The Sum of Their Parts: Defining Regional Industry Clusters

By Kyle Uphoff
October 2008

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In the 1930s a group of talented electronics engineers moved to the San Francisco Bay area. In the 1780s James Watt invented progressively improved steam engines in Glasgow, Scotland. In 1957 Earl Bakken created the first wearable pacemaker in Minneapolis. From these nascent beginnings the California computer industry, the British shipbuilding industry and the Minnesota medical devices industries were born.

The economic competitiveness of nations has long depended on the presence of natural resources or proximity to trade routes or markets. Yet industries often defy that logic, growing and clustering in seemingly unlikely places. Why did one of the world’s most vibrant medical device industries emerge in a frigid agricultural state in the upper Midwest? The answer lies in those many factors that go beyond natural endowments.

Some of these factors are tangible. An industry may need to be close to suppliers or specific markets. When specific skills are needed, an industry might locate where labor and training providers are available. Industry clustering produces increased efficiencies. Information, along with culture and historical accident, represents a somewhat more intangible factor for cluster development. Firms may locate near a prominent university to benefit from its innovative atmosphere and knowledge assets. Suppliers produce not only widgets but ideas about how to improve products and processes. By sharing information through formal and informal networks, firms are better able to conceptualize, design, produce, tweak and market new products. Sharing also allows firms to engage in joint action to solve economic, regulatory or natural problems that might be too big for any individual firm to address. Information networks form between competing firms, suppliers, universities and regional government/nonprofit entities. This is the basis of an industry cluster—a geographically proximal group of entities with a shared interest in a line of business.

Identifying Clusters

Identifying clusters entails a mix of art and science. Mature industries with a set of closely related products and services are easily identified by their NAICS (North American Industrial Classification) code, a classification that describes the products and services a firm offers. State and federal data provide employment levels and wages for those industries at local and regional levels. Because this information is reported nationally, it is also possible to get an idea of what industries have a higher concentration in the state or region versus the nation as a whole or against other states and regions. The location quotient allows for such comparisons by providing the relative percentage of jobs in an industry regionally against the nation as a whole. An industry with a regional location quotient greater than 1.0 is considered to be distinguishing because it has a higher share of workers than the nation. For example, the electronic medical device industry in Minnesota has a location quotient of 10.5. Industry employment in medical device manufacturing in Minnesota is 10.5 times that of the U.S. as a whole.

Location quotients can tell something about the relative economic importance of an industry, and its potential cluster, to a region. Typically, the importance of an industry is thought of in terms of its employment size and wages. Figure 1 shows how some distinguishing industries in Minnesota compare with one another in those areas. Some industries such as insurance carriers or computer manufacturing firms employ larger numbers of people, given by the width of each bar, and pay high average wages, shown by the height of each bar. Industries such as mining pay high wages but have relatively few workers. While this might be true at a statewide level, computer manufacturers may have little exposure in northeastern Minnesota, while mining is an important industry. Similarly, animal production may be an important job and wage producer in southern or western Minnesota but has less importance in other parts of the state. For this reason, it is important to identify clusters regionally.

Much of the analysis above works well for established industries that are easily characterized by the NAICS system. It does not do so well for emerging industries, those that are producing new products that did not exist or existed only on a small scale until relatively recently. Wind turbine blades or solar cells would be good examples of such products. It also does not work particularly well for industries that may produce different products but use a common technology. For instance, biotechnology and nanotechnology have the potential to revolutionize the way we produce pharmaceuticals and crops or electronics and plastics, respectively. However, the NAICS system does not distinguish between firms that use or do not use these technologies. Measuring the magnitude of such activities requires extensive interviews with industry representatives and any academic partners involved.

Location quotients also tell something about the health of that industry regionally. An industry that is growing faster or losing jobs slower than the nation as a whole will have an increasing location quotient. Table 1 identifies those industries in Minnesota that are distinguishing to the state.

Animal production, miscellaneous manufacturing, and nursing and residential care facilities are growing in location quotient and employment. These industries are growing faster than the U.S. average. Printing and computer and electronic product manufacturing are growing in LQ but not in employment, an indication that these industries are losing jobs slower than the U.S. trend but are generally remaining competitive. Industries with diminishing LQs may be losing jobs while the U.S. is growing (e.g., management of companies) or are losing jobs faster than the U.S. average (e.g., machinery manufacturing).

