METROPOLITAN REGIONS AS A FACTOR SHAPING THE DYNAMICS OF COLLECTIVE
TECHNOLOGICAL KNOWLEDGE1
Pier Paolo Patrucco
Laboratorio di Economia dell’Innovazione ‘Franco Momigliano’
Dipartimento di Economia
Università di Torino
Via Po 53, 10124 Torino
Tel. +390116702767
Fax. +390116702762
pierpaolo.patrucco@unito.it
ABSTRACT. Collective technological knowledge is the result of systemic dynamics that make it
possible the access, accumulation and distribution of interdependent bits of localized technological
knowledge among complementary actors. Interactive behaviors and shared learning are the
determinants of such collective character highlighting the need for effective communication
opportunities and devices for the generation and diffusion of technological knowledge.
Technological communication relies upon favorable social and institutional conditions and emerges
as the crucial factor in the generation and distribution of localized technological knowledge.
Against the traditional argument of geographical proximity conceived in terms of physical
proximity that reduces physical costs (such as transport costs), it is argued that in metropolitan
regions the chances and actual implementation of technological communication find in social
proximity the suitable factor enhancing the quality of social and personal relations, hence low freeriding, reciprocity and thus repeated interactions based on trust. Therefore metropolitan regions
account for more reliable and valuable communication at the industrial and scientific levels that
favor the dynamics of collective knowledge. Providing a far more positive social and institutional
context making for the dynamics of collective technological knowledge, metropolitan regions
emerge as a key element in policy issues for the configuration of effective regional innovation
systems.
1. INTRODUCTION
The conditions of production, emergence and diffusion of technological knowledge is an object of
increasing attention in recent economic analysis where the systemic and localized character of the
dynamics of technological knowledge is appreciated as the result of industry-specific and regionspecific interactions among technological, institutional and social factors (Archibugi and Michie,
1997 and 1998; Clark et al., 2001; Swann et al., 1998; Storper, 1996 and 1997).
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I acknowledge the financial support of the Research Funds of the European Union Directorate for Research, within
the context of the Key Action ‘Improving the socio-economic knowledge base’ as a part of the project ‘Technological
Knowledge and Localised Learning: What Perspectives for a European Policy?’ carried on under the research contract
No. HPSE-CT2001- 00051. I wish also to thank Cristiano Antonelli for his useful insights.
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In this context, two fields of analysis might contribute the understanding of the production,
accumulation and diffusion of technological knowledge.
First, much empirical evidence gathered in regional and innovation economics has revealed that
innovation activity is strongly localized in well-defined technical and geographical spaces (Davies
and Weinstein, 1999; Jaffe and Trajtenberg, 1999; Jaffe et al., 1993; Paci and Usai, 2000; Patel,
1995). Technological knowledge is localized because of geographical factors since its production is
more and more concentrated in clear-cut and narrow regions. Moreover it is localized also because
of technical factors, since the specialization in a given technological field is the result of the specific
accumulation of complementary kinds of technical know-how stemming from different and yet
contiguous industries. The analysis has been especially focused on the role of proximity and
agglomeration in favoring knowledge spillovers and R&D externalities, underscoring the
indivisibility in the production of knowledge, the partial appropriability of knowledge and the
interdependences among different knowledge bases.
Second, the innovation systems approach has clarified the characteristics of technological
knowledge in terms of technological and institutional complementarities that impinge upon
common opportunities to generate and diffuse new bits of interdependent kinds of knowledge
(Edquist, 1997; Freeman, 1995; Lundvall, 1992; Nelson, 1993). This approach appreciated the
overall industrial and institutional context of economic systems as the relevant source of innovation.
The plurality of technological and industrial structures, combined with the presence of intermediary
institutions and knowledge infrastructures, enhances several interactive behaviors and learning
occasions among innovators with different kinds of competences and know-how. Here, the
production and diffusion of new knowledge is not only a matter of the linear application of
scientific or technical advances, such as those generated by universities and R&D laboratories, into
the specific productive process of the firms. As a matter of fact, the generation and transmission of
new knowledge involve the feedback among diverse kinds of knowledge owned by a variety of
actors, the integration of the results of their relevant utilization into interdependent economic
activities (e.g., academic research, R&D, production, consulting, technology-transfer, policy
making), and in turn the systemic and complementary recombination of such heterogeneous, i.e.
generic and idiosyncratic, kinds of knowledge.
The production and distribution of localized technological knowledge is now the complex result of
interdependences not only among specific industrial and geographical factors, but also among them
and institutional and technological ones. The localized character of technological knowledge is now
understood in terms of both technical and regional factors that co-define, technically and
geographically, the peculiar, technological and institutional, features affecting the systemic
generation and diffusion of the specific body of technological knowledge we finally observe.
