1. Economic Development Policy Part 6: Industry Clusters ECON 4480 State and Local Economies 1

2. Industry clusters What are industry clusters? Why are they important? How does one identify industry clusters? How do clusters grow? State policy and clusters 2

3. What are clusters? Industry clusters and cluster analysis have taken a major position in state Third Wave policies. An industry ‘cluster’ is a collection of organizations in a locality that share commonalities among one or more of the following: – Buyer or supplier relationships, – Distribution channels, – Technology, and – Labor supply and skills. 3

4. What are clusters? 4 Related sectors Trading sectors Supporting institutions Clusters: Interdependent firms and institutions Common suppliers of inputs, producer services Similar technology Shared pool of labor Similar strategies Education Training (vocational) R&D Regulatory agencies Interdependence: each firm’s competitive position depends on at least some other members of the group. Source: Feser, 2001.

5. Why are clusters important? Why think about clusters instead of using traditional industry classifications? The cluster as a concept is better aligned with the nature of competition and is more likely to have a role for government policy. Clusters describe linkages and spillovers in terms of skills, information, technology, and customer needs that cut across traditional industry classifications. Encouraging these spillovers suggests a possible role for government policy. 5

6. What are clusters? The important aspect of clusters is that these organizations are inter-connected: events and trends that benefit the cluster will also benefit individual firms. The benefits related to the existence of clusters have implications for state and local economic development policy. More often, policies are crafted to benefit a cluster instead of a particular industry. 6

7. Locational advantage Proximate relationships between buyers, suppliers, and institutions are important to build competitiveness and innovation. The degree and quality of these relationships for a given area is its locational advantage (Porter). 7

8. Sources of locational competitive advantage 8 Local context for firm strategy and rivalry Factor input conditions Demand conditions Michael Porter’s diamond metaphor Source: Porter, 2000. Related and supporting industries

9. Locational advantage A cluster grows when the diamond improves. Factor inputs – must become more efficient and innovative if the cluster is to grow. Context for firm rivalry – includes the rules, norms, and incentives governing local competition; vigorous local rivalry improves competitiveness. 9

10. Locational advantage Demand conditions – local demand conditions can move firms from low-productivity exporters of uniform products to competing on differentiation. The presence of sophisticated local demand presses firms to improve, increasing global competitiveness. Related and supporting industries – benefit and are benefitted by an improving cluster. 10

11. What are clusters? Clusters are not formalized, well-defined, institutions. There are no generally agreed-upon definitions for clusters. Organizations in a cluster can be private firms, non-profit organizations, or governments. 11

12. Why clusters? Popularity of clusters among state and local policymakers owes to two reasons: – 1) geographic concentration of related industries can be influenced by state policies, and – 2) clusters can defend local economies against the negative effects of globalization. 12

13. Are clusters important? The relevance of clusters is supported by the flexible specialization hypothesis. Markets have entered a period of high uncertainty and rapid technological change. This environment punishes vertically integrated firms, typically unable to innovate and adapt. Alternatively, the environment rewards firms that vertically disintegrate: replace one’s own supply capacity with specialized external suppliers. 13

14. Flexible specialization Successful firms will develop strong linkages with specialized suppliers and partners. Suppliers and partners learn about the producer’s needs, and are expected to adapt and anticipate future shifts in technology, with the needs of the producer in mind. This is quite different from traditional arms- length relationships with a subcontractor. 14

15. Flexible specialization Critics of flexible specialization point to inefficiencies that may be lost by moving away from arm’s length contracting. Firms that need standardized inputs, for example, may get a better price by letting competitive bids, rather than working with highly specialized suppliers. 15

16. Flexible specialization Another criticism is that today’s successful cluster may be tomorrow’s failing industries. Relying too much on a single or small number of related clusters opens the region up to substantial risk if the market environment changes: too many eggs in one basket. 16

17. Flexible specialization One approach to deal with this might be to develop alternative clusters that follow a different business cycle. Thus, if market conditions turn sour for Cluster A, it is likely that Cluster B will be on the upswing. This argument gets at the need to balance growth potential against diversification of the industrial base. 17

18. How to identify clusters? Observation: there is no generally accepted method of identifying clusters. Two approaches have been identified: – Inter-industry approach: uses input-output data to identify industries that have similar patterns of inter-industry trade. – Concentration approach: identify localities with an unusually strong concentration of a type of economic activity as measured by LQs. 18

