economics of competition. Harv Bus Rev 76:77–90 Porter M (2000) Location, clusters and company strategy. In: Clark GL, Feldman MP, Gertler MS (eds) The Oxford handbook of economic geography. Oxford University Press, Oxford, pp 253–274 Putnam RD (1993) Making democracy work. Civic traditions in modern Italy. Princeton University Press, Princeton Putnam RD (2000) Bowling alone. The collapse and revival of American community. Simon & Schuster, New York Putnam RD (2001) Social capital community benchmark survey: community result matrix. http:// www.ksg.harvard.edu/saguaro/communitysurvey/results_matrix.html (2004-01-16) Rauch JE (1996) Trade and search: social capital, sogo shosa, and spillovers, Working paper 5618. National Bureau for Economic Research, Cambridge, MA Rauch JE (1999) Networks versus markets in international trade. J Int Econ 48:7–35 Rauch JE (2001) Business and networks in international trade. J Econ Lit XXXIX:1177–1203 Schumpeter JA (1934) The theory of economic development. Harvard University Press, Cambridge, MA Schumpeter JA (1950) Capitalism, socialism, and democracy, 3rd edn. Harper, New York Scitovsky T (1954) Two concepts of external economies. J Polit Econ 62:143–151 So¨rlin S, To¨rnqvist G (2000) Kunskap fo¨r va¨lsta˚nd: Universiteten och omvandlingen av Sverige. SNS, Stockholm Sraffa P (1926) The laws of returns under competitive conditions. Econ J 40:79–116 Suarez-Villa L (2004) Technocapitalism and the new ecology of entrepreneurship. In: de Groot HLF, Nijkamp P, Stough R (eds) Entrepreneurship and regional economic development: a spatial perspective. Edward Elgar, Cheltenham Westlund H (2004) Social capital and the emergence of the knowledge society: a comparison of ¨ stersund Sweden, Japan and the USA. ITPS, O Westlund H, Bolton R (2003) Local social capital and entrepreneurship. Small Bus Econ 21:77–113 Westlund H, Decaio E, Johansson M (2005) Utva¨rdering av Delegationen fo¨r Regional Samverkan ¨ stersund om Ho¨gre Utbildning. ITPS, O

Chapter 9

Hidden Order in Traffic Flows Using Approximate Entropy: An Illustration Kingsley Haynes, Rajendra Kulkarni, and Roger Stough

9.1

Introduction

The dynamic nature of traffic flows on urban freeways is self-evident. The plots of workday traffic on segments of major roads against time of day display the familiar contours of lumpy, peaked curves. Over the years the peaks have become blunt and the valleys filled, suggesting nearly day long high-volume traffic. At the same time that the average travel speed on congested freeways has gone up, average commute time has either remained steady or increased marginally and the number of accidents per 100 million VMTs has gone down or remained constant (Gordon et al. 1991; BTS 2006). Traffic at high volumes and high speeds or under designed roads should result in more accidents and slower travel times. This has not occurred but traffic has continued to increase. Congested traffic patterns suggest an inherent disorder or randomness. Could it be that there is a hidden order in the congested traffic patterns? It would be helpful to analyze and understand these linear spatial patterns to see the degree to which order/disorder associated with these patterns can be determined.

9.2

Level of Service

Currently, congestion is measured as either a ‘‘Level of Service’’ (LOS) category (HCM 1998a) or as a volume to capacity (V/C) ratio (Meyer 1994). However, both of these measures are found increasingly to be inadequate in describing the nature of high volume congested traffic in urban regions with growing travel demand.

R.R. Stough (*) Vice President for Research and Economic Development, George Mason University e-mail: [email protected]

C. Karlsson et al. (eds.), New Directions in Regional Economic Development, Advances in Spatial Science, DOI: 10.1007/978-3-642-01017-0_9, # Springer‐Verlag Berlin Heidelberg 2009

