5. Alternative Models of Industrial Clusters

While the agglomeration arguments of Glaeser and Krugman imply that there is an inherent geographical-structural weakness within the New Zealand economy, there are other models of industrial clustering and growth, which are rather more circumspect in terms of their perception of the critical spatial extent of information transactions, externalities and growth. While the new economic geography models of Krugman (1991) and the urban agglomeration models of Glaeser (1998) are based on the assumption that the individual urban area is the critical spatial extent which defines geographic advantage or disadvantage in growth performances, two other types of clustering-interaction models suggest that growth mechanisms may take place over rather different spatial and population scales. As such, these two other types of models may provide some opportunities for optimism on the part of New Zealand’s policy-makers, because they imply that the relationship between geography, trade and economic growth is rather more subtle than the simple Marshallian agglomeration model suggests.

These two other models are the ‘industrial complex model’ and the ‘social network model’, and they suggest that simple observations of the scale of urban population levels and industrial clustering will not necessarily be instructive as to the nature of localised growth mechanisms. In order to understand how the insights of these two additional models of clustering may be interpreted in the New Zealand context, we will first explain their particular foundations and transactions-costs characteristics in direct comparison to the agglomeration model outlined above.

In order to do this, we can adopt a transactions costs approach to present three stylised sets of geography-firm-industry organizational relationships (McCann and Gordon 2000; McCann 2001a; Simmie and Sennet 1999). The three stylised characterizations of industrial clusters are distinguished in terms of the nature of firms in the clusters, the nature of their relations, and transactions undertaken within the clusters. These three distinct types of industrial clusters can be termed the pure agglomeration, the industrial complex, and the social network. In reality, all spatial clusters or industrial concentrations will contain characteristics of one or more of these ideal types, although one type will tend to be dominant in each cluster. The characteristics of each of the cluster types are listed in Table 1, and as we see, the three ideal types of clusters are all quite different.

5.1 The pure agglomeration model

Firstly, in the model of pure agglomeration, inter-firm relations are inherently transient. Firms are essentially atomistic, in the sense of having no market power, and they will continuously change their relations with other firms and customers in response to market arbitrage opportunities, thereby leading to intense local competition. As such, there is no loyalty between firms, nor are any particular relations long-term. The external benefits of clustering accrue to all local firms simply by reason of their local presence. The cost of membership of this cluster is simply the local real estate market rent. There are no free riders, access to the cluster is open, and consequently it is the growth in the local real estate rents which is the indicator of the cluster’s performance. This idealised type is best represented by the Marshall (1920) model of agglomeration, as adopted by the new economic geography models (Krugman 1991; Fujita et al. 1999). The notion of space in these models is essentially urban space, in that this type of clustering only exists within individual cities.

5.2 The industrial complex model

Secondly, the industrial complex is characterised primarily by long-term stable and predictable relations between the firms in the cluster, involving frequent transactions. This type of cluster is most commonly observed in industries such a steel and chemicals, and is the type of spatial cluster typically discussed by classical (Weber 1909) and neo-classical (Moses 1958) location-production models, representing a fusion of locational analysis with input-output analysis (Isard and Kuenne 1953). Component firms within the spatial grouping each undertake significant long term investments, particularly in terms of physical capital and local real estate, in order to become part of the grouping. Access to the group is therefore severely restricted both by high entry and exit costs, and the rationale for spatial clustering in these types of industries is that proximity is required primarily in order to minimise inter-firm transport transactions costs. Rental appreciation is not a feature of the cluster, because the land which has already been purchased by the firms is not for sale. The notion of space in the industrial complex is local, but not necessarily urban, and may extend across a sub-national regional level. In other words, these types of complexes can exist either within or far beyond the boundaries of an individual city, and depend crucially on transportation costs.

5.3 The social network model

The third type of spatial industrial cluster is the social network model. This is associated primarily with the work of Granovetter (1973), and is a response to the hierarchies model of Williamson (1975). The social network model argues that mutual trust relations between key decision making agents in different organisations may be at least as important as decision-making hierarchies within individual organisations. These trust relations will be manifested by a variety of features, such as joint lobbying, joint ventures, informal alliances, and reciprocal arrangements regarding trading relationships. However, the central feature of such trust relations is an absence of opportunism, in that individual firms will not fear reprisals after any reorganisation of inter-firm relations.

