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Industrial and Regional Clusters: Concepts and Comparative Applications
Edward M. Bergman and Edward J. Feser

4.1 Introduction

The policy literatures and regional development conference buzzwords resonate with affirmations of how and why industrial or business clusters are relevant to understanding nearly every regional development issue. Industrial clusters are seen as permitting possibilities of linking together several strands of regional policy interest into a single framework: technology, regional productivity advantages, growing vs. declining sector balancing, etc. At the same time, academic conferences and journals flourish with newly found or refashioned evidence of clustering behavior in bewildering varieties and regional contexts. Chapter 2 describes the complexity and currency of these debates. Even after accounting for the inevitable half-life to which every emergent approach to regional development is subject, it is clear that industry cluster concepts are likely to survive in some recognizable form for a considerable time, and for good reasons.

Reasons begin, but do not end, with scholarly ambitions to reconcile and possibly integrate a wide array of existing regional development theories, using industry cluster concepts as a unifying theme. Regional scientists or other academic investigators base their cluster studies on testing and refining more robust conceptual regional development frameworks and theories of the type reviewed earlier.

At the same time, there is keen interest to adopt policies and approaches that give advantage to ‘competitive clusters,’ which is the main interest of firms or industry groups seeking competitive advantages, and is often the interest of host regions. While the competitiveness of local sectors is an important objective when facing global markets and widely traded goods, the hallmark of regional policymaking is a balancing of regional interests, mobilizing all potential resources, marshaling consensus, offsetting economic losses with gains, building on past assets to seize future opportunities, and so on. This requires knowledge of more than which clusters and cluster fragments seek advantage in a region.

Fortunately, industry cluster concepts are sufficiently well-understood among public and private sector members of regional development partnerships that possibilities of regional policy implementation are much enhanced. Common nomenclature, similar concepts, and decision implications are even more familiar to all decision sectors if framed in terms of value-chain clusters at the regional level. This is no small advantage when joint investment decisions must be coordinated, when new policies are under active consideration, or when existing approaches deserve reconsideration in light of ‘seeing regional economies whole.’ ¹

Seeing regional economies whole is perhaps one of the greatest advantages permitted by use of regional value-chain clusters, which is an approach that cannot be supported with micro-based cluster studies. A regional mapping of the economy’s many interrelated sectors offers strong visual reinforcement of existing and possible connections affected by the local mix of policies and practices. These interrelationships also help understand how a region’s key sectors and clusters are linked to internal and external threats and opportunities, or how they are mutually buffered and advantaged by the region’s unique portfolio of assets.

We argue that the prevailing micro approaches to industry cluster analysis are based heavily upon the apparent needs of specific firms and industries. These needs may partially overlap the differing needs of other sectors for which regional development officials are responsible, but these interests of firms may more frequently differ markedly for many of the reasons already mentioned. Depending upon the client who commissions the cluster analysis [link to Appendix 1], there is a discernible bias toward what precisely is examined, how studies should be organized and conducted, and the range of possible uses to which the results can be applied.

This section will offer a sampling of several applications of industry clusters drawn mainly from the authors’ expansions of the approach outlined in earlier passages and subsections. The risk of immodest self-reference is taken in the interests of more certain knowledge of the details and documentation of most salient applications now available using this approach. At the same time, efforts are made here to incorporate or compare others’ work where similarity of application or sufficient detail of results permit. This is perhaps the section that is most subject to amendment and expansion in future editions. If so, it may also stimulate valuable documentation of analytic approaches underlying new applications that would further enrich the previous sections. Discussions will draw upon extensive sources for some applications in the text, while other more tightly focused issues might be treated entirely within sidebars.

The discussion will be organized in two major subsections. The analytic uses of value-chain clusters at state and regional levels will be discussed in Section 4.2. Much of this analytic potential is based on ‘sector-cluster taxonomies,’ which result from the value-chain partitioning of all pre-classified (SIC or I/0-based) sectors of an economy in ways that reveal their interdependent structure. The taxonomies are of inherent interest in terms of their methodological derivation, as explored in the previous section. This section explores their principal utility as an ‘open application architecture’ that permits various tests of propositions or concepts.

We will first illustrate this application potential for research in general, and then demonstrate specifically how spatial proximity can be shown to differ for several clusters in a given economy or how taxonomies of similar clusters might be tested in ways that reveal the effects of alternate methods of study or country of application.

Second, we will discuss and illustrate in Section 4.3 how the partitioned taxonomies might be viewed as ‘cluster templates,’ which are more stylized and accessible policy frameworks for applying value-chain clusters to development issues. Our experience indicates that cluster templates are more useful way of evaluating potential regional policy applications, than by expressing equivalent concepts with tabular data or symbolic abstractions. Cluster templates also reveal more of the implicit meaning inherent in performance measures available from regionally specific structural and dynamic micro data. Implicit meanings and development potentials drawn from template-framed data are further enhanced when visualized through graphic or mapping techniques that reflect the organizing properties embedded in the idea of templates.

