[ieee technology - portland, or, usa (2009.08.2-2009.08.6)] picmet '09 - 2009 portland...

Typology and Evolution of Technology Clusters-Evidences from the Hi-tech Industries

Jiang He, M. Hosein Fallah

Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ - USA

Abstract--Clustering is one of the key drivers for regional economic growth. Development of technology clusters is a dynamic process, which is influenced by a variety of internal and external factors. Availability of skilled labor, presence of functioning networks and partnerships, and evolution of the industry are among the key factors. According to Ann Markusen, technology clusters can be distinguished from one another based on their fundamental typology: the Marshallian form, Hub-and-Spoke form, Satellite form, and State-centered form. However, the effect of cluster typology on the development of cluster has not been studied. In this paper, we investigate 15 metropolitan-based technology clusters in the United States, covering communications equipment manufacturing, information technology, and biopharmaceutical sectors. By examining the composition of these high-tech clusters, we observe: 1) how these technology structures have changed their typology over time; 2) differences in cluster typology among different industries. Our analysis results suggest that the map of cluster typology varies significantly for different industries. In addition, our analysis results suggest that the long-term sustainability of a cluster is largely determined by the prosperity of small-and-medium firms within the cluster.

I. INTRODUCTION

Clusters are geographic concentrations of interconnected companies with other supporting organizations such as universities, public research institutions, standards agencies, or trade associations. Companies in a cluster may co-operate or compete with each other [12]. The contribution of clustering to regional economic development has been well documented [4 and 8]. Companies cluster together to benefit from the availability and quality of local labor pools, well-developed intermediate input suppliers, and better information flows which can facilitate the generation of new ideas.

Because of the significant benefits brought by clustering, both academic researchers and regional planners seek to understand the factors shaping the evolution of clusters. In many of the real-world cases, clusters have emerged unplanned and then go through a dynamic lifecycle process, which typically consists of four stages: embryonic, established, mature and declining. Although we rarely see a successful cluster which was built from scratch via government planning, it has been widely accepted that policy interventions, if appropriate, can favorably influence the development of clusters which have been existing. When cluster policy interventions are concerned, an important fact one has to acknowledge is that all clusters are different one to another and therefore any cluster policy intervention needs to

be developed based on the characteristics of the specific cluster.

Despite of the differences between clusters, there are some important common features found in certain groups of real-world clusters. Several previous studies have attempted to classify real-world clusters based on those key common factors. Based on the literature on the cluster classification, our study examines 15 hi-tech geographical clusters covering information technology, communications equipment manufacturing, and biopharmaceutical sectors. We focused on the hi-tech sectors because of their growth in recent years and their economic impact. The objective of this study is to find out: 1) What are the typology structures of these hi-tech clusters? 2) Do clusters in different industries tend to feature different typologies? 3) How do technology clusters change their typologies over time? The rest of this paper is organized as follows. Section 2 examines the existing literature on technology clusters and cluster categorization. Section 3 describes our methodology of data collection and analysis. In section 4, we present and interpret the results of data analysis. We then discuss the implication of the data results for cluster researchers and practitioners.

II. CLASSIFICATION AND DYNAMICS OF

TECHNOLOGY CLUSTERS

The studies of clusters date back to Marshall [10], who developed the agglomeration concept, based upon the cost-saving scale effects brought by industrial localization. The Marshallian industrial district is a spatially delimited region where the business structure is comprised of small, locally owned firms. It is characterized by substantial intradistrict trade among buyers and suppliers in the local area. The original Marshallian formulation did not consider collaborations between firms and/or individuals as an important driver for the development of clusters.

Following Marshall’s concept, a newer version of Marshallian formulation was developed by several recent researchers [1, 2, 5, 11, and 15]. The updated Marshallian formulation has been validated so well when explaining the success of the industrial clusters of small manufacturing companies in central and northern Italy so that the reworked formulation has been labeled as the Italian Variant of the Marshallian district.

The modified Marshallian version was later used to examine some American technology clusters including Silicon Valley and Orange County [13 and 14] as well. An important feature distinguishing the Italian Variant from the original formulation is that the updated version emphasizes

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PICMET 2009 Proceedings, August 2-6, Portland, Oregon USA © 2009 PICMET

on the long-term socio-economic relationships between cluster members. According to the Italianate version, the most important facilitating factor for a cluster is its “industrial atmosphere” which enables trust and encourages collaboration and exchanges of creative information between cluster members. Second, the existence of a well-built governance structure (e.g. trade associations, local government involvement, etc.) is considered as a necessary factor for a sustainable cluster.