In turn, agglomeration economies favor the accumulation of knowledge over time, and cumulative
effects in the generation and diffusion of knowledge, i.e., standing on the shoulders of giants, apply
to those locations that have higher concentrations of different supporting institutions (Feldman,
1994).
In this perspective, the role of metropolitan regions should be emphasized because they provide the
overall social and institutional endowment enabling the development of positive local dynamics
through which complementary kinds of knowledge are generated and shared. More precisely,
metropolitan regions account for institutional variety in terms of the mix of industrial, scientific and
technological infrastructures and in terms of systematic learning opportunities and communication
mechanisms. Such institutional variety finds in social proximity and the sharing of a common body
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of conventions, norms and practices the favorable conditions that make technological
communication effective and reliable.
Metropolitan regions in turn represent a key element in the definition of regional innovation
systems and thus a critical target for regions-oriented innovation policies.
The paper is structured as follows. Section 2 introduces the economics of collective technological
knowledge. The role of learning activities and communication processes is considered as crucial in
fostering the generation and distribution of complementary kinds of knowledge, and their merging
into a common body of knowledge. Section 3 presents a conceptual framework in which
geographical proximity is considered in terms of social proximity rather than in terms of physical
proximity. Social proximity facilitates the sharing of commons norms of interactions, therefore
more reliable and mutual social and personal relations, trust and thus repeated interactions that
favor the generation and transmission of collective knowledge. Section 4 concludes summing up the
results and the implications for a regional innovation systems perspective.
2. THE DYNAMICS OF COLLECTIVE TECHNOLOGICAL KNOWLEDGE
The spillover-based and the innovation system approaches appreciate the production and diffusion
of technological knowledge in terms of learning opportunities and interactive behaviors that relies
upon productive indivisibility and institutional complementarity, in turn representing two distinctive
and yet interdependent empirically based underpinnings to the understanding of the systemic
character of localized technological knowledge.
Firms are not merely involved in either internal R&D efforts or in user-producer and inter-firms
relations. The institutional context of economic systems in terms of the variety of knowledgeproducing institutions is the relevant source for the production and diffusion of technological
knowledge. For instance, user-producer interactions between small firms and multinationals play a
major role in enhancing the tradability of firm-based formal knowledge and technical know-how.
Moreover, the scientific and research community acts crucially to diffuse and share a plurality of
codified and science-based pieces of knowledge in complementary technological fields. Finally a
key role is also played by knowledge intensive business services (KIBS), such as consultants,
agencies for innovation and financial markets. KIBS are intermediary institutions within an array of
actors generating different knowledge bases and ensure the recombination of such diverse body of
knowledge.
In this perspective, technological knowledge shows clear features of a collective good from both the
structural and dynamic viewpoint.
From the viewpoint of the structural factors contributing to the collective character of technological
knowledge, three main knowledge bases might be clearly identified (Antonelli et al., 2002):
1. Internal knowledge, which relates to technological knowledge produced internally by each
firms. In that it relates to both tacit and codified knowledge, main sources are formal R&D
activities as well as internal learning mechanisms. Digital communications, e.g. Intranet, are
to be though of as key factors contributing to the codification of tacit knowledge;
2. External knowledge belongs to sources that are external to the firm but consistent with
productive systems where the firms play. I.e. inter-sectoral interdependences among local
industries, dynamics of market entry, market re-organization and labor mobility, interactions
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within the supply chain which can be enhanced by both production relations and
technology-enabled communication infrastructures;
3. External knowledge that pertains to external sources relating to the institutional environment
of local innovation systems. Universities and research centers, technology transfer centers,
business associations and knowledge intensive business services (KIBS) (i.e.
telecommunication services, business consultants, venture capitalists) are key knowledgeproducing and -recombining institutions.
From the dynamics viewpoint, technological knowledge is strongly affected by both horizontal and
vertical indivisibility and systematic cumulability among advances and increases made available in
different institutional and technological contexts (Latour, 1987; Stephan, 1996). Technological
knowledge is now seen as a coherent stock of fragmented pieces of information, embedded in a
number of productive and market conditions, and partially owned by a variety of economic agents
(Cohen and Levinthal, 1989; Malerba, 1992). Moreover, and more importantly, the process of
production and distribution of technological knowledge entails that learning efforts are needed to
accumulate and recombine such dispersed and complementary pieces of knowledge. Learning
efforts and interactive behaviors make it possible the access and use of such different and external
knowledge bases in contexts that are different from those in which they have been elaborated and
implemented (Loasby, 1999; Richardson, 1998).