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Usually, traffic flow data are gathered by direct observation using permanent and/or seasonal traffic sensor counters installed on roads, and in some cases, aerial surveys (SkyComp 1996). These data are used by transportation agencies to plan for land use changes, design road patterns and capacity levels and schedule activity such as construction and maintenance work. Analysis of such data is also useful to real time traffic management centers in controlling for the effects of congestion, and ultimately it is useful to the users of road networks if the data can be used to produce the appropriate kinds of information to support decentralized decision making. Figure 9.1 shows traffic patterns for various linear spatial segments of highvolume urban freeways in terms of level of service (LOS). The letters ‘‘A’’ through ‘‘F’’ (HCM 1998b) are proxies for the number of vehicles per lane per mile in a given time period. The Highway Capacity Manual (HCM) defines LOS as a ‘‘qualitative measure describing operational conditions within a traffic stream, and their perception by motorists and/or passengers’’. To quote TRB Special Report 242, ‘‘Congestion for any facility using LOS approach depends on the quality of service ‘expected’, which may vary between designers and users and even among users. . . .The judgment of the designer and analyst plays a large part in what is defined as congestion (p.21)’’. From this explanation it is obvious that, there is an inherent fuzziness associated with LOS definitions of A–F. For example, according to the Highway Capacity Manual, the spectrum of LOS from ‘‘A’’ to ‘‘F’’ grades the traffic flows in terms of a certain number of cars per lane per mile. The LOS ‘‘A’’ A C

A

A

B C A A

C B

B C

B C C D C F

C F E B B

D E C C B

A A

Fig. 9.1 LOS on multiple links of a freeway network

B

A

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Table 9.1 Average separation distance and LOS expressed as alphabets Level of service (LOS)~ Avg. distance in feet Level of service (LOS) number of cars/lane-mile between two consecutive expressed as alphabets (C/lm) cars and number of cars (C) A~12 ~440 (~22) a~b+c B~20 ~260 (~13) b~c+d C~30 ~175 (~09) c~d+e D~42 ~125 (~06) d~e+f E~67 ~080 (~04) E F>67 ~020 (~01) F

pattern has thirteen or fewer cars representing free flow condition, while LOS ‘‘F’’ at the other end of the spectrum represents near breakdown in flow with more than 67 cars per lane per mile (see Table 9.1). Another way to look at LOS is in terms of the average space – or separation distance – between two vehicles in a lane segment. Column 2 in Table 9.1 shows the corresponding separation distances for each level of service (A through F) measured in terms of the number of car spaces. We represent these in small case letters ‘‘a’’ through ‘‘f’’. The representation of the separation distances (‘‘a’’, ‘‘b’’, ‘‘c’’, ‘‘d’’, ‘‘e’’ and ‘‘f’’) will be used to develop the production rules for a flow of traffic. These rules will allow us to express complex traffic patterns in a single comparable metric. Below we discuss computational (Kolmogorov) entropy as it relates to traffic flow. We do this in the most minimalist way possible so that operational background issues are covered in order to move to a statistical specification of approximate entropy.

9.3

Kolmogorov’s Entropy and Traffic Flow

Consider a coin tossing experiment. For each toss, if the outcome is a head, we write ‘‘1’’, otherwise we write ‘‘0’’. The following is one of the possible outcomes of a large number of coin tosses s ¼ 001010001110100010:::000110101000:

ð9:1Þ

Though the frequency of number of tails and heads (zeros and ones) turns out to be nearly 1/2 as the number of coin tosses tends to infinity, the series can never be predicted to reproduce itself exactly in the same way as is shown above. Hence the randomness of this series is measured in terms of the length of the series ~| s |, or the number of bits needed to specify the series ‘‘s’’. On the other hand, if with a biased coin we get only heads (all ones) or only tails (all zeros), obviously a non-random sequence, its randomness is simply the number of bits needed to specify the number ‘‘n’’. For example, a series with random heads and tails of length 100 would need 100 (binary) bits to specify that series, while a series of 100 ones (or zeros) can be specified by a maximum of seven binary bits (27 >100). Kolmogorov’s entropy ‘‘k’’

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of a sequence ‘‘s’’ is measured in terms of the number of bits of the smallest program that can regenerate the sequence. Thus, kðsÞ ¼ js j;

ð9:2Þ

where the right hand side of the above equation measures the length of the program s*. Of course, if there is no such program then the length of the entire sequence ‘‘s’’ in bits becomes the entropy of the sequence. Thus the number of binary bits needed to specify a sequence can be used as a measure of the entropy of the sequence (Zurek 1989a,b). Note that, it is customary to represent the entropy of an integer ‘‘n’’ simply as kðnÞ  lnðnÞ:

ð9:3Þ

If there are two sequences ‘‘s’’ and ‘‘t’’ then the following relations hold: kðs þ tÞ  kðsÞ þ kðtÞ if s > t such that s > t ; then

ð9:4Þ

kðs Þ > kðt Þ:

ð9:5Þ

Next, let us apply the Kolmogorov’s entropy concept to the following traffic situation on a free way. Imagine a single lane spatial segment of a freeway with a traffic sensor installed somewhere in the middle of this segment. Let the output of traffic sensor be fed into a processor (computer) for further analysis of traffic patterns, just as the outputs from all other sensors from different sections of a freeway road network are fed to the processor. At any instant the traffic sensor detects the presence or absence of a vehicle. Let the presence of a vehicle be coded as ‘‘1’’ and the absence as ‘‘0’’. If this spatial segment has a near free flow traffic, then we may observe a series of ‘‘1’’s and ‘‘0’’s such as the one shown below: . . . 0000000000110000100100001. . . 00001:

ð9:6Þ

The series in (9.6) shown above has no pattern and appears as a random sequence of zeros and ones. Next let us imagine extremely congested traffic on the same one lane link. One of the possible series of observations is given by: 1111111111111111111111 . . . 11111:

ð9:7Þ

The series of ‘‘1’’s is clearly not random. To describe the series in (9.7), all one needs to do is to count the number of ‘‘1’’s¼n1¼n, an integer number. On the other hand there is no way to describe the series in (9.6), but to reproduce the entire sequence as it is. To get maximum information from series in (9.7), all we need to know is the number ‘‘n’’, representing the number of ones, while for series in (9.6), the only way to gain information is to look at the entire sequence. The example above is analogous to the description of Kolmogorov randomness. To quote

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Chaitin, ‘‘. . .A series of numbers is random if the smallest algorithm capable of specifying it to a computer has about the same number of bits of information as the series itself’’ (Chaitin 1975). Thus the computer would process series in (9.7) in a single step, while the series shown in (9.6) would need as many steps as there are ones and zeros in the series. In fact, the series in (9.6) appears similar to the outcomes of a series of un-biased coin tosses, while series in (9.7) is similar to a biased coin toss that invariably produces a series of heads (ones). As was stated earlier, the traffic patterns have been described in terms of levels of service ‘‘A’’ through ‘‘F’’. Our aim here is to transform these into numeric patterns that could be used to compute degree of randomness based on Kolmogorov entropy. One of the ways this can be achieved is by devising transformation rules based on Table 9.1. 1. Given below is a set of transformation rules: Rule I a ! bc | cb Rule II b ! cd | dc Rule III c ! de | ed Rule IV d ! ef | fe Rule V af ! a, bf ! b, cf ! c, df ! d, ef ! e, ff ! f; where, ‘‘a’’, ‘‘b’’, ‘‘c’’, and ‘‘d’’, ‘‘e’’, ‘‘f’’ are service levels and ‘‘f’’ denotes a null character, and ‘‘|’’ and ‘‘!8’’ are meta-characters. Note that ‘‘a’’ is equivalent of combinations of either ‘‘b’’ and ‘‘c’’ or ‘‘c’’ and ‘‘b’’ (see Figs. 9.3 and 9.4 for details.) Similarly, ‘‘b’’ is equivalent to a combination of ‘‘c’’ and ‘‘d’’ and so on. The above rules are applied as follows, all occurrences of ‘‘a’’ are replaced by ‘‘bc’’ or ‘‘cd’’ (Rule I); all occurrences of ‘‘b’’ are replaced by ‘‘cd’’ or ‘‘dc’’ (Rule II) and so on. Further Rule V can be decomposed into two sub-rules based on the separation distance between vehicles in a lane. For example, level of service ‘‘e’’ means the separation distance between two cars in a lane is about 80 ft, equivalent of four cars’ lengths including headway. Thus ‘‘e’’ can be decomposed as follows: Rule V(1) e ! 100001 Similarly, a level of service ‘‘f’’ is equivalent of two vehicles separated by a distance of one car length plus the headway. Thus ‘‘f’’ can be decomposed as: Rule V(2) f ! 101 2. Next a series ‘‘W’’ of levels of service on a stretch of road can be represented numerically in terms of the length of such a series as: | W |, and for a series with no vehicles can be represented as | |=0. 3. Within a series ‘‘W’’, a frequency of service element such as ‘‘e’’ is given by |W|e. 4. If U and V are two elements on a stretch of road such that U6¼V then, UV¼VU; |U+V|¼|U|+|V|, and |U+V| f ¼|U| f +|V| f. Thus, the above transformation rules 1 through 4 can be used to describe a typical traffic pattern expressed in levels of service.

