Adam Whittle is a postdoctoral research fellow in the Spatial Dynamics Lab at University College Dublin. He received his Ph.D. in economic geography from the School of Geography (UCD) in 2018 and since then has been employed on government-funded Science Foundation Ireland (SFI) project examining the relationship between scientific and technological knowledge for regional development.
The diversification of economic activities is considered a key driver of regional growth and economic prosperity. In this literature the concept of regional branching describes how the emergence of new economic activities can be understood as a function of the regions pre-existing knowledge base and relatedness structure (Boschma and Frenken, 2012). Following this line of argument, we argue that the network dimension of the regional branching thesis – which has been largely under-explored to date – can be enriched by incorporating the conceptual underpinnings of the buzz-pipeline debate (Bathelt et al., 2004). Recognizing that an upper echelon exists, limiting the extent to which a single region can rely solely on their internal technological structure, this synthesis pushes evolutionary discourse beyond a purely relatedness perspective and questions whether internal (local buzz) or external (global pipelines) connections matter more when diversifying into new areas of the knowledge space (Kogler, et al., 2013; 2017).
Taken together, technological relatedness has been shown to affect the scale and scope of knowledge spillovers within a region, whereas the buzz-pipeline debate emphasises the spatial configurations of knowledge creation in terms of local and nonlocal networks. Here, local connections (collaborations within the same region) promote learning opportunities through serendipitous encounters and the concentration of firms, while nonlocal connections (collaborations with other places) ensures that regions are kept up to date of ideas developed elsewhere.
While there seems general support for these claims, the way these have been addressed and verified throughout the relevant literature is concerning for two primary reasons. Firstly, while there has been increasing support to the claim that external linkages promote regional diversification through the introduction of new ideas, this has not been tested in any systematic way. Secondly, it remains to be seen at what level of technological relatedness do internal or external linkages matter more? For instance, if an emerging technology is loosely related to the existing regional knowledge base, are then internal linkages more important to ensure the growth of the infant technology sector? Conversely, when do external linkages matter more for entry into new technological domains? These questions are explored in turn.
Relatedness Density and External Internal (EI) Index
At the centre of our analysis are two indicators, one which measures relatedness and the other which measures network connections. In short, technological relatedness measures how close an emerging technology is to the existing regional knowledge base. The network dimension is captured using an External-Internal Index (Figure 1) which measures the relative density of internal connections of an entity (firm, organisation, region etc.) compared to the number of connections that entity has to the external world (Krackhardt and Stern, 1988). The EI index can take value from -1 (all connections are intern to the entity) to +1 (all connections are with external entities).
This index is in turn is applied to detailed information contained within patent documents, in particular inventor location and technological knowledge domain data. From there it is possible to first create regional knowledge spaces (Kogler et al., 2013) and thereafter to discern for each European NUTS2 region whether its inventors collaborated more frequently with inventors in the same region (local buzz) or with inventors in other regions (global pipelines) in the production of novel knowledge of economic value.
Figure 1. External Internal Index. Source: Authors
Deconstructing Technological Knowledge Entry
Figure 2 highlights the main findings of the study. Technological Relatedness (x-axis) indicates how close newly added knowledge domains are to the existing regional knowledge space, while the rate of entry in those domains is shown on the y-axis.
The rate of entry against technological relatedness (red line) is consistent with the regional branching literature, and confirms that new domains close to the existing regional knowledge space enter much more frequently than those that are distant. The probability of entry changes almost by an order of magnitude along the spectrum of weak to strong technological relatedness.
Figure 2. Probability of Technological Entry. Source: Authors
Turning to the network dimensions, we find that as technological relatedness increases so does the importance of external linkages (blue line) becoming more important for entry. This result suggest that external collaboration is especially efficient when the region has technological capacities to diversify into a related technology. In such cases, external knowledge can bring the final push for diversification.
A follow-up question that derives from the present analysis concerns sectoral differences. In this context the relevant literature provides evidence, anecdotal and otherwise, that certain types of industries rely more heavily on external sources of knowledge inputs than other sectors.
Figure 3. Internal-External Linkages and Aggregate Technology Classes. Source: Authors
Figure 3 aggregates the approximately 650 distinctive knowledge domains, i.e. four-digit CPC classes listed on patent documents, into 8 overall technology fields. While the evidence points to sectoral differences, the overall trend remains and external linkages do indeed increase the probability of entry into technology classes new to the region, something that is in particular evident at higher levels of relatedness. Latter finding provides an exciting insight for further studies as it is counterintuitive in the context of agglomeration economies and the spatial stickiness of specialized knowledge.
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Bathelt, H., Malmberg, A. and Maskell P. (2004) ‘Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation’, Progress in Human Geography, 28 (1), pp. 31-56.
Boschma, R. and Frenken, K. (2012) ‘Technological relatedness and regional branching’. In: Bathelt, H. Feldman, M. and Kogler, D. (eds.), Beyond Territory. Dynamic Geographies of Knowledge Creation, Diffusion and Innovation. London: Routledge, pp. 64-81.
Kogler, D., Rigby D. and Tucker, I. (2013) ‘Mapping Knowledge Space and Technological Relatedness in US Cities’. European Planning Studies, 21 (9), pp. 1374-1391.
Kogler, D. F., Essletzbichler, J. and Rigby, D. (2017), ‘The evolution of specialization in the EU15 knowledge space’, Journal of Economic Geography, 17 (2), pp. 345-373.
Krackhardt, D. Stern, R. (1988) ‘Informal Networks and Organizational Crises: An Experimental Simulation’ Social Psychology Quarterly, 51 (2), pp. 123-140