4.2 Cluster Taxonomy Research and Analysis Applications

Cluster taxonomies partition detailed sectors for which widely available data can be aggregated and analyzed from a value-chain logic. Each cluster identified by the overall taxonomy consists mainly of primary sectors that trade among themselves far more than with others. Such primary trading groups could be termed a ‘clique’ by directed graph theorists who study innovation clusters, while clusters involving secondary sectors that trade at lower frequencies link them and primary sectors together in a series of interesting configurations: ‘non-standard cycles,’ ‘technological complexes,’ and ‘simple agglomerations’ (Debresson, Sirilli, et. al., 1996, p. 170-172). Whatever the configuration a particular cluster may take, the vast majority of industrial production occurs within one or more of them. Thus, most national or regional production data can be analyzed for meaningful groups of linked sectors that have been distinguished elsewhere as industrial value-chain clusters.

Partitioning a production economy into distinct groups of logically linked clusters provides an additionally useful conceptual taxonomy generally absent from SIC or other sectoral classifications. The general value of this taxonomy can be appreciated by demonstrating how industrial value-chain clusters yield quite different interpretations of the strength and complexity of North Carolina’s industrial base, particularly its motor vehicle industry (Feser and Bergman, 1999).

Traditionally measured,² the North Carolina manufacturing economy appears dominated by textiles and tobacco, followed by smaller but significant concentrations of activity in furniture, apparel, and heavy industrial machinery. Rounding out the top five manufacturing industries in employment terms are furniture, apparel, industrial machinery, and electronic equipment. While pharmaceuticals and industrial chemicals sectors are growing rapidly, they still constitute less than five percent of total manufacturing employment. Exhibit 4.1 illustrates the composition--using standard industrial categories--of the state’s manufacturing sector in terms of value-added and employment.3

However, Exhibit 4.2 reveals a very different picture of the NC manufacturing after its detailed sectors are re-partitioned into the value-chain cluster taxonomy. For example, when transportation equipment is identified by its most general Standard Industrial Classification (37), it appears relatively inconsequential to the economy, since it accounts for less than 3 percent of manufacturing value-added and employment (see Exhibit 4.1). After assigning industries to their value-chain cluster and re-calculating the aggregate figures, our new taxonomy puts metalworking, chemicals and rubber, and vehicle manufacturing among the largest clusters in North Carolina, next to knitted goods and fabricated textile products. Only by considering the many industries that typically supply transportation equipment manufacturers does the potential significance of the vehicle industry for the state’s economy become apparent. In terms of primary cluster industries only, the vehicle manufacturing input-output chain accounted for 15 percent of total North Carolina manufacturing employment in 1994. Together, manufacturers associated with the vehicle manufacturing and knitted goods clusters account for 37 percent of statewide manufacturing employment in 1994.

As this simple comparison clearly illustrates, the availability of taxonomically rigorous value-chain clusters permits one to re-frame research and analytic approaches, including the consideration of more complicated questions by such means as pre-sorting available secondary data into variables or specifying cases for further analysis. Two additional examples discussed below illustrate the potential of the taxonomy: the first tests the utility of using similar or identical cluster taxonomies to characterize restructuring underway in economies of widely varying countries and regions, and the second examines relative spatial distributions of firms according to their taxonomic cluster membership.

4.2.1 International Comparison of Value-chain Clusters

Work presently underway within OECD’s cluster working group to apply a common value-chain cluster estimating procedure to several member countries will provide a commonly-available means of refining procedures and comparing cross-country results (Roelandt and den Hertog, 1999). Similarly, another study presently underway will apply to the newly released Austrian I/O table the value-chain clustering techniques used and reported by the authors in this monograph, in addition to analytic procedures being tested by OECD team (Bergman, Maier, and Lehner, 1998-99). In the absence of results from these more ambitious comparisons, we illustrate below an international comparison by applying a single value-chain cluster based on the U.S. taxonomy to two U.S. and Austrian regions.

In this instance, the comparison is used to illustrate restructuring dynamics for the same cluster. The comparison is somewhat complicated because the original industrial classifications of the countries differ: two sets of concordances were used to convert sectoral data originally recorded under two co-existing Austrian classifications (ÖNACE and BS68) to the U.S. SICs in which industry cluster taxonomies were originally expressed (Bergman and Lehner, 1998b). Complications also arise because the comparison includes two different time periods during which clusters were observed to have restructured.

Since the basic value-chain cluster definition was derived from I/O trading behavior within U.S. industrial value-chain clusters of North Carolina, we selected its largest region. The ‘Carolinas’ region is so-named because it borders and influences heavily its South Carolina neighbor; it is home to several small cities and the city of Charlotte, North Carolina’s largest city, which is now one of the nation’s largest financial centers, although the regional economy was historically based upon apparel, textiles and furniture production, and still reveals strong concentrations in these clusters. The other region is Upper Austria, home to many small cities and the Danube-straddling city of Linz, one of the country’s inter-war centers of heavy industry and manufacturing, although furniture, textiles, ceramics and other industry clusters are also present in the region.