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Randomness and Order in Traffic Patterns

Next, let us consider an urban freeway with N links and L levels of service (LOS). The total number of possible traffic patterns in terms of LOS would be given by NL. For example, a freeway of N=10 links with L=6 would have 106 possible traffic patterns in terms of LOS. Let this 10 link freeway have a traffic pattern represented by LOS ‘‘A’’, i.e. AAAAAAAAAA:

ð9:8Þ

When expressed in terms of the formal traffic language as defined above, it would be equivalent to aaaaaaaaaa:

ð9:9Þ

Without loss of generality let us consider link 1, with LOS ‘‘A’’, and coded as ‘‘a’’ (see Table 9.1). Then using the production rules I–V successively we would get the following: A ¼ bcjcb ¼ efðcdcjdccÞjðdebjedbÞg;

ð9:10Þ

where, each of the terms inside the curly brackets can be further expanded by recursively applying rules I–V. An example of decomposition of ‘‘a’’ into its components is shown in Fig. 9.2. Although there are multiple ways of decomposing each of the LOS as shown in Figs. 9.3, 9.4 and 9.5, if each rule is applied to the left most symbol at a time, then the total number of possible combinations is given by jwjeþf ! je þ f j! ; qffi jej!  jf j! jwje !  jwjf !

Fig. 9.2 One of several ways of decompositions of ‘‘a’’ into its sub-parts

ð9:11Þ

9 Hidden Order in Traffic Flows Using Approximate Entropy: An Illustration efcc fecc ddec dedc dcde dced

dcc

efced feced

bc

dedc cdc

a cb

deb

eddc feeb efeb

ccd

effeeed efefeed

efdeed efeded efeefed fedeed fefeeed feeded feefeed feefeeef feeefed feefeefe feeefefe feeefeef

149

effeeeef effeeefe efefeeef efefeefe efeefeef efeefefe fefeeeef fefeeefe

decd edcd

Fig. 9.3 Partial decomposition of LOS ‘‘a’’

ded

cd edd b dc

efeef efefe feeef feefe efefe efeef eefef eeffe

100001,010,100001,100001,010

efefe efeef effee efefe

efed

efc

efde

100001,010,100001,010,100001

fefee fec

fede

feefe feefe

feed

feeef

Fig. 9.4 Partial decomposition of LOS ‘‘b’’

where ‘‘!’’ stands for the factorial, ‘‘w’’ is a combination, ‘‘e’’ and ‘‘f’’ are the levels of service, ‘‘q’’ represents the tiny number of combinations prohibited by the production rules, and from axiom four, | w |e is the frequency of ‘‘e’’ in ‘‘w’’. It is obvious that when ‘‘a’’ is decomposed using the rules of production, the number of possible combinations are many more than for ‘‘b’’, ‘‘c’’ and ‘‘d’’. In terms of number of possible combinations, the following relation holds a  b  c > d > e ¼ f:

ð9:12Þ

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e

e

100001

f

f

010

ef

100001,010

d fe

010,100001

efe

100001,010,100001

fee

010,100001,100001

de c

eef

100001,100001,010

efe

100001,010,100001

ed

cd d dc

Fig. 9.5 Partial decomposition of LOS ‘‘c’’

Specifically, we have the following relations: total number of combinations expressed in terms of service levels ‘‘e’’ and ‘‘f’’ for combined service level ‘‘a’’: ffi

je þ f j! 8! ¼ ¼ 56 jej!j f j! 5!3!

ð9:13Þ

ffi

je þ f j! 5! ¼ ¼ 10 jej!j f j! 3!2!

ð9:14Þ

combined service level ‘‘b’’:

combined service level ‘‘c’’: and je þ f j! 3! ¼ ¼3 jej!j f j! 2!1!

ð9:15Þ

je þ f j! 2! ¼ ¼2 jej!j f j! 1!1!

ð9:16Þ

combined service level ‘‘d’’:

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while level ‘‘e’’ is ‘‘e’’ and level ‘‘f’’ is ‘‘f’’. Note that, for each of these combinations, the neighboring links have their own set of combinations. Next let us consider a freeway consisting of ten links with traffic flow patterns under different conditions described by the LOS. For illustration purposes, let us assume that all ten links are of same length (about 500 ft.). Suppose that, each spatial segment has a free flow traffic pattern corresponding to LOS ‘‘A’’, which could be represented in terms of the separation distance corresponding to LOS ‘‘A’’, namely ten ‘‘a’’s, as a combination sequence ‘‘aaaaaaaaaa’’. Next we apply the production rules I–IV from axiom 2, to each link, then one of the possible combinations is given by RðAÞ ¼ efeffeeeffefeefeef . . . fefefefeef;