The location quotient provides only one step in identifying an industry cluster. It does not identify all of those suppliers that feed into the industry at the core of the cluster. A true cluster includes all of those relationships, supplier and otherwise, that exist within an industry. By using input-output analysis or economic impact software, one can estimate the percentage of local inputs that may go into producing a product. Using this information in conjunction with local interviews of businesses can provide an idea of the size of the regional industry cluster and the types of linkages that are predominant. These interviews also provide information about linkages to universities and trade groups that do not show up with traditional economic data.

Not All Clusters are Equal

Industry clusters can fall under several categories. For instance, local clusters represent industries that typically produce goods or services for use in the immediate geographical area. Health care might be an example of such an industry, though the health care industry in southeast Minnesota certainly pulls in customers from around the world. In aggregate, local clusters generally pay lower than average wages and depend upon more export-driven industries for their viability.

Natural resource clusters typically export beyond an immediate region and have the potential to pay high wages (See the example of mining in Figure 1.) In many cases, the value-added work around these products is carried out in another region. Historically, northeast Minnesota exported taconite to other states where it was turned into steel, though that may be changing with new steel mill development on the Iron Range. Unfortunately, natural resources can be depleted or take a long time to reform. Firms may be highly innovative in harvesting a natural resource but not very innovative in creating value-added products for the resource. As a result, natural resource clusters do not tend to be very innovative—producing fewer patents than other industry clusters.

Traded clusters generate products that are exported beyond the region and might be in competition with other clusters in regions beyond the state or country. Traded clusters typically produce high-value added goods or services and pay higher than average wages. They are also often highly innovative, producing a larger number of patents than the clusters mentioned above. As a result, traded clusters are often the focus of economic and workforce development planning activities. One study found that Minnesota’s traded industry clusters account for 28 percent of cluster employment in the state and generate 23 patents per 10,000 employees. [1] While the annual wage range of $48,148 is comparable to the wages in natural resource employment, the rate of patent creation is seven times higher that of the natural resource-based cluster.

All Clusters are Regional

Industry clusters are likely to span political geographies, drawing workers, materials and ideas across cities, counties, states and even countries. Using the location quotient concept, Figure 2 shows those counties in Minnesota with high concentrations of employment in the metalworking machinery manufacturing industry. Firms in this category might produce industrial molds, machine tools, or tools and dies. In this example there is a high industry concentration in the counties surrounding the core metro region. There are more than 200 firms and about 4,000 workers involved in this activity in these 14 shaded counties plus Hennepin, Ramsey and Dakota counties with high industry concentrations. There are also lesser concentrations in other parts of the state including west central Minnesota. High location quotients alone do not identify a cluster, but they provide the basis of future work. Clusters need not be as dispersed as the metalworking machine manufacturing industry. The financial securities industry, for example, is highly concentrated in Minneapolis and St. Paul and could even be isolated to individual ZIP codes within those cities.

Beyond the Numbers

Data such as those above give a cursory overview of industries and their recent trends compared with the national economy. However, the analysis says little about the strengths of the industry, the challenges facing the industry, or the linkages of the industry to partners. Most of the work around cluster analysis is not quantitative. Analysis of clusters requires the identification of specific firms and partners (trade associations, economic developers, financiers, researchers, etc.) and the mapping of supplier relationships. Once identified, these groups must be interviewed (social network analysis) to discuss the magnitude and nature of relationships across the region as well as the strengths and challenges that a cluster faces.

A parallel activity is general analysis of competitiveness. Information is collected on patents relevant to the cluster, key products, major firms and entrepreneurial activity. This is particularly important when researching clusters that are not confined by NAICS codes. Information on research and entrepreneurship provides outlook on new industries and where mature industries are headed. Once collected, this information can be compared with other regions with similar strengths to determine the relative competitiveness of the region in that cluster. Groups can use this information in the strengths, weaknesses, opportunities and threats (SWOT) analysis of an industry cluster. This process is used to characterize clusters by the external and local factors impacting those industries (i.e., new technologies, skill shortages, new markets, etc.) and the relative position of the region to influence or be influenced by those impacts.