Choice of these regions has two implications that require immediate comment. First is data availability. The single consistent measure of sectoral activity available in both regions is employment, although not for consistent periods. This is less a problem for our attempt to illustrate restructuring than it first appears, since the 1981-91 period for Austria captures quite well a significant period in which the country steadily shed its state-sectors, opened more of its industries to privatization and global trade, and began large cross-border investments permitted by the 1989 opening of the east. This decade included dramatic adjustments in the industrial reorganization of production and the internationalization of investment.

For North Carolina, a more recent five year period from 1989-94 was possible to select, during which the economy began its post-recession (and post-restructuring) boom that has continued to propel many of its remaining core industries to new heights. This was also a period following which a substantial share of motor vehicle production had consolidated in the mid-South along its key transport corridors shared by North Carolina, including the recent BMW investments just inside South Carolina’s border. In 1994, about 60,000 of a total 400,000 manufacturing employees were counted in the Carolinas region’s vehicle manufacturing cluster, thereby accounting for some 15 percent of regional manufacturing employment. In contrast, about 58,000 worked during 1991 in the same cluster of Upper Austria, which comprised some 11 percent of its total regional employment (nearly 508,000).

The second implication is largely technical: cluster definitions and NC data are re-partitioned SIC sectors, which must be concorded to permit the use of sectoral data organized according to the Austrian industrial classification systems. As in North America, Europeans are now harmonizing their common industrial classification system among all continental trading partners, although an earlier Austrian classification system applies to information collected for these particular dates.

As a consequence, a considerable amount of cross-coding from industrial concordances was necessary, and this resulted in a slightly lower overall resolution of industrial detail for our comparisons, simply because certain sectors lack one-to-one correspondence in both classifications. The full task of concordance revision undertaken here is onerous and will be unnecessary for future data classified according to ÖNACE, so only one familiar industrial value-chain cluster was selected with which to illustrate our templates: motor vehicles.

Upper Austria lost about 1 percent of its vehicle manufacturing cluster employment in the full 1981-91 decade, while the Carolinas region cluster gained at about 1 percent over the shorter, more recent half-decade period. While the Carolinas regional cluster expanded and Upper Austria contracted in roughly similar proportions, coefficients of sectoral variation within the motor vehicle clusters of both regions increased by some 10 percent, leaving the Carolinas region with slightly more sectoral variation (1.62 in ’94 vs. 1.34 in ’91).⁴

Cluster graphics are organized in this illustration by declining size of sectoral employment (see Exhibits 4.3 and 4.4). The largest sector of the Carolinas’ motor vehicle cluster is 3714 (motor vehicle parts and accessories), while the largest sector in Upper Austria is sector 3711 (motor vehicles and car bodies). As both regional templates indicate, the remaining sectors drop off dramatically in size and number, and large portions of the total cluster graphic of both regions are totally uninhabited. Sectoral representation of Upper Austria’s cluster might be somewhat affected by concordance artifacts that arose when translating two national industrial classification schemes, but it is far likelier that our depiction is generally accurate in both regions, particularly their depiction of heavy concentrations in very few sectors, a minor presence in several, and absence of many others.

Upper Austria lost significant employment shares in the vehicle manufacturing sector (SIC 3711, 3716) and engine components (carburetors, pistons, rings, valves: SIC3952), whereas its vehicle parts and accessories (3714) production gained employment.⁵ In the Carolinas Region, the sectors most closely tied to this cluster grew strongest from 1989 to 1994, including the secondary sectors producing technology and equipment (welding and soldering equipment, machine tools, and metal cutting: SICs 3548, 3541) used in vehicle and parts production.

The clusters differ quite obviously in their composition, and their host regions differ markedly in overall economic structure as well. But the regional templates yield even stronger hints about the formation processes taking place within each region. The Carolinas region template indicates that its vehicle manufacturing cluster is expanding in nearly all its 1989 sectors, with more absolute growth in the largest. Its vehicle manufacturing cluster seems to have reached an optimal growth composition in ’89 and expanded in the following five years along, perhaps, an increasing returns trajectory.

The template suggests a quite different growth process for Upper Austria: sectors described above expanded dramatically, while others, even very large sectors, contracted equally dramatically. Both interpretations offered earlier imply considerable restructuring underway in Upper Austria’s motor vehicle cluster over the ten year period. It is possible that Upper Austria’s remaining cluster segments may repeat in the next decade some version of the story told from by the Carolinas’ changing regional template, particularly if the remaining sectors are well niched into its regional economy in ways that permit them to cross-trade competitively with EU and other regions to the east, yet produce efficiently in Upper Austria.

This illustration demonstrates the potential for applying commonly defined cluster taxonomies to very different regions as a means of comparing their underlying processes. This implies that an OECD or EU-based set of cluster taxonomies might become a very valuable analytic tool, particularly when applied to smaller, open economies that host only some of all potential sectors associated with a full value-chain cluster.

4.2.2 Spatial Bunching in Cluster vs. All other Firms

Can cluster member firms be convincingly shown to ‘bunch’ together more (or less) tightly than they bunch together with average firms (Bergman and Feser, 1999) Clusters based on value- or supplier-chain criteria may consist of firms whose trading behavior is accompanied by co-locational tendencies, perhaps due to JIT transactions or to capture technological and other spillovers present in the same region. This is an interesting question, since it asks whether firms that are ‘close’ in their value-chains are also located physically close in space, a question that requires evidence of both kinds of proximity and a method by which to make the comparison. In short, how closely located are the value-chain cluster firms?