ð9:17Þ

where ‘‘f’’, is a null character that separates each link. Note that ‘‘f’’ does not contribute anything towards the free flow traffic pattern. Alternately, coding ‘‘e’’ as ‘‘100001’’ and ‘‘f’’ as ‘‘010’’ we get the following: RðAÞ ¼ 100001f 010f 100001f010f010f100001f100001f100001f 010f100001f 010f100001f100001f010f100001f100001f . . . : f100001f010f100001f010f100001f010f100001f100001f ð9:18Þ If we want to determine Kolmogorov’s entropy k for the sequence in (9.18), the best we could do is to rewrite the entire sequence as is. Hence from (9.2), k for such a sequence is just the length of the sequence, given by kðRðAÞÞ ¼ jRðAÞj ffi 80:

ð9:19Þ

When each of these ten links have patterns corresponding to LOS ‘‘D’’, then from the production rule IV, we could rewrite corresponding ‘‘d’’ as either ‘‘ef’’ of ‘‘fe’’. Rewriting ‘‘100001’’ for ‘‘e’’ and ‘‘010’’ for ‘‘f’’, one of the possible combinations (~12) is given by RðDÞ ¼ 100001f010f100001f010f100001f010f100001f010f 100001f010f . . . f100001f010f100001f010f100001f010f ð9:20Þ 100001f010f100001f010f: The best one can do to get a measure of Kolmogorov entropy ‘‘k’’ is to compute the length of the sequence, just as shown in (9.19). Of course, there is a possibility that all ten links will have exactly the same pattern only of ‘‘ef’’ (‘‘fe’’), an exceedingly small probability that such an event would occur.

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On the other hand we consider the following situation, where all ten links have traffic pattern corresponding to LOS ‘‘E’’, rewriting the pattern in terms of alphabet ‘‘e’’ we get the following: RðEÞ ¼ eeeeefeeeeef . . . feeeeef eeeeef

ð9:21Þ

Alternately, when ‘‘e’’ is replaced by ‘‘100001’’ we get RðEÞ ¼ 100001f100001f100001f100001f100001f100001f 100001f100001f100001f100001f . . . 100001f100001f

ð9:22Þ

100001f100001f100001f: The pattern in (9.22) can be described using (9.2) and (9.3) by its Kolmogorov entropy as kðRðEÞÞ ¼ lnð10Þ  kðeÞ ¼ lnð10Þ  j10001j;

ð9:23Þ

where the first term corresponds to the number of links and the second term corresponds to the number of bits needed to describe the pattern. Next, let us consider the situation when all ten links have a traffic pattern corresponding to LOS ‘‘F’’, which can be represented in terms of alphabet ‘‘f’’ as given below: RðFÞ ¼ fffffffffffffffffffffff ffffffffffffffffffffffff f . . . ffffffffffffffffffffffff f:

ð9:24Þ

Note that there can be 255 f’s corresponding to 20-foot separation distances between cars over one lane-mile. Relation (9.24) can be coded as ‘‘010’’ in place of ‘‘f’’ and we get RðFÞ ¼ 010f010f010f010f010f010f010f010f010f010f010ff010f010 f010f010f010f010f010f010f010f010f010f010f010f010f010 f010f010f010f010f010f010f010f010f010f010f010f . . . 010f 010f010f010f010f010f010f010f010f010f010f010f010f: ð9:25Þ Series R(F) can be described as ‘‘n’’ links with ‘‘010’’ pattern and hence the Kolmogorov entropy of the sequence using (9.2) and (9.3) is given by kðRðFÞÞ ¼ kðn  ðf ÞÞ ¼ lnðnÞ j010j:

ð9:26Þ

where, the first term refers to the log of the number of links and second to the number of bits needed to describe the traffic pattern of type ‘‘f’’. It is clear from (9.18)–(9.25), that although Kolmogorov’s entropy is high for LOS ‘‘A’’ through ‘‘D’’, it goes to a minimum when the traffic patterns correspond to LOS ‘‘E’’ and

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LOS ‘‘F’’. Alternately, LOS ‘‘F’’ has the least amount of randomness in traffic flow patterns, and hence has the maximum order resulting in maximum throughput. It is clear that Kolmogorov’s entropy measure ‘‘k’’ can be used to measure the hidden order in the traffic flows.