The generation and diffusion of technological knowledge is now viewed as a process of
interpolating relationships among 1) firm-based learning and accumulation of internal tacit
knowledge, 2) intra-muros R&D activities which favor codified knowledge to be gathered, 3)
access to external tacit know-how and competence, 4) accumulation and recombination of existing
(internal and external) codified knowledge. In this systemic process each element is necessary
(Antonelli, 1999).
In this context, access conditions to existing external knowledge are key factors improving the
effectiveness and rate of knowledge production, enabling the acquisition and accumulation of
technological knowledge already stored but dispersed and fragmented in a number of artifacts,
technologies and users. Although, when technological knowledge is industry- and region-specific, it
is costly to use it elsewhere, i.e. in other regions, other industries and also other firms. Access
conditions are harmed by communication costs, that are costs necessary to search, store and decode
the relevant bits of knowledge owned by a certain different and complementary agent (Antonelli,
2001; Carter, 1989).
The basic trade off between codified and tacit knowledge clearly applies. The larger the codified
knowledge base incorporated in the stock of technological knowledge, the lower are the costs firms
have to copy with in order to screen the market, assess the relevant bits of knowledge and
complementarily accumulate and recombine them with the internal knowledge base. On the other
hand, the more idiosyncratic and hidden the content of external knowledge, the higher are the costs
to access a given amount of technological knowledge, and search, store and decode the relevant
complementary bits of knowledge. In turn the more tacit the knowledge and the stronger the need
for personal communication efforts and social interactions, while formalized and standardized
channels of communication are often less appropriate and reliable (Polanyi, 1958 and 1966).
In such a framework where the access, accumulation and recombination of knowledge are by no
means free and communication conditions are key factors explaining the effective opportunities to
access, understand and use an array of external knowledge bases, location has been seen as
conducive for lower costs in the communication and hence in the collective dynamics of
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technological knowledge. The externalities approach (Becattini, 1987 and 1989; Brusco, 1982) and
the transaction costs approach (Storper and Harrison, 1991; Harrison, 1992) respectively
acknowledged local economic spaces in terms of recursive exchanges of complementary know-how
and of trustworthy relations countervailing opportunistic behavior in such an exchange.
Agglomeration and geographical proximity provide the technical conditions for a recursive
exchange of complementary competences and knowledge among local actors.
Nevertheless, agglomeration is not sufficient per se to give place to technological communication.
The localized production and diffusion of technological knowledge is the result of the
interdependences between firms’ based tacit learning and the formalized acquisition of external
knowledge originated in both firms and institutions, which are fostered by the presence of multiple,
formal and informal, interactive mechanisms (Maskell, 2001; Maskell and Malmberg, 1999;
Maskell et al., 1998).
The construction of a network of dissimilar but complementary communicative relations based on
the variety of local economic systems favors the accumulation and recombination of different
knowledge bases and ensure the production of new technological knowledge (Patrucco, 2002a,b).
Economic systems are in turn conceived as communication networks where knowledge and
information are exchanged and where the social and economic structures and processes
characterizing economic systems are the channels through which knowledge flows (Hayek, 1945;
Lamberton, 1971, 1996 and 1997).
The opportunities for individual actors to affect and, at the same time, capitalize on the localized
base of technological knowledge rest crucially on the ability to implement and carry on effective
communication channels. The social and institutional endowment of economic systems in terms of
interaction opportunities and communication channels plays a major role in assessing the systemic
conditions under which collective leaning can take place and technological knowledge be generated
and distributed. Geographical and technical proximity, but also institutional and social proximity
favor the existence of common norms, formal rules and institutions that support interactive and
collective learning among complementary actors (Antonelli and Quéré, 2002).
These dynamics are especially evident in metropolitan regions such as technological districts, the
cité scientifique of Toulouse in France, Silicon Valley and Route 128 in the US, the financial
districts of New York and London, the knowledge complexes of Oxford and Cambridge in the UK,
and in Italy the Turin automobile district, the packaging and the ceramic tile districts of Bologna
and Modena, and the Brianza (Antonelli, 2000; Belussi, 2002; Dorfman, 1983; Enrietti and Bianchi,
2002; Patrucco, 2002a; Saxenian, 1994; Lawton-Smith et al., 1998; Storper, 1995; Russo, 1985).
