The essential feature of industry clusters is the tight connections that bind certain fi rms and industries together in various aspects of common behavior (Bergman and Feser, 1999, p. 1). Regional clusters are concentrations of businesses that colocate because of trading (buyer–supplier) relation- ships and/or to share common factor markets (including infrastructure, knowledge resources and labor) and/or common goods markets. As suggested in the introduction, there is a subtle, but important, difference between static agglomeration economies and the more dynamic benefi ts that accrue to members of a cluster. Informal or formal institutions are often used to leverage the dynamic benefi ts within a cluster, namely, venues and opportunities for networking, information sharing, collaboration and social learning that aid and encourage continuous innovation, technology upgrading, extension and adaptation to new markets, and so on.
These defi nitions are unavoidably imprecise. What is meant by tight or colocated? Are colocated fi rms those in a submetro district, a broader metro area, the same state, the same region? Is there causality implied here? What if fi rms colocate but do not trade with each other? By the very nature of their colocation they share markets and local resources, but fi rms may not have chosen their location for the purpose of realizing economies of that sort. Do we only care if they could realize those economies, even if they selected their locations for other reasons? What types of interdependence are most important? If it is knowledge spillovers that we are interested in, by what conduits do they occur? Through conventional product chain ties or labor pools? Through memberships in industry associations or joint linkages with R&D performers such as universities? Are size and critical mass fundamental elements of a cluster and, if so, are rural clusters a theoretical impossibility?
These ambiguities manifest themselves in the operationalization of the concept. There is no single accepted method for identifying clusters. In some cases location quotients on individual industries are used, paradoxically
eliminating any notion of sectoral interdependence from the start. More sophisticated approaches rely on factor analyses of input–output or staffi ng patterns data or discriminant analysis of multiple variables measuring regional industrial characteristics (see DeBresson, 1996; Feser and Bergman, 2000; Hill and Brennan, 2000; Feser and Koo, 2001). The factor analysis approach tends to work better for understanding relationships among mature manufacturing businesses than for smaller businesses, especially those that are service rather than goods-oriented. When factor analysis is performed, the model generates loadings that provide valuable informa- tion, but the researcher still must interpret and name the factors. In some instances the loadings make sense, but in others there is often no apparent common feature among the elements. The subjectivity in the approach is no more apparent than when the analyst talks about rotating the factors in order to get loadings that are plausible. The lack of regional input–output and staffi ng patterns data also limits analysis of localized interdependencies, necessitating tenuous inferences based on national trends.
Emerging and potential clusters are often of greater interest to policy makers than the mature clusters that are the most easily measured. After all, many mature clusters are declining or stagnant and, therefore, not likely to add new jobs in the future. But nascent clusters may be too new or too small to appear in the data. In today’s high-tech, high-speed world, several years can be a lifetime. Policy makers also may be more interested in high-tech clusters, as opposed to clusters in general. High-tech businesses are more likely to provide good jobs. That introduces an additional judgment call:
how to defi ne high tech. Despite years of debate in the regional science, economics and planning literature, there is still no consensus about the proper defi nition of that term. (See, for example, Markusen and Glasmeier, 1986; Armington, 1987; Hecker, 1999: Luker and Lyons, 1997; Hadlock et al., 1991).
The challenges inherent in quantitative analysis of secondary data have led to the heavy reliance on interviews, focus groups and surveys of experts.
The gathering of expert opinion is the preferred approach of a growing consulting industry that specializes in cluster analysis. The goal is to draw useful information from industry leaders, economic development profes- sionals, chamber of commerce offi cials, state department of commerce personnel, the manufacturing extension service, academic economists, university scientists and others intimately familiar with the local economy.
Stough et al.’s assertion (1997, p. 2) that such experts ‘are the agents who know the region’s industries [best] in terms of basic practice, supply chains, current investment patterns and potential opportunities for new products’ is certainly true. Large quantities of subtle and contextual information about a region’s business and industry base can be gathered through such largely
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qualitative methods. Moreover primary data collection of some form is almost always necessary to evaluate adequately key supporting agencies and institutions for which there is often very little high-quality secondary information.
But there are serious risks to the expert-opinion method in cluster analysis as well. First, many such studies implicitly invoke ‘know it when you see it’ logic; because nothing is being measured in quantitative terms, it is easy to avoid clarifying concepts at the outset. Defi nitions are likely to shift as information is collected, seriously compromising the objectivity of the fi ndings. Second, experts, like anyone else, are prone to some response bias. Third, the administration of surveys and interviews to convenience samples, as has become the standard practice, very rarely generates data that are representative of the population of experts in a region. Analysts also have a strong incentive to survey and interview those who are receptive to the policy relevance of the study, thus limiting the range of perspectives gathered. Compounding the problem are differences in response rates.
Business offi cials are far less inclined to fi ll out surveys, grant interviews and attend focus groups. Thus the university and government sectors are usually much better represented than is industry. Finally, because gathering information from experts is time-intensive, comparatively few are consulted.
Given that even experts can be myopic in their knowledge of their region’s economy, small (and, as we have seen, unrepresentative) samples pose serious problems.
The fact is that neither quantitative nor qualitative methodologies in cluster analysis are without drawbacks. Quantitative studies have probably faced the greatest criticism, but that is largely because they are inherently more transparent. Identifying clusters via the analysis of secondary data requires clear defi nitions, indicators and decision rules. It is easy to identify weaknesses in specifi c indicators, existing data sources, industry classifi ca- tions schemes and statistical models. Qualitative studies, on the other hand, are rarely documented suffi ciently to permit a serious validity assessment. It is too easy to assume that experts know best, to trust one’s hunch, even in the face of obviously inadequate samples and response bias. The fact that convenience samples, drawn on the basis of an analyst’s own views of what is happening in a region, typically confi rm that analyst’s perspective should come as no surprise. Yet such confi rmation is invariably taken as an indicator of the plausibility of the results, not a whole lot different from rotating factors based on their plausibility. Qualitative analysis does indeed have the potential to solve some of the defi ciencies of quantitative approaches, but unfortunately that does not make its own defi ciencies go away. Likewise the results of quantitative cluster analysis can be seriously misleading if they
are interpreted improperly, assumed to be more authoritative than they are, or applied to policy without confi rmation via other methods.
In summary, cluster-based planning typically consists of a quantitative analysis of the way individual industries interact through trading, and a qualitative assessment of what industries seem to be emerging. The units of analysis are Standard Industrial Code (SIC) or North American Industrial Classification System (NAICS) groupings and, usually, metropolitan regions. Both the quantitative and qualitative approaches are subject to technical challenges. Most pertinent to this chapter, they fail to account for the growing inappropriateness of SIC/NAICS categories as meaningful aggregates of businesses, they treat regions as undifferentiated spatial units, where all constituent communities have the same workforce and infrastruc- ture capacity, and they do not address the growing need to coordinate among different cluster foci.