Results from industrial value-chain cluster analyses are the base that permit a ‘spatial-economic test’ of this question (Feser and Sweeney, 1999). The test involves a use a case-control design to test whether certain types of manufacturing firms (i.e., cluster firms) are more spatially concentrated than might be expected, given the general geographic pattern of all manufacturing firms in the state. All plants associated with a given industry cluster are used as cases and a matched sample of all other manufacturing firms is drawn as comparison case. The difference in concentration between the two is measured by using a D statistic derived from two K functions (a standard statistical geography technique; see Feser and Sweeney, 1999, for details), thereby providing evidence of spatial concentration or dispersion at different spatial scales for the firms in the economic cluster. A positive (negative) value of D outside defined confidence bands implies statistically significant spatial clustering (dispersion).

Findings for three regional clusters with distinct degrees and types of spatial tightness are particularly illustrative: vehicle manufacturing, printing and publishing, and wood products (see Exhibits 4.5, 4.6 and 4.7). In the case of the vehicle manufacturing cluster, its firms are more tightly concentrated at all spatial scales shown, although spatial clustering is most significant at scales of two to six kilometer radius. JIT practices known to characterize this cluster’s supply chain imply greater than average spatial tightness over a wide range of distances. Very gradual convergence toward average spatial concentration over ever longer distances may result from the many different sectors that comprise this cluster, ranging from highly urban, skill-intensive sectors to fairly rural, standardized production sites, which are spread widely along connecting interstate highways and major transportation corridors.

Similar in pattern, yet still unique, is the printing and publishing cluster. It also begins higher than average spatial concentration, but relative concentration peaks earlier at about a 12km radius, and converges rapidly toward average concentration after 50km. This is clearly a highly urban industry, where shorter radial distances are the rule, often with face-to-face contacts necessitated by frequent design or delivery requirements.

Wholly distinct is the wood products cluster. Its pattern shows that cluster members are far more dispersed relative to each other than they are to non-cluster firms. From 7km onwards, wood cluster firms become increasingly more dispersed (relative to the average). This is one practical consequence of wood products being a natural resource-based industry cluster, where proximity to high-weight, moderate-value inputs automatically disperses its firms to remote places of resource availability.⁶

From these findings, it is evident that firms in some clusters are indeed far more closely co-located with each other than with other non-cluster firms. This suggests that cluster externalities and advantages exceed those available to all other firms that enjoy available urbanization externalities. Greater spatial tightness also implies stronger face-to-face possibilities, and the diffusion of technology, knowledge, and general learning that is possible through such spatially-permitted contacts. Findings for the motor vehicle and printing and publishing clusters confirm industrial folklore about the role of localized suppliers and machinery vendors in many industries, particularly the needle trades industries, but it also gives support for certain spatially-centered localization economies based on commonly provided services to firms in such clusters.

4.3 Regional Policy Development Frameworks

As argued above, value-chain cluster definitions and their detailed taxonomies permit researchers to incorporate available data and related concepts in ways that reveal more about broader issues of regional development and aid the empirical research that helps test and build regional development theory. The same quality is valuable for policy applications in specific regions because clusters logically organize large bodies of regional data in more concise and analytically useful categories. To help organize and present such data in a more convenient and less formal framework, we extend the idea of ‘cluster templates.’

These are simply another way of ‘seeing’ the same cluster concept, but the emphasis shifts to the idea that the sectors of a particular region can be seen to fit into two or more representative cluster templates and secondary data can then be used to characterize key components of the overall regional economy.⁷ Consistent with this approach is the further introduction of graphic and mapped visualizations of templates, which are illustrated for several regions.

The utility of these approaches is based directly on the types of policy questions that can be addressed or policy answers that have been sought. It is therefore appropriate to begin our discussion of applications with the main policy question to which our value-chain approach was first applied.

4.3.1 Modernization of North Carolina Industrial Base

In 1996, NCACTS wanted to identify the principal channels through which modern production technologies tended to spread and diffuse in North Carolina. The agency was particularly concerned with a specific policy problem: how to diffuse advanced production technologies efficiently among businesses in a manufacturing economy traditionally dominated by a least-cost competitive ethos . North Carolina’s rapid growth from the 1960s through the 1980s was fueled initially by the re-location of branch plant facilities from high wage, unionized locations in the industrial mid-western and northeastern parts of the country. Although the state has gradually established a solid high technology base (principally centered upon Research Triangle Park) and banking presence (in the Charlotte region), its economy remained disproportionately specialized in traditional sectors under unrelenting pressure from low-cost, overseas producers (e.g., textiles and apparel). In this environment, encouraging producers to invest in, adopt and utilize best practice production technologies can be an exceptional challenge.