3. GEOGRAPHICAL, SOCIAL PROXIMITY AND REPEATED INTERACTIONS IN
COLLECTIVE TECHNOLOGICAL KNOWLEDGE: THE CASE OF METROPOLITAN
REGIONS
Geographical proximity in metropolitan regions has been often conceived in terms of physical
proximity that reduces physical costs such as transport costs. Lower levels of transport costs are the
results of the physical proximity in narrow, and especially metropolitan, regions and are the key
determinants making for the spatial accumulation of technological knowledge (Krugman, 1991).
Nevertheless, the appreciation of the advantages of geographical proximity in terms of physical
proximity that reduce the costs of transportation of goods neglects a variety of economic, and
especially institutional and social factors that influence the dynamics of collective technological
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knowledge in particular, and the regional dynamics at large (Martin, 1999). The understanding of
these factors is most consistent with our systemic approach to the emergence of technological
knowledge, which underlines the interdependences among social, institutional and technological
factors in the collective generation and distribution of technological knowledge. Especially, it is
most pertinent to grasp the role of communication and interaction in building-up a collective pool of
technological knowledge in narrow regions that are characterized by a far more positive
institutional and social context, such as metropolitan regions.
In other words, geographical proximity is here conceived in terms of social proximity, which allows
the sharing of a common array of institutional infrastructures and social structure and norms (i.e.,
formal organizations, rules and practices, and informal customs, routines and norms), in turn
enhancing the quality of relationships among co-localized actors and allowing a collective
recombination and regeneration of individual knowledge (Amin and Thrift, 1992 and 1995).
Geographical factors are not per se a determinant of the dynamics of collective technological
knowledge but only because they account for the proper social and institutional conditions.
Technological knowledge can be exchanged because its production and distribution rely on a
territorial base of common social norms. These kinds of interactions are often informal and not fully
mediated by price mechanisms, but largely based on the connections of the societal structure and on
the personal communication of tacit knowledge. Localized technological knowledge emerges in
turn from the contextual set of social interactions determining the dynamics of collective learning
(Lawson, 1999).
Therefore, geographical proximity is not conceived as a mark of transportation costs but as a
measure of the quality of relationships among individuals. Knowledge is mostly embodied in
human beings and the specific set of institutional forms and social interactions arises as the crucial
arrangement for the governance of collective knowledge production and distribution (Metcalfe,
2001 and 2002).
In this perspective, the lower is the distance, the higher is the amount of social and institutional
factors that are shared among actors, the higher are the costs of free-riding in terms of bad
reputation, exclusion from the network and retaliation, the higher is the replicatibility of interactions
based on trust and mutual exchange of skills, competences and know-how. In other words,
geographical proximity could be also conceived in terms of the reciprocal reputation among
knowledge owners. Reciprocal reputation facilitates the search of the more viable source (either a
worker, a firm or an institutions) of complementary skills and competences, the transmission and
exchange of the bits of knowledge among such sources in terms of lower codification and
decodification efforts because of the sharing of the same base of language and communication
norms, and the reproducibility of such knowledge exchange over time.
When articulating the effects of social proximity in metropolitan regions on the quality of repeated
and knowledge-oriented interactions, the well-known Arrovian approach to the market failure in the
production of knowledge can be put in a new light by the appreciation of the spatial elements in the
transaction costs analysis. According to Arrow, the non-appropriability of knowledge and its nonrivality in use force market failures in the generation of knowledge that are due to the trade-off
between the social benefits of a public diffusion of knowledge and the private appropriation of
invention efforts. Free riding and opportunism could undermine the latter when the diffusion of
knowledge is public (Arrow, 1962 and 1969). High levels of transaction costs in the market
exchange are also due to the need of controlling the behaviors of eventual free riders and
opportunists. This explains vertical integration strategies in the firm and in-house creation of new
knowledge (Williamson, 1975, 1985 and 1996). Moreover, Coase (1960) stressed that common
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rules and governing structures establish the base for human interaction, giving it a certain degree of
predictability, certainty and hence replicability without new and higher transaction costs, in turn
also discouraging conducts (e.g., free-riding and opportunism) that violate shared norms and
behaviors.
Social proximity facilitate a collective, i.e., quasi-public or quasi-private, transmission of
knowledge where the exchanges are credible, loyal and replicable because of the sharing of the
same social norms, conventions and practices and because of higher costs in terms of social
exclusion, bad reputation and retaliation. Three implications could be developed.
First, labor mobility is not the direct effect of closer physical distance among places within the same
region. Metropolitan regions favor the mobility of human resources at large across the variety of
industries and firms because closer social distance and the sharing of the same institutional forms
(such as labor market rules and forms of industrial relations) and common practices are not a
constraint to labor mobility and thus to the mobility of embodied knowledge. In a Coasian
perspective, common ‘rules of the game’ define how labor markets work and ease the interactions
and transactions both within the same labor market and among different labor markets. Cities
provides low barriers to mobility of workers and human capital. Thus labor markets can play more
effectively, facilitating the access to such common pools and also the entry of new kinds of skills.