19. How to identify clusters? Using the inter-industry approach, Feser and Bergman identified 23 major clusters within the U.S. manufacturing sector, accounting for 90% of manufacturing output. Clusters are comprised of a major producing sectors and first, second, and third tier suppliers. Largest cluster: metalworking, with associated 116 industries. Smallest cluster: tobacco, with four associated industries. 19

20. How to identify clusters? Using these 23 clusters, Feser and Bergman constructed cluster t emplates: – For each clusters, a detailed listing of all the primary and secondary industries comprising the cluster. – An industry may belong to more than one cluster. 20

21. How to identify clusters? How are the cluster templates used? Example: a local economy interested in building up a cluster can use the U.S. cluster template as a guide to spot linkages that are missing locally. Policies could be adjusted to attract or develop firms in the ‘missing’ sector, thereby strengthening the cluster and increasing the cluster multiplier. 21

22. How to identify clusters? The next slide, by Feser and Bergman, shows linkages among 58 primary and secondary sectors involved in the U.S. auto manufacturing cluster. In the early 1990s, the cluster employed 4 million workers in 80,000 establishments. Major purchasing linkages in the graphic include: – 3711: passenger car bodies – 3714: auto parts and accessories – 308: plastics – 321: flat glass – 306: fabricated rubber products 22

23. Vehicle manufacturing intra-cluster supplying linkages 23 Source: Feser, 2001.

24. How to identify clusters? Next are shown the major sales linkages in the the U.S. auto manufacturing cluster. Two industries dominant purchasing: – 3711: passenger car bodies – 3714: auto parts and accessories. 24

25. Vehicle manufacturing intra-cluster sales linkages 25 Source: Feser, 2001.

26. Feser and Bergman method Starting with the U.S. input-output transactions table, Feser constructed two other tables: – 1) Input table: a table of direct requirements coefficients, showing the distribution of input purchases by the column industry from the row industries, and – 2) Sales table: table showing the distribution of sales from the column industry to the row industries. 26

27. Feser and Bergman method This leads to four possible linkages between two industries, A and B: – 1) A and B have similar input purchase patterns from industries, – 2) A and B have similar sales patterns to industries, – 3) A’s input purchases are similar to B’s sales pattern, and – 4) B’s input purchases are similar to A’s sales pattern. 27

28. Feser and Bergman method 28 Supplying industries Industry AIndustry B Purchasing industries 1) Pattern similar? 2) Pattern similar?

29. Feser and Bergman method 29 C Industry A Industry B 3) Patterns similar? DFE B’s sales pattern A’s input purchase pattern

30. Feser and Bergman method 30 C Industry B Industry A 4) Patterns similar? DFE A’s sales pattern B’s input purchase pattern

31. Feser and Bergman method Next step: develop a numeric measure of linkage strength by calculating correlations among industries for each of the four possible linkages. If A and B have similar input purchase patterns, for example, then the input purchase correlation will be high between A and B. The same for the other three linkages: if patterns between industries are similar, correlations ill be high. A low correlation indicates a weak linkage pattern. 31

32. Feser and Bergman method From all the correlations, select the highest single correlation among the four sets for every industry pair, resulting in a matrix of correlations. Next, Feser and Bergman used the correlation matrix as data for a principal component analysis. Principal component analysis is a data reduction technique designed to condense many variables into a few variables by ferreting out underlying relationships and linkages. It involves a straightforward but tedious decomposition of the correlation matrix. 32

33. Feser and Bergman method Principal component analysis (PCA) searches for a linear combination of the variables that explains the most variance. Once the first principal component is constructed, it searches for a second principal component that accounts for the remaining variance. PCA provides two important pieces of information: – 1) the total variance accounted for by a given principal component, and – 2) the distribution of the principal component among the variables (the factor loading). 33

34. Feser and Bergman method The principal components, after interpretation, become the identified clusters. The explained variance for the principal component becomes a measure of the importance of the cluster for the economy. 34

35. Feser and Bergman method 35 Results: the first principal component was interpreted as ‘metalworking’ accounting for 25% of total variance. Next, the second principal component identified as ‘vehicle manufacturing’ accounted for 11% of variance, and so on. Eigenvectors (not shown) provide the contribution of each detailed industry within the cluster.