In an earlier study of technology adoption practices among producers in the state’s nominal automotive supply chain, the authors found that smaller and often more rural producers tended to be less aggressive in adopting new manufacturing techniques (Bergman et al., 1995; Bergman, Feser and Kaufmann, forthcoming). Reasons cited included lack of information about advanced technologies and inadequate access to sources of capital that do not dilute control over the firm. More passive or traditionally-oriented firms appeared satisfied with the existing market, and not interested in pursuing an aggressive growth strategy through investment in technologies that could open new and protect old markets, even though such complacency is surely fatal in certain traded industries.

The authors also found that producers presently in the NC vehicle supply chain tend to adopt and use technologies at a significantly higher rate. Consistent with other research, study evidence suggested this results partly because final market vehicle assemblers were essentially ‘forcing’ adoption of new methods by their suppliers as well as serving as a source of information about best practice techniques. Also important were increasingly strict international certification requirements (e.g., ISO 9000) that maintain buyer confidence.

There is considerable evidence of powerful diffusion effects that spread competitive production technologies through the supplier or value-chains, a well-known view that continues to receive considerable support from the growing research on buyer-supplier relations. Indeed, Roelandt and den Hertog (1999) make an even broader case that value-chain clusters are actually industry or region-specific ensembles of the larger ‘national innovation system.’ Based on either viewpoint, industrial modernization policies [www.cherry.gatech.edu/mod99/index.htm] might be coherently designed and implemented for the supply-linked firms of certain clusters considered important to state and regional economies.

4.3.2 Technology Composition of Regional Clusters

Regional development authorities responding to statewide policy initiatives of the type pursued by NCACTS would surely need to know how its clusters might be affected. At the same time, a region has many related policy considerations that might depend on the technological level at which its key clusters presumably function, e.g., education programs, public services, or infrastructure.⁸ Therefore, it is important to develop policies with at least a primitive understanding of its situation and that of other regions.

Gaining this understanding can be illustrated below by comparing ‘high technology’ intensity indices that were calculated for industrial value-chain clusters of both the U.S. and North Carolina production economies (Bergman and Feser, 1999). High technology shares of output and employment of industrial value-chain clusters reported below for both the U.S. as the ‘reference area’ and North Carolina are simply calculations possible for any region. Output in several U.S. industrial value-chain clusters is predominantly in sectors that are characterized as high tech at some level.

When their primary industries alone are considered (second column, Exhibit 4.8 ), the share of output in sectors classified as high technology meets or exceeds 80 percent in the petroleum, aerospace, chemicals and rubber, electronics and computers, and aluminum clusters. Several other clusters range from low to moderate shares of high tech output: vehicle manufacturing (63 percent), platemaking and typesetting (35 percent), metalworking (36 percent), and fabricated textile products (23 percent). Fourteen of twenty-three clusters, including the five food products clusters, knitted goods, nonferrous metals, wood products, printing and publishing, tobacco, cement and brick, brake products, and earthenware products produce very little or no high technology output.

A comparison of cluster output in North Carolina versus the U.S. suggests some under- and over-representation of high technology sectors in the state’s industrial value-chain clusters. The ratio of high-to-standard technology production in the North Carolina chemicals, electronics and computers, and aluminum clusters equals or exceeds (in the case of aluminum), the ratio for the U.S. as a whole. Confirmation of high-tech intensities is impossible without a detailed look at the component sectors in each cluster, so one should interpret aggregate profile indicators as tentative evidence that at least some of the critical high technology links are present in the state’s extended buyer-supplier chains, including the strong probability that high-tech links are more concentrated in certain regions than in others.

North Carolina‘s most traditional manufacturing base operates at generally lower levels of technology, although specific industrial value-chain clusters or product chains contain very high concentrations of high technology sectors. The percentage of high technology production in North Carolina’s metalworking cluster, for example, well exceeds its U.S. benchmark. As shown below, the majority of statewide activity in this cluster is in the higher tech, higher wage industrial machinery sectors, rather than basic metals production and fabrication.

Conversely, the share of high tech production in the comparatively very small NC aerospace and petroleum clusters falls well below U.S. averages; the few establishments in these clusters are producing largely standard rather than high technology output. Other value-chain clusters that contain moderate shares of high tech activity at the national level (vehicle manufacturing, fabricated textiles, and platemaking and typesetting clusters) consist in NC of sectors that contain significantly lower relative shares of high tech production. To the degree that supply chain diffusion strongly influences technology adoption, lower overall levels of technology in sectors now linking these chains could limit technology upgrading possibilities, particularly among its neediest cluster members.

4.3.3 Visualizing Intercluster Trade and Technology Flow Networks

Tentative conclusions reached above about the implications of limited technology flows among cluster members may seem obvious, but for many development officials these conceptual insights would be far stronger if reinforced through visualization methods.⁹ If actions are expected of those for whom unfamiliar data and obscure inferential methods are misunderstood or mistrusted, then it is vitally important to demonstrate clearly the inherent consequences of value-chain trade and clusters. To do so, we add visual value to our data templates through the use of network plotting applications.

We have shown earlier that one can identify with which clusters a listed sector is likely to be linked as its first, second and third most important trading partners. Purely secondary sectors often have weaker internal links with any single cluster when they buy or sell interindustry goods to several sectors that are core members of different clusters. While it is these primary sectors that essentially define value-chain clusters because of their intense internal trading patterns, this conversely implies that it is the so-called secondary sectors’ multiple points of cluster contact that serve as the main technology transmission channels between the clusters of any region. Multiple points of contact and linkage are very difficult to explain or grasp unless visualized properly, the possibility of which is illustrated below.