This causes lower levels in the costs of the mobility of human resources, in turn favoring the flow
of knowledge that is embodied in individuals and workers, the exchange of skills and know-how
among individuals and the creation of common pools of labor.
Urban and metropolitan environments make it possible for workers to access human capital by
imitating social models, learning by seeing and learning-on-the-task, in turn sharing their
competences. On the one hand, innovators and knowledge-producing institutions can acquire ideas
and knowledge by accessing this common pool of human capital. On the other hand, also new
individuals may enter metropolitan regions, at the same time accessing and innovating the flow of
know-how and hence the common pool of knowledge. The role of social structures and conventions
has been stressed in probing such collective modes of production and diffusion of new knowledge
(Glaeser, 2000; Jacobs, 1969; Storper and Salais, 1997).
Moreover, such labor mobility does not comprise only the workers of the firms but also the personal
relationships between academics, scholars and local firms. Social proximity in fact can account for
a better local valorization of the most excellent skills of each academic vintage. Finest academics
and scholars can provide fitting consultancy and research support for local firms, even benefiting
from close interaction with firms in terms of reputation, new chances and stimuli for academic
research, and also in terms of the diffusion of knowledge through the creation of students’ job
opportunities and placement on the local labor markets. At the same time, firms can benefit from
the access to new knowledge in the form of infrastructures (such as laboratories and databases) and
from the opportunity to temporarily post researchers and scientists in academic infrastructures,
establishing new chances for learning and research.
Building on such social conditions, metropolitan regions are loci where effectual industry-related
R&D infrastructures can be centered upon the academic system. In facts, the growing scientific and
technological contents of industrial production meets the increase in the provision of specific
knowledge services from the university to industry such as biotechnology, life sciences and
engineering-based sectors. University can contribute the knowledge outputs of industry providing
new inputs in three major ways. First, firms can receive new inputs in terms of codified knowledge
through individuals, both in the form of highly and formally educated human capitals and in the
form of scientists and senior researchers. Second, the academic system diffuses new knowledge that
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can be used in the industrial process of knowledge creation through publications. Third, cooperative R&D projects focused on the development of specific technological applications for
industrial needs are more and more characterized by the reliable presence of universities. Even in a
historical analysis, universities located in urban and metropolitan regions were the earliest
knowledge institutions to be involved in the technological needs of the industry, providing both the
foundations of a professional and managerial training and, subsequently, the industry-related
contents of research undertakings (Geuna, 1999).
More generally, in metropolitan regions local industrial dynamics and science-industry linkages are
major factors in the production and diffusion of technological knowledge also because they can take
advantages from the lower costs in the mobility of human capital and embodied knowledge, and
from the consequent presence of a common pool of labor, skills and knowledge which is available
to be accessed. When technological knowledge is embodied in human capital, metropolitan regions
can provide a favorable context for the transmission of knowledge through personal relations.
As far as industrial dynamics are taken into account, the entry of multinational corporations plays a
strategic role in this context, especially when considering the opportunity to establishing R&D and
productive interactions with local small and medium firms (Cantwell and Iammarino, 2000; Phelps
et al., 1998). Upstream and downstream user-producer relations (i.e. sub-contracting, provision and
purchase of specific and complementary intermediate inputs) are crucial elements in supporting the
generation and exchange of knowledge. Such production interdependences are crucial factors
enabling external knowledge, especially tacit knowledge, to be accessed and learnt (Lundvall, 1985;
Russo, 1985; Von Hippel, 1988). Within metropolitan regions, large firms can guide the generation
and diffusion of technological knowledge by means of networks of user-producer relations which
are implemented and coordinated by a central actor. Moreover, also the dynamics of entry based
upon the process of local spin-offs favor tacit knowledge to be pooled in a peculiar technical and
geographical space, hence accumulated and diffused. Generative relationships among local firms
leading to endogenous spin-offs are most important to grasp the dynamics of creation of a common
base of technological knowledge in well-defined geographical spaces (Belussi, 2002; Feldman,
2001; Russo, 2000).