Trust relations between key decision-makers in different firms are assumed to reduce inter-firm transactions costs, because when such trust-based relations exist, firms do not face the problems of opportunism. As such, these trust relations circumvent many of the information issues raised by the markets and hierarchies dichotomy (Williamson 1975). Where such relations exist, the predictability associated with mutual non-opportunistic trust relations, can therefore partially substitute for the disadvantages associated geographic peripherality. Inter-firm cooperative relations may therefore differ significantly from the organisational boundaries associated with individual firms, and these relations may be continually reconstituted. All of these behavioural features rely on a common culture of mutual trust, the development of which depends largely on a shared history and experience of the decision-making agents.

This social network model is essentially aspatial, but from the point of view of geography, it can be argued that spatial proximity will tend to foster such trust relations over a long time-period, thereby leading to a local business environment of confidence, risk-taking and cooperation. Spatial proximity is thus necessary, but not sufficient to acquire access to the network. As such, membership of the network is only partially open, in that local rental payments will not guarantee access, although they will improve the chances of access. In this social network model space is therefore once again local, as with the complex, but not necessarily urban, and often extends over a sub-national regional level. Once again, in this case, both information transactions costs and transportation costs may play a role in determining the importance of geographical peripherality.

The major geographical manifestation of the social network is the so-called ‘new industrial areas’ model (Scott 1988), which has been used to describe the characteristics and long-term growth performance of areas such as the Emilia-Romagna region of Italy (Piore and Sabel 1984; Scott 1988), or to a lesser extent Silicon Valley in California. The Emilia-Romagna region has large networks of primarily small firms which are tied together by close personal ties. The trust networks evident between the firms allow the firms to arrange cooperative syndicates for certain types of activities, such that longer-term and more comprehensive investment programmes can be undertaken by the small firms than would be the case in an orthodox market mechanism. The result has been a continuous upgrading in the technology of the firms from traditional craft-based leather-goods activities to currently very high levels of technological inputs. There has also been some evidence of similar trust networks developing in the case of Silicon valley in California (Saxenian 1994), although this particular cluster appears to be primarily something akin to a pure agglomeration model (Arita and McCann 2000).

Meanwhile, the clustering model of Porter (1990, 1998) can also be argued to fit into this social network category. Although Porter assumes that the dominant competitive effects of clustering are mediated by information flows between firms and individuals within the urban sphere, the primary effect of which is to stimulate local competition by increasing the transparency associated with competitive improvements, he also acknowledges that such information flows may also extend well beyond the urban scale in situations where trust exists.

Both the industrial clustering model of Porter (1990, 1998) and the ‘new industrial areas’ model of Scott (1988), are therefore much less specific than the urban agglomeration about the particular spatial dimension which is critical in terms of information transactions costs. In cases where there are small-firm industrial structures, the spatial extent over which such trust relations operate will tend to be over small sub-national regional scales (Scott 1988; Porter 1990). On the other hand, in industrial structures characterized by large vertically-integrated firms, such trust relations may operate over much larger regional spatial scales, and in the case of contiguous small-area nations, these regional scales may extend beyond the individual country boundaries (Casson and McCann 1999). Where industrial structures are characterised by both small and large firm networks, such long-term trust relations can exist over national spatial scales.

There is some empirical evidence which supports these various arguments. Observations of the formal inter-firm outcomes of informal information exchanges (Arita and McCann 2000; Audresch and Feldman 1996; Suarez-Villa and Walrod 1997), technology spillovers (Cantwell and Iammarino 2000) or the spatial patterns of joint-lobbying activities (Bennett 1998), suggest that the spatial extent of such long-term inter-firm networks may be much greater than that of a single city, and may extend across whole national or sub-national regional areas. These various arguments suggest that the critical spatial areas which define geographic growth advantage or disadvantage, may be far larger than any of the Marshall, Glaeser or Krugman arguments imply.