Finally, actions and policies are identified. In collaboration with representatives from industry and partner organizations, actions can be formulated that address issues at the state, regional or local levels. Quantitative and qualitative research around the cluster must be maintained as an ongoing process. Benchmarked performance indicators are used to determine the efficacy of policies put in place. Ongoing research and benchmarking are important since the needs of the cluster are likely to change with national and international economic, demographic and technological shifts.

Workforce Implications

Industry clusters are important from a workforce development perspective. Industry clusters give a region distinct workforce needs. The workforce needs for a region that is a finance center are obviously going to differ greatly from a region that has industries that depend on natural resources. Clusters drive the workforce needs of the regions where they are located. Indeed, the presence of a qualified workforce can be as important as suppliers in determining the location and expansion potential of firms. As clusters produce higher value-added products, the skill requirements for workers are likely to expand. Skill requirements could become very specific depending upon types of tools or specific knowledge requirements being used within the cluster. In such cases training institutions become an integral part of the cluster. Educational institutions must be responsive to the changing workforce needs of industry, and employers must be engaged in a constant dialogue with those institutions. In workforce shortage conditions, employers in the cluster can engage in joint action, providing the resources necessary to attain a skilled workforce. Government, educational institutions and nonprofits can provide the catalyst to drive such action.

It is also possible to look beyond the industry cluster to find industries with common workforce needs. This is particularly important if a cluster is fairly small or if there are regions that are not characterized by an industry cluster. In the example above, the metalworking machinery manufacturing industry has a fairly substantial presence in the greater metro region. It is possible to find other industries with occupational staffing patterns that are similar to that industry. Table 2 shows five industries in the 17-county area identified above that have similar staffing patterns. This “occupational cluster” has 944 firms with similar staffing needs and employs more than 22,000 workers, not including firms in Wisconsin.

Firms and Employment Numbers for Industries with Workforce Patterns
Similar to Metalworking Machinery Manufacturing:  17-County Region, 2007

Where Next?

If clusters are built in part around information networks, what happens when information is shared at the click of a mouse? The programming language LINUX was conceptualized in Finland but is now being developed by a scintillating lattice of programmers around the world. What does this mean for regions with software or other knowledge-based clusters? Toyota, Boeing and other manufacturers share information with global networks of suppliers and their engineers and designers. Will new information technologies lower the distance barriers that once restricted the flow of ingenuity? Will the high value-added industries of the future defy cluster principles?

Despite such questions, the cluster concept maintains relevance in a changing world. For example, not content to be the “shop floor of the world,” China is increasingly focused on creating products with higher value-added and having a role beyond simply making products that others design. To this end, the Chinese government has created new labor laws, instituted new taxes and cancelled tax incentives—all aimed at reducing industries with high pollution and low-skilled employment. [2] Industry clusters that are synonymous with China, such as shoe production in Guangdong province, will now face off-shoring to other low-cost labor, low-regulatory nations such as Vietnam. A maturing cluster in one nation becomes a growing cluster in another. Alternatively, Chinese officials talk of “technological solutions” that could save such industries. Firms that can survive regulatory bottlenecks through innovation and joint action can provide the basis of a restructured more competitive cluster.

While industry clusters evolve with changes in regulation, technology and demand, and while cluster analysis evolves to keep up with changes in the way people share information and collaborate, the concept of clusters will continue to be relevant to economic and workforce development over the long term. Within DEED, the cluster concept is being put to use in the field. Workforce and economic development leadership and staff have been trained in Porter’s cluster theory through the University of Minnesota’s Microeconomics of Competitiveness training. In addition to theory, students are exposed to “real world” examples of how cluster analysis can be incorporated into their work with job seekers and employers. This work is helping DEED leadership and staff to build better partnerships with business and to be more effective partners in economic and workforce development.

 [1]“A Competitive Benchmarking of the Minnesota Economy,” National Governors Association Winter Meeting, Feb. 24-27, 2007. Monitor Company Group L.P. and NGA Center for Best Practices.
[2]“Last call for Guangdong Shoemakers,” Asia Times, Feb. 5, 2008.

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