To do so, we will represent these linking relations in visualized template form (KrackplotÔ or similar) for two North Carolina Regions (Exhibits 4.9 and 4.10). The template illustrated here identifies which secondary sectors trade with two or more of a region’s clusters. A graphically depicted set of trade linkages is represented by specific sectors (ovals) that simultaneously trade across two or more clusters (boxes).

The information value of any graphic is greatest when regionally relevant data are embedded within the visual templates. The linking elements might be scaled by an essential channel characteristic, which is the case shown here. Each sector is known to have some characteristic technology, or what we might call technology density. This density is indicated on the graph by different width lines for secondary sectors that trade between various regional clusters. Sectors with the greatest density have higher probabilities of transmitting technology flows between their linked clusters. This permits regional development officials to identify important key sectors, based on their role as potential technology channels.

To illustrate these ideas, we compare here the technological linking of three clusters in two North Carolina Regions: Research Triangle Park Region and Southeast Region. The relative technological ‘carrying capacity’ of sectoral linkages, as indicated by their widths, shows that the RTP Region has more and technologically denser sectors linking its Electronics, Aerospace, and Metalwork clusters than do Southeast Region linking sectors. Similar differences are also visible for other combinations of clusters and sectors. Note also that secondary linking sectors present in one are not necessarily in the other region, and many are absent in both regions.

Regional value-chain clusters are the locally adapted sectoral ensembles that presumably enjoy shared production advantages. The links that connect secondary sectors and their multiple-cluster memberships permit us to grasp another dimension of a region’s coherence. The high potential for intraregional trade between them establishes a provisional understanding of the full ensemble, and offers a framework for seeing a regional economy as a coherently interconnected whole.

4.3.4 Visualizing Regional Cluster Portfolios

The coherent wholeness of a region revealed by graphic representation of linked secondary sectors necessarily omits details concerning the composition of primary sectors that define a region’s main clusters, representing them only by titled boxes. These characteristics of value-chain clusters can be expressed by other software-assisted visualizations to help reinforce understanding of their composition. A consistent graphical framework also provides a common visual vocabulary with which to discuss their meaning and consequence. As we shall demonstrate, there is much room to better understand how the underlying concepts of industrial value-chain cluster templates shed new light on a region’s constituent sectors, using simple spreadsheet graphics.

Direct comparisons of relative specialization in the particular sectors that comprise each region’s clusters are illustrated in Exhibits 4.11 and 4.12. Selected value-chain cluster templates are illustrated for the Research Triangle and Southeast regions. A template is configured for all possible sectors in the value-added cluster, starting with its innermost core sectors positioned at 12 o'clock, all others spiraling clockwise in declining order of ‘trading tightness,’ as measured by correlations with the overall cluster . In other words, we can visualize first the overall distribution of sectors, while those present in the region are indicated by employment vectors measured radially from the center.

Regional employment data are embedded for all sectors along their appropriate radians. A cursory glance at the templates shows each region has only a subset of sectors for a given cluster, and they are typically quite different sectors. The selective presence of sectors reveals how internally specialized the value-chain clusters in regional economies within the same state can become and why regionally-specific cluster policies are essential.

A ‘bulls-eye’ size and share graphic is also located next to each template to gauge the relative importance of the cluster to its region’s economy. The full circle represents 100 percent of each region’s total manufacturing (the Research Triangle’s larger full circle indicates proportionately more total manufacturing employment in all its clusters). The white inner circle represents a cluster’s share of total regional cluster employment (note the Triangle’s relatively much larger electronics cluster), while the black inner circle represents the most tightly trading sectors within that cluster. The white halo represents the relative size of the main linking sectors that trade with more than one cluster, as plotted in Section 4.3.2 above. The Research Triangle region’s relatively small textile cluster consists of higher proportions of core sectors that trade among themselves than is true for the Southeast region’s relatively larger textile cluster. However, the larger Southeast textile cluster also includes higher shares (white halo) of cross-trading sectors, thereby indicating that it is more strongly inter-linked with other clusters in its region or elsewhere.

Other types of regionally available data can also be embedded in value-chain templates, although the data shown here illustrate the potential clearly. Considered as a whole, these visualized templates compress a considerable amount of regionally relevant information into a very tightly organized and easily compared image that helps a policy maker assess important key features and detect proportionate relationships that may escape attention when embedded deep in some data table.

4.3.5 Policies for Small and Independent Firm in Clusters

The small business revolution in regional development thinking shifted attention away from large, exogenous investment development projects pursued under growth center strategies (link to Chapter 2) and more toward indigenous firm incubation and support policies that began in the 1980s. Small and medium enterprise strategies also focused attention on the adequacy of a service industry base seen as necessary to supply key enterprise and producer services. Early industry cluster advocates have repeatedly stressed that the very essence of clusters revolves around the flexibility and shared resources of such firms, usually by drawing attention to the success of Italianate clusters. More recent formulations of industry clusters based on value-chains include firms of all sizes and ownership, although no clear typology has been accepted by which to differentiate all the various concepts (Harrison, 1992; Markusen, 1996; Polenske, 1997).