9.5

Approximate Entropy

Algorithmic entropy described above can be used to distinguish between regular sequences and those that are irregular. However, it may happen that, there can be a number of sequences with enough variations in 1 and 0s, but have the same value of algorithmic entropy (Pincus and Singer 1996, 1998). So, which of these sequences are more random or more irregular or have more algorithmic entropy? How does one measure the degree of randomness? Pincus (1991) offers a statistical tool called Approximate Entropy (ApEn) to help determine degree of randomness of sequences of arbitrary length. Here we provide a brief description of this measure. According to Pincus (1991), the following definitions are applicable to real number sequences. Given a time-series of data t(1), t(2), . . . ,. t(N) from measurements equally spaced in time, let x(i) and x(j) represent two subsequences, where xðiÞ ¼ ½tðiÞ; tði þ 1Þ; . . . ; tði þ m  1Þ

ð9:27Þ

xðjÞ ¼ ½tðjÞ; tðj þ 1Þ; . . . ; tðj þ m  1Þ

ð9:28Þ

such that m is positive integer and mN. Then the distance between x(i) and x(j) is defined as d½xðiÞ; xðjÞ ¼

max ðtði þ k  1Þ  tðj þ k  1ÞÞ:

k¼1;2;...;m

ð9:29Þ

Next, define, 1 Ym ðrÞ ¼ ð N  m þ 1Þ

Nmþ1 X

! log Pm i ðrÞ ;

ð9:30Þ

i

where r 0 pm i ðrÞ ¼

1 ½number of subsquences  ðN  m  1Þ jd ½xðiÞ; xðjÞ  r : ðN  m  1Þ ð9:31Þ

Now from (9.27)–(9.30), the approximate entropy is defined as

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when m 1:

ð9:32Þ

And by definition when m=0 ApEnð0; r; N ÞðSÞ ¼ Y1 ðrÞ;

ð9:33Þ

where r 0. It follows from the above definition that, when subsequences of length m and m+1 are similar (not random), i.e., the distance between x(i) and x(j) is smaller than r, then ApEn is small. When subsequences are dissimilar (random), the distance between subsequences x(i) and x(j) is large and therefore ApEn is large. Note that m specifies a sliding window that travels over the sequence and generates subsequences, which are tested against each other for similarity with the distance function (9.29), subject to the upper limit of value r. Thus, the value of ApEn indicates the regularity or randomness of a sequence. Pincus refers to ApEn as a measure of logarithmic frequency of subsequences (Pincus and Kalman 1997). Variations of above definitions are applied to binary sequences, when r [0, l] and r > ;

;

e ¼ f. . . ; eit ; . . .g0 ; a ¼ fa0 ; a1 ; ð1  a1 Þ; f; a3 g0 : ð12:13Þ

Using the above algebra, (12.14) or (12.15) is obtained (as to no differencing parameter for error term) Y  Xa  a2 ð1  LÞdG g ¼ ð1  ’LÞð1  LÞde e;

ð12:14Þ

h i ð1  ’LÞ1 ð1  LÞde ðY  XaÞ  a2 ð1  LÞde dG g ¼ e:

ð12:15Þ

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For the error term, e □ Nð0; s2 IÞ is assumed. Parameters are estimated by pseudo maximum likelihood estimation for time domain data series proposed by Chung and Baillie (1993) and Tanaka (1999), called the CSS estimator. In this study, the data set is composed of 46 time series for each prefecture. The log-likelihood function is shown in (12.16) log L ¼ 

n n 1 XX 2 log 2p  log s2e  2 e : 2 2 2se i t it

ð12:16Þ

By using estimated parameters, instantaneous marginal productivity and the sum of persistent marginal productivity of infrastructure are calculated in (12.17), and (12.18) X Yjt @Yt @Yit X @Yjt Yit ¼ þ ¼ a2 þf wij ; @Git @Git @G G G it it it j j j6¼i

m X @Yi;tþj j¼1

@Git

j6¼i

¼ a2

m X j¼1

GðdG þ jÞ Yi;tþj : Gðj þ 1ÞGðdG Þ Git

ð12:17Þ

ð12:18Þ

In (12.17), the first term on the right indicates productivity contribution to own region, while the second term is contribution to other regions. The truncation order m in (12.18) is exogenously set at parameter estimation.