When science-industry relations are conceived, the local concentration of technology centers and
R&D laboratories and of academic infrastructures that characterize urban and metropolitan regions
provides the suitable endowment to generate opportunities for co-localized firms to take advantage
from the diversity of science- and technology-based knowledge. The local diffusion of scientific
and technological complementary knowledge bases is more and more increased via the knowledge
externalities which stem from human capital in university and R&D laboratories, e.g. by means of
postgraduates, researchers mobility and personal contacts among them (Acs et al., 1998; Audretsch
and Stephan, 1996; Feldman and Audretsch, 1999; Quéré, 1994).
Second, lower transaction costs in the exchange of both embodied and disembodied knowledge are
also the result of co-localization. Utilizing the Williamson’s framework, transaction costs in the
production of technological knowledge are most of all costs that are due to the risk of free-riding
and opportunistic behavior in the market exchange. When these costs are higher than the costs of
organizing the production of new knowledge internally, the firm is the result of vertical integration
strategies, hence the proper governance mechanism for the organization of knowledge production
and distribution counterpoised to the market. Co-localization could be seen as an alternative
governance mode in the dynamics of technological knowledge because it provides the proper
environment in terms of trusted relations, transparency in economic behaviors and hence lower risks
of opportunistic behavior and information leakage. Social proximity accounts for the sharing of the
same social norms and conventions reducing the risks of leakage. Social proximity also allows for
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higher possibilities to discover eventual opportunism and free riding, signaling such behaviors in
terms of bad reputation, exclusion from social and economic relations, and retaliation. In this
perspective, co-localization and the collective technological knowledge emerges as a substitute
between the market’s and the firm’s modes to organize the production and diffusion of
technological knowledge. It also reconciles the Arrovian trade-off between the private incentive to
innovate, which derives from the private appropriation of the result of R&D efforts in terms of
economic returns, and the social welfare drawing from the public diffusion of new knowledge.
The implementation of local (quasi)markets for knowledge as viable devices in the generation and
distribution of technological knowledge can be centered upon these arguments, therefore finding in
metropolitan regions a far more suitable environment.
More precisely, the implementation of actual (quasi)markets for knowledge is the result of the
increasing specialization and division of labor in the production of knowledge and the development
of institutional devices that allow trustworthy knowledge exchange and repeated user-producer
interactions. Formal and informal institutional devices (such as, patents and informal agreements)
favor technological communication in that they provide a common definition of the codes, norms
and procedures by means of which interactive agents can articulate their relevant demand and
supply of problem solving capabilities, in turn making market transactions efficient and replicable
in the future. At the same time, long-term interactions positively affect the implementation of
institutional devices in that they favor the build-up of an environment of trust and confidence based
on common experiences. The overall cost of trading knowledge may be reduced of the cost of
eventual opportunistic behavior and excessive information leakage (Guilhon, 2001).
Knowledge intensive business services (KIBS) are major players in this context. Because
institutional devices reduce the costs of trade and exchange of disembodied pieces of knowledge in
the market place between the players of reiterated interactions, the markets for knowledge increase
the number of actual transactions between manufacturing firms, universities, specialized R&D
firms, consultants and KIBS at large. Lower transaction costs and the chance to appropriate rent
knowledge externalities drive economic actors to the outsourcing of specific pieces of knowledge,
creating a sort of intermediary market for knowledge producers (i.e., KIBS).
The transmission of technological knowledge in this perspective greatly benefits also from the
working of financial markets. Technological knowledge can be embedded into financial assets,
making the access to external knowledge possible. In this case, technological knowledge is not
disembodied and traded as such, but is embodied in the shares of a certain firm, which are
exchanged in the marketplace. This element reduces the risks of opportunistic behavior through the
active involvement of knowledge owners (e.g., entrepreneurs, investors, the management) in the
business enterprise. When financing new comers and innovators, financial markets, venture
capitalists, but also local banks screen the markets and select certain kinds of new ideas and
knowledge, contributing not only their growth but also their diffusion into the business community.
This implies that a positive evaluation on a specific body of knowledge embodied in the assets of a
certain firms has been expressed, making this knowledge valuable for perspective investors, in turn
also being the accumulation of that embodied knowledge easier and more reliable (Antonelli and
Quéré, 2002).