What is clear is that regional development officials often give due consideration to the firm size and ownership distribution when formulating various policies. This may be even more true if such policies are intended to help support and promote the success of a region’s portfolio of value-chain clusters. The importance of these considerations can be illustrated clearly in the case of industrial modernization policies.

Size and branch plant status have consistently proven key indicators of the level and rate of advanced process technology adoption among manufacturing plants in scientific studies. Numerous surveys have found that large branch plants in nearly every major manufacturing industry adopt new technologies faster and to a greater degree than their smaller independent counterparts (Bergman, Feser and Scharer 1995).¹⁰ Smaller producers have fewer of the necessary resources, both financial and human, to effectively integrate complicated new technologies into their production regimes.

Alternatively, the owners of some smaller businesses show reluctance to invest in technology upgrading if the financing of such investment requires dilution of their equity in and control of the firm. Identifying those sectors with a predominance of smaller manufacturers, particularly those at the smallest end of the size scale, is thus one preliminary means of narrowing down areas of potential demand and need for competitiveness initiatives. Size, in effect, serves as a very rough proxy for level of modernization, and indirectly, of a need for some form of technology assistance.

Exhibit 4.13 lists the shares of both small and single (versus branch plant) establishments in each cluster. Among the largest North Carolina clusters, the wood products, printing and publishing, and metalworking clusters are each made up predominantly of very small firms and establishments. In each case, close to 80 percent of businesses employ fewer than 50 workers. With the average share of branch plants at just 12 percent, these clusters are also largely composed of single-establishment enterprises. The clusters with the lowest shares of small plants are knitted goods (41 percent), packaged foods (52 percent), fabricated textile products (55 percent), chemicals and rubber (55 percent), and vehicle manufacturing (58 percent). With the exception of vehicle manufacturing, close to one-third of the establishments in each of these clusters are branch locations of multi-location firms.

4.3.6 Cluster Targeting and Tradeoff Policies

Industrial clusters can be used to detect the presence of a critical mass of value-chain sectoral activity that might benefit from the strategic application of targeted development policies. Scott and Bergman (1996), for example, examined the prospects for developing a ground transportation manufacturing complex in southern California, after mapping important input-output linkages of key sectors. North Carolina appears similarly poised to take advantage of the continuing southward shift in the geographic center of vehicle production in the United States (Klier 1994, 1999). At present, there are no automotive assembly plants in the state, although trucks, school buses and specialty transportation equipment are now produced. Moreover, the recent location of production facilities of several major automakers to the south and west, including BMW in South Carolina, Saturn in Tennessee, and Mercedes in Alabama constitute potential markets for suppliers based in North Carolina. Consistent with these trends, the vehicle manufacturing cluster within the state appears to be shifting westward. The international comparison of motor vehicle cluster restructuring discussed in Section 4.2.1 showed increasing concentration of this cluster in the Carolinas Region, which is along the southwest border of the state.

Though it cannot be known from value-chain clusters alone which local firms in the vehicle manufacturing cluster produce goods related directly to automaking or the production of trucks and busses, there is nevertheless strong and visible potential in the state for the further development of its vehicle manufacturing chain, including the possible recruitment of a major final assembly plant. But in establishing policies that target one cluster, there is always the question of how to consider other potential clusters.

At the other extreme, consider the knitted goods cluster, which is this region’s largest (19 percent) and perhaps most threatened cluster (wood products (18 percent), vehicle manufacturing (15 percent), and fabricated textile product (11 percent) clusters also account for more than 10 percent of total primary cluster employment). All are significant components of the regional economy whose combined needs require a portfolio of suitable policies. Larger regions or cities typically adopt policies suited to a continually changing mix of industries distributed across segments of several value-chain clusters, rather than to only one of 23 total possibilities. The four larger clusters mentioned above are joined at some threshold presence by electronics and computers (4 percent), printing and publishing (3 percent), chemicals and rubber (2 percent), plus slight traces of ten other clusters.

Any of these may contain the seed of quite dramatic economic transformations in future decades, or the linking agents that connect important common elements of larger clusters now active in a region. Accordingly, regional development officials and company managers who are committed to making interdependent investment decisions concerning the full regional economy may proceed with greater confidence when informed by readily envisioned clusterings and potential reconfigurations of a region’s many firms and industries.

To appreciate the possibility of improved regional strategies that account for more than one cluster at a time, consider the following evidence for the Western Economic Development Partnership Region. First, the spatial concentration of the knitted goods cluster, as revealed by common GIS mapping procedures in Exhibit 4.14 clearly distinguishes its spatial intensity, major interstate highway systems, and proximity to nearby cluster concentrations of other regions. The spatial pattern of all firms in this and other significant clusters permits better alignment with existing or planned features of infrastructure in the region and neighboring regions. Transportation improvements, essential public utilities, key public service areas, education and training facilities, and similar development policies may be fully reconsidered in light of each cluster’s pattern.