12.2.3 Diagnostic Tests Since the CSS estimator is asymptotically consistent and asymptotically efficient, a likelihood ratio test based on Large-sample theory can be applied as a diagnostic test for parameter significance (Chung and Baillie 1993). Now the parameter vector €  ¼ ðb € ;    ; b € ;   Þ. The null and of the CSS estimator in (12.15) is denoted by b 1 i alternative hypothesis of likelihood test is in (12.19) 

H0 H0

€ ¼ 0; b i € 6¼ 0: b i

ð12:19Þ

The parameter vector of the CSS estimator estimated under the constraint € € ¼ 0 is denoted by b  . A statistics of likelihood ratio of objective parameter as b i i € is xi in (12.20) for b i

n h i h io € ; s € s €2 €2 Þ  ln Sðb xi ¼ 2 ln Sðb; i  Þ :

ð12:20Þ

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Since xi asymptotically converges to w2 ð1Þ distribution, the critical value to reject null hypothesis H0 is obtained in (12.21) xi  w2ð100aÞ% ð1Þ:

ð12:21Þ

As in (12.11), under the null hypothesis a2 (for infrastructure) should be considered simultaneously with estimates of dG , because these two parameters are dependent. Statistics for simultaneously testing a2 with dG are xG; dG , shown in (12.22), n h i h io € 2 € s € €2 Þ  ln Sðb  ; s Þ ; xG; dG ¼ 2 ln Sðb; G;dG

ð12:22Þ

€  where b G;dG is the parameter vector of CSS estimator under the constraint with a2 ¼ dG ¼ 0. xG; dG follows w2 ð1Þ distribution. In terms of longitudinal correlation, Durbin–Watson statistics (DW) are used to detect first order serial autocorrelation. Suppose ^eit are the residuals at region i and time t. Overall Durbin–Watson statistics are in (12.23a), and Regional DW statistics are in (12.23b). If positive first order autocorrelation remains in residuals then DWis close to 0, and in the case of no first order autocorrelation, DW is close to 2 PN PT t¼mþ1 DW ¼ 2  2 Pi¼1 N PT i¼1

^eit^ei;t1

t¼mþ1

PT DWi ¼ 2  2 Pt¼mþ1 T

ð^eit Þ2

^eit^ei;t1

t¼mþ1

ð^eit Þ2

:

;

ð12:23aÞ

ð12:23bÞ

When the residuals of cross sectional models show unspecified spatial correlation, the spatial correlation left in ^et can be tested by Moran’s I statistics (Moran 1948). Suppose ^et is a residual vector for each cross-section. Based on ^et ,IM is calculated by (12.24) IM ¼

^e0 t

1

0 2 ðW 0 ^e

 þ WÞ ^et ; et t^

ð12:24Þ

where, W is a spatial weight matrix. We assumed it by inverse distance matrix M identical which have wij s in (12.10). The standardized Moran’s I statistics I is defined as follows: I M ¼

  IM  E IM  1=2 ; Var IM

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where,

  trðUÞ ; E IM ¼ nk     trðUPW0 Þ þ tr U2 þ ½trðUÞ2   M 2 Var IM ¼  E I ; ð n  k Þ ð n  k þ 2Þ

ð12:25Þ

and n is a number of samples, k is a number of parameters, P(projection matrix) and M U are defined as P ¼ I  XðX0 XÞX0 ,U ¼ PW, respectively. It is known that I asymptotically follows the standardized normal distribution.

12.3

Empirical Measurement of Infrastructure Productivity

12.3.1 Data The privatization of Japan Railway companies (JRs), Nippon Telephone and Telecommunications (NTTs), and Japan Tobacco (JT) caused a disjoint at 1987 in the longitudinal statistics of infrastructure. The corrections in the data set of infrastructure and private capital are provided by Doi in order to remove the inconsistency before and after 1987 (2000). Both data were depreciated with the identical rule to infrastructure data. Gross Regional Product (GRP) and labor force are also estimated based on the national census. Input and output data are deflated at the 1995 price. These data sets were provided for each prefecture unit, from 1955 through 1999. In order to ameliorate the small sample problem, we pooled all the available data. Okinawa prefecture was excluded because of its distant location and the lack of data availability before 1972.

12.3.2 Results of Estimation In order to estimate (12.11), lag truncation order m needs to be set. After several trials, m is set at 10. Therefore, 34 cross-sections (from 1965 through 1998) and 46 prefectures data are pooled up to be used for parameter estimation. Then totally 1,568 observations are used. Previous studies in the ARFIMA model reported that setting time-trend parameters affect dG ; de and other structural parameters. Considering the economical situation of the data period, we set three durations with different time-trend parameters as follows: (1) 1965–1973 as a rapid economic growth term up to the oil shock in 1973; (2) 1974–1990 as a post oil shock term up to the end of the bubble economy in 1991; and (3) 1992–1998 as the post bubble economy. In the regional production function, time-trend parameter indicates an average growth of total factor products, or of technological innovation rate.