Third, the replicability of such knowledge exchanges and learning interactions over time is
consequently based on the shared set of social norms and on the trusted environment. Most
important, the replicability of interactions and exchanges assures reciprocity among actors and thus
mutual and reliable exchanges of information and knowledge. A network of trustworthy interactions
can be implemented also intensifying the strength and the density of the links when the number of
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repetitions increases and when new actors enter the flow of knowledge exchanges, for instance
because of low barriers to labor mobility and entry. The stronger and denser are the links and the
higher are the costs of opportunistic behaviors in terms of bad reputation, exclusion and finally in
terms of costs related to the acquisition, generation and transmission of knowledge. In facts, in this
context, repeated interactions are characterized by lower overall costs in the generation and
transmission of knowledge. These are due, on the one hand, to lower costs in the mobility of human
capital that embodies individual and yet shared knowledge because of the labor pool effect. This
causes lower costs in the transmission of knowledge because of the higher mobility of human
capital, but also lower costs in the acquisition of knowledge, mainly in terms of search costs,
because of the presence of a well-defined pool of skills that provides firms for a recognizable source
of knowledge embodied in individuals and workers. On the other hand, lower transaction costs in
the exchange of embodied and disembodied pieces of knowledge (that are due to trust and
mutualism) account for lower costs both in the provision and the acquisition of new knowledge.
The localized pattern of face-to-face communication and interaction mechanisms is again a key
issue when explaining the production and diffusion of new technological knowledge (Howells,
1996; Lundvall, 1999). In this perspective, the set of social mechanisms and institutions needs to be
taken into account when appreciating the local endowment of communication channels and
opportunities. It has been stressed that in metropolitan regions, technological knowledge is
developed by localized collective learning that takes place because of social institutions (Morgan,
1997; Storper, 1995). These are represented by a plurality of collective actors such as business
associations, chambers of commerce and the local polity community at large; but also by inherently
cultural and social conditions such as the sharing of the same social background and languages that
favor the existence of an environment of trust among firms and among them and institutions, and in
turn the habit of co-operation. Knowledge is generated and distributed because of the existence of
informal codes, shared norms, conventions and routines that enable the exchange and interpretation
of the body of knowledge which is transmitted, hence allowing for lower costs in the codification
and decodification of information about new products, technologies, best practices and
organizational ameliorations, and market conditions (Allen, 1983; Freeman, 1991; Richardson,
1972).
In sum, geographical proximity in metropolitan regions is not conceived in terms of physical
proximity that reduces physical costs (e.g., transportation costs). Rather, it is considered in terms of
social proximity that enhances the quality of social and personal relations, hence low free-riding,
reciprocity and thus repeated interactions that favor the generation and the exchange of collective
knowledge. Repeated and trustworthy interactions at the industrial and scientific levels are therefore
at the basis of the dynamics of collective technological knowledge in metropolitan regions.
This conceptual model is consistent with the classical analysis of Jane Jacobs (1961 and 1969) and
at the same time can put it in perspective. Inter-industrial transmission of technological knowledge
is difficult because of the highly idiosyncratic character of skills and competences. Knowledge is
more and more embodied in individuals, which are rooted in certain firms, industries and
geographical spaces, and whose skills and know-how are constrained by such technical and
geographical localization. Nevertheless, inter-industrial transmission of technological knowledge is
most important and requisite in the dynamics of collective technological knowledge. Knowledge
complementarity and fungibility play a major role here. As far as the former is concerned, the larger
the number of the bits of knowledge which can be recombined and the larger the chances of
generating new relevant knowledge. Knowledge fungibility is instead identified with respect to the
number of activities a given bit of knowledge can apply. The larger is the number of activities to
which the new knowledge can be applied and the larger are the advantages in terms of economies of
scope and internalization of externalities.
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Metropolitan regions offer the context and the opportunities to valorize the variety of bits of
knowledge, in turn making inter-industrial transmission of knowledge feasible. The presence of
different industries and workers, and thus the existence of horizontal differentiation, makes learning
and imitation among firms and formal exchange and informal barter of knowledge among
individuals more reliable. Moreover, information leakage and opportunistic behavior within the
same filière are limited as well. It has been argued that social proximity, the sharing of same
conventions, norms, codes and means of interactions facilitate the mobility of human capital, lower
transaction costs in the exchange of knowledge, and repeated interactions based on reciprocity and
mutual communication of knowledge. In turn, complementarity among different and yet
interdependent bits of knowledge can be better exploited generating a collective body of
technological knowledge. Since it encompasses and recombines different idiosyncratic skills and
competences, higher levels of fungibility are also achieved.
As far as the dynamics of collective technological knowledge is concerned, metropolitan region
appear therefore as superior to rural regions. Whereas in traditional vertical clusters and districts
located in rural regions the risks of leakage and opportunism might affect relations within the same
industry, urban and metropolitan regions provide a favorable location for the emergence of a multisectoral environment and a social context where actors can credibly and reliably exchange
information and know-how. The effective internalization of inter-industrial technical externalities is
the key determinant in the growth of a local common body of technological knowledge.