Joint spatial patterns of a region’s principal clusters reveal possibilities for designing comprehensive policies, particularly since the mountainous landscape and widely dispersed towns and production centers of these regions require very careful infrastructure planning. The same logic also applies at higher levels of policy responsibility, e.g. statewide policies, where spatially networked clusters of several regions are involved. For example, North Carolina’s declared interest in promoting the vehicle manufacturing cluster and the present strength of this cluster in neighboring regions (Exhibit 4.15) readily suggests a strategic restructuring of the WEDP region away from its primary dependence on the knitted goods cluster.

Each region has its own cluster targets and tradeoffs to consider in formulating reasonable development policies. It is therefore essential that the kind of information and analysis described here be used to supplement the usual sources and frameworks available to policy officials.

Because of this need, these and many other regionally specific data were drawn upon to prepare a series of separate policy framework documents (50+/- pages) to guide ongoing policy discussions in each of North Carolina’s seven economic development partnerships. It is important to note that these reports are based totally upon the analysis and presentation of data that adopt value-chain clusters as the organizing framework.

Notes

1. These broader concerns were first advanced elsewhere (Bergman 1998), some points of which are emphasized and sharpened in this section. Author self-sources are repeatedly drawn upon in this section to provide a range of applications, each of which will cited at relevant discussion points.

2. Traditional sectors at have been renamed ‘industry clusters’ in many recent (mis)applications of the broader concept for states and regions. Some of this renaming is merely the marketing residue of quick and dirty sectoral studies, even though these efforts often result from a genuinepolicy interest in understanding and harnessing the forces that now sweep through regional economies. http://www.credc.com.au/cluster.htm, http://www.users.uswest.net/%7Egaryboydston/index.html, http://www.commerce.ca.gov/california/economy/neweconomy/addendum2/index.html

3. Value-added is estimated by applying industry specific wage/output ratios derived from input-output tables to wage data from ES202 files. Employment data also taken from ES202 files.

4. Calculated net of large wood processing sectors that trade more heavily with other key clusters in both regions. As the wood processing sector is a major component of both regional economies, the result would be biased in favor of more variation, leaving the overall picture, however, the same.

5. These shifts could not have occurred because of simple classification artifacts, as the classification remained stable in both years. A decade-long restructuring away from larger or state-owned firms of a dominant classification into smaller firms of different but more precise classification in 1991 is more likely responsible, even though it is impossible to know if this happened or whether such a case would imply a true shift in the types of goods produced.

6. See discussion of policy issues facing firms of this cluster for California. http://www.commerce.ca.gov/california/economy/neweconomy/addendum2/ad250.html

7. Data is generally available at highest level within which supply chains deliver interindustry goods unimpeded, such as a nation or customs union (NAFTA, EU), that can be aggregatively transformed into useful attributes of industrial value-chain clusters. As demonstrated elsewhere, the most obvious imputed detail is output data (value of output, value-added, etc). However, basic factor input data (labor, capital or resources) and a wide range of supplemental data collected for constituent sectors only at high levels of aggregation (e.g., technology levels, productivity, production residuals, energy consumption, etc.) permit other ways to characterize and compare economies. Weighted proportions of sectoral presence in each cluster can be employed to calculate a central tendency measure of its constituent sectors. There are many opportunities afforded by various secondary data sets that can be incorporated to characterize value-chain clusters in greater detail or to capture more finely-calibrated distinctions, as earlier analytic discussions and schematics have demonstrated. Certain possibilities are illustrated in the text by characterizing a cluster in terms of the average technology or types of establishments that comprise its constituent sectors, as revealed by their size and organizational structure.

8. Arizona, for example, uses high-technology cluster needs as a useful rationale for organizing and focusing various education programs. http://aztec.asu.edu/k12/htic/

9. A shortcoming common to many analytically-based studies is the difficulty of demonstrating how to draw inferences or detect meaningful relationships from the analyses that inform options and provide good overall policy perspectives. This is part of the challenge of ‘seeing local economies whole.’ Wholeness will not matter much if it cannot beheld firmly within the mind’s-eye as alternatives are posed and considered. The very ideas embedded in value-chain clusters and their inherent internal and external linkages drift easily from view in the welter of tables, graphs and statistics supporting analytically sound but opaque concepts that such results often contain. Edward Tufte built a wide reputation by publishing his own series of books about the utter importance and utility of visual representation http://www.amazon.com/exec/obidos/subst/features/t/tufte/tufte-edward-interview.html. Tufte has also noted the equally important point that analysts often don’t fully understand their evidence without such assistance.

10. A value-chain cluster can serve as the sampling frame for in-depth survey or interview methods of micro investigation. The sampling-frame approach permitted by value-chain cluster definitions has been used with good success by authors in micro studies of one cluster in North Carolina (Bergman, Feser, and Scharer, 1995) and of four clusters in four Austrian regions (Bergman and Lehner, 1998a). http://iuwhp1.wu-wien.ac.at/iir-clusters/

Copyright ©1999, Regional Research Institute, WVU

Book List    Contents      Chapter One     Chapter Two     Chapter Three     Appendix 1