12

Infrastructure Productivity with a Long Persistent Effect

209

Table 12.1 shows the estimation results; the first two columns are the estimates of production function with long persistency and three deterministic trends, referred to as long memory model-1. The estimated value of infrastructure parameter is considerably small compared with that of conventional studies (0.2–0.3). Because the long persistent model measures persistent productivity and spatial spillover effect of infrastructure, the productivity index would separately appear in different terms. As alternate model specifications, two models are estimated. The middle two columns are the estimates of the model without long persistent terms for both as de ¼ dG ¼ 0, referred to as short memory model. The last two columns are the estimates of the model without long persistent term of infrastructure as dG ¼ 0, referred to as long persistent model-2. First, we compare the long memory model-1 with the short memory model. The adjusted R-squared coefficient is slightly high in the long persistent model. The critical values of Durbin–Watson statistics

Table 12.1 Parameter estimates of regional production function Variables Long persistent MA model Long persistent model-1 model-2 Estimates ChiEstimates ChiEstimates Chisquaredc squaredc squaredc a Labor 0.544** 24.9 0.488** 35.69 0.459** 62.69 0.456** 99.94 0.512** 138.45 0.541** 105.73 Private capitala 0.028** 13.36 0.149** 14.73 0.072** 10.73 Infrastructureb Spatial spillover of 0.030** 6.63 0.013** 1.13 0.022** 8.04 infrastructure Long persistency of 0.310** 13.36 – – – – infrastructure Long persistency of 0.438** 57.24 – – 0.487** 150.52 stochastic error MA(1) in stochastic error 0.753** 194.19 0.972** 370.25 0.734** 187.12 Deterministic trend 0.077** 35.78 0.008** 0.06 0.078** 48.64 (1965–1973) Deterministic trend 0.025** 9.57 (0.021) 1.47 0.027** 15.3 (1974–1991) ** Deterministic trend (0.016) 23.9 (0.051) 78.86 (0.014) 38.1 (1992–1998) ** ** ** Constant (2.612) 13.73 (0.320) 0.03 (2.516) 48.3 ** ** ** Variance 0.032 0.034 0.033 – No. of samples 1,564 1,564 1,564 0.999 0.998 0.993 Adjusted R2 REG statistics 1.716 3.935** 2.024* Durbin–Watson statistics 1.93 1.718** 1.924 a Parameters of private capital and labor are constrained by constant returns to scale b In (12.9), infrastructure parameter and long persistency parameter of infrastructure are jointly tested because they are nonlinearly dependent. Critical values of Chi-squared test are 5.99 (2 d.f., 5% significance), 9.21 (2 d.f., 1% significance) c ** Indicates null hypothesis of zero slope is rejected with 1% significance, and * indicates the null is rejected with 5% significance. Critical values of Chi-squared test are 3.84 (1 d.f., 5% significance), 6.63 (1 d.f., 1% significance)

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(1,564 samples, 1% significant level, 11 parameters) are 1.868–1.896 for ’ > 0 and 2.104–2.132 for ’ < 0, so that ’ > 0 is accepted if DW < 1:868, ’ ¼ 0 is accepted if 1:896 < DW < 2:104, and ’ < 0 is accepted if DW > 2:132, respectively. The statistic values calculated by (12.23a) indicate that ’ ¼ 0 is accepted for long persistent model-1, while ’ > 0 is accepted for the MA model. The prefecture’s Durbin–Watson statistics calculated by (12.23b) ranges between 1.576 and 2.189. The critical values of Durbin–Watson statistics (34 samples, 1% significant level, 11 parameters) are 0.610–2.160 for ’ > 0 and 1.840–3.390 for ’ < 0. Table 12.2 shows that only two prefectures can reject ’ < 0, but the others cannot test the autocorrelation. Figure 12.2 shows the standardized Moran’s I for each cross M section. The rejecting range of no spatial correlation is I > 1:96; therefore there is no significant spatial correlation for all cross-sections. Parameters of labor, private capital and infrastructure are significant in long persistent model-1. Compared with the MA model, the labor parameter is slightly large, and private capital parameter is slightly small due to the constant returns to scale constraint. The estimated infrastructure parameter is considerably small compared with that of conventional studies (0.2–0.3). Because the long persistent model measures persistent productivity and the spatial spillover effect of infrastructure, productivity index would separately appear in different terms. Three timetrend parameters in the long persistent model are significant, but in the MA model, only one parameter for 1991–1998 is significant. The spatial spillover parameter is significant in long persistent model-1, but not for the MA model. The MA parameter is significant for both models. Note that the MA parameter is smaller in long Table 12.2 Results of Durbin–Watson test f>0 f