When the social conditions strengthening the generation and diffusion of technological knowledge
are identified together with the crucial role of variety, the characteristics of metropolitan regions
with respect to the quality of interactions among different actors and with respect to the
effectiveness of technological communication are noteworthy and become a necessary issue for the
regional and national policy maker. Urban and metropolitan regions account for the mix of variety
and complementarity of productive and market conditions, endowment of scientific and
technological infrastructures, and systematic communication mechanisms, in turn providing a far
more positive context explaining and fostering the collective dynamics of technological knowledge.
The appreciation of such an environment governing workable technological communication and the
effective internalization of local knowledge externalities can be a guideline for the policy makers.
In this perspective, metropolitan regions can be thought of as a key element for the configuration of
effective regional innovation systems, allowing for a better exploitation of the advantages of the
dynamics of collective technological knowledge. In turn, metropolitan regions should be taken into
account as a more desirable objective for public intervention in terms of public subsidies, and
institutional, technological and academic infrastructures.
3. CONCLUSIONS
Technological knowledge emerged as a collective good in that its production is the result of a
process that combines pieces of generic, scientific knowledge and specific, idiosyncratic
knowledge, which are owned by a variety of economic agents, and which are accessed, accumulated
and recombined through interpolating dynamics. Such a complementarity among the diverse
knowledge bases emphasizes the importance of interactions among different knowledge owners.
The proper knowledge-enhancing environment is made up by a communication network of SMEs
and client firms building user-producer relationships in a multi-technological industrial structure
and contributing each other to the internal, tacit and codified, knowledge base of productive
partners. In this context, universities and R&D institutions establish linkages with business firms
undertaking basic research efforts and providing the external codified knowledge base upon which
11
implementing firms’ internal knowledge; telecommunications, consultants and the financial sector
provide knowledge-based services for business, in turn favoring the transmission and the
recombination of knowledge, acting as interfaces between the scientific and codified knowledge
provided by institutional and business external sources, and internal tacit know-how and R&D
efforts.
In this perspective, the implications stemming from our conceptual framework are two-fold. First,
the relevance of the analysis of the collective technological knowledge stressing the
interdependences among institutional, technological, industrial and social factors found support.
Second, the crucial role of communication opportunities and communication channels is also
highlighted emerging as the intertwined effect of technical and social proximity. Such technical and
social proximity in turn accounts for the common endowment of technological and institutional
factors that foster the production and diffusion of complementary bits of knowledge. Since they
account for the complementary endowment of institutional, industrial, technological and social
factors that favor the build-up of explicit communication opportunities, metropolitan regions are a
determinant shaping the dynamics of collective technological knowledge.
According with this view, the framework provided in this paper is consistent with a notion of
regional and sub-regional innovation systems that stresses the internal set of interactions among
firms, and between them and institutions operating in the region. One where the institutional and
social structure of the region in terms of communication opportunities and communication channels
is the crucial factor favoring local learning and knowledge sharing, in turn determining the internal
dynamics of the production and distribution technological knowledge (Braczyk et al., 1998; Cooke,
2001; Cooke et al., 1997; Howells, 1999). In this context, metropolitan regions can be thought of as
informational entities that favor and speed the collective flow of learning and knowledge (Glaeser,
2000).
The implications for innovation policies are also relevant. In regional innovation systems where
learning is linked to the local social and institutional structure and requires a variety of means,
incentives and capabilities of individual, firms and systems to access and recombine external
knowledge, metropolitan regions are crucial elements. Learning represents the key process in the
dynamics of collective technological knowledge and it can be improved through the specific set of
social and institutional endowments. When considering that metropolitan regions account for the
variety of social conditions and institutions that enhance the production and distribution of
complementary kinds of knowledge, thus metropolitan regions emerge also as the focus for
innovation-oriented innovation policies.
Metropolitan regions emerge as valuable and operative governance structures of geographically
based innovation systems because they provide the rich set of institutions that are conducive to
implement effective formal and informal communication channels and learning devices fostering
the interaction and recombination of different specific practical skills, areas of competences and
scientific knowledge. When developing innovation policy, both at the national and the regional
level, policy makers should take into account metropolitan regions, when and where they present
such features, as viable targets for the allocation of resources and funds. At the same time, this
framework can provide the regional policy maker with a base for the implementation of the proper
social, institutional and industrial conditions that make for metropolitan regions as a valuable
environment for the effective exploitation of the dynamics of collective technological knowledge
and thus a reliable objective for region-oriented innovation policies. The concentration of
technological and scientific infrastructures and public subsidies in a few metropolitan regions might
be considered as an appropriate policy strategy to sustain the overall amount of technological
knowledge a region generates, in turn supporting the eventual rate of innovation.
12
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