Even with its Italian version, the Marshallian formulation could not explain the flourishing of some present clusters and demise of others. In order to address the increasing

complexity and variety of real-world technology clusters, Markusen [9], through inductive inquiries, came up with a new approach for distinguishing among types of industrial clusters by their typologies. In addition to the Marshallian formulation including its Italian version, three new categories of clusters have been proposed by Markusen [9] – hub-and-spoke, satellite platform, and state-anchored type.

Figure 1 demonstrates the basic structure for each of the four cluster categories. The links depicted in this chart reflect the regular inter-firm business activties such as supplier-customer transactions.

A) Marshallian B) Hub-and-Spoke

C) Satellite Platform D) State Centered

Source: Markusen (2005)

Figure1: Typology of Technology Clusters [9]

The second type of clusters proposed by Markusen, hub-

and-spoke form, is structured around one or few dominant firms supporting the regional cluster, while suppliers and other activites spread around the hubs like the wheel spokes. In its simple version as depicted in Figure 1A single large firm trades with local and external suppliers and sells mainly to external customers, which could be large or small ones. In real world, single-hub supported clusters include Seattle-based aircraft manufacturing cluster supported by Boeing, and Toyota City-based car manufacturing cluster supported by Toyota; and a multi-hub supported cluster can be observed in Detriot where the “Big Three” dominate the regional economy and are surrounded by a number of small and large suppliers. In a hub-and-spoke cluster, inter-firm collaborations usually occur only between hub and non-hub firms, and the terms of cooperation are in many cases set by the hub firms. Collaborations between smaller firms are rarely seen as the smaller ones are usually very focused on benefiting from the large anchor.

According to Marsuken’s study, the dynamics of a hub-and-spoke cluster are largely determined by the hub firms, either via its success or failure. When job positions become available in hub firms, workers “often abandon smaller employers to get onto the hub firms’ payroll”. When hub firms are declining for poor management or industry downturn, the local economy can be damaged substantially [9].

Marsuken’s third type of clusters is satellite platform. This type of cluster largely consists of a congregation of branch facilities of externally based multiplant firms. In many cases, a satellite platform cluster emerged when certain local or national policies were developed to create a favorable investment environment for externally headquartered firms. The Research Triangle Park in North Carolina represents a high-end example of satellite platform cluster, where a number of high-tech multinational firms established their R&D centers there. In such a cluster, the remotely located parents companies make important decisions for the local

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branches thus influence the regional cluster. A key characteristic for this type of cluster is the absence of any sort of connections or networks within the regional cluster, as the local branches maintain regular business relations mainly with their remote headquarters or other partners. Thanks to the availability of skilled professionals and presence of platform tenants, local entrepreneurship may be encouraged in this type of clusters. However, over the long run, the lack of financial service and cluster-oriented governance structures often become barriers for the start-ups. The literature suggests that the aggregate growth for such a cluster is largely determined by the region’s capability of attracting tenants and retaining them [6]. In addition, the satellite platform clusters always have to face challenges from similarly constructed platforms, which may be remote or nearby. For high-end satellite platforms, the most challenging issue is to maintain and develop the pool of skilled professionals; while low-end platforms are more vulnerable to increases of cost of running business. Given the nature of this type of clusters, a satellite platform is less sustainable over time if the cluster fails to develop a more diversified local industry.

The fourth type of cluster proposed by Marsuken features a state-anchored typology. The local business structure in this type of clusters is dominated by the presence of one or few large public or non-profit entities, such as universities, public research institutions, or military bases. The key public entities are typically surrounded by smaller firms/organizations, thus forming a hub-and-spoke structure similar to the second category in Figure 1. In fact, many fast-growing cities in US and overseas, to a large extent, owe their success to the presence of government-supported entities. For example, cities such as Madison, Ann Arbor benefit greatly from the presence of the state universities in region. Over the long run, the growth outlook for this type of clusters is very much dependent on two factors - the size and power of the dominating public entities within the region, and its capabilities of creating start-ups for an increasingly dynamic and diversified regional economy.

It is noteworthy that these above mentioned cluster classifications proposed by Markusen only establish a simplified framework for cluster researchers and cannot be applied to real-world cases in a simple approach. As acknowledged by Markusen, real-world clusters are often with characteristics of a mix of multiple categories. More importantly, clusters keep evolving over time thus may change typologies from one to another as they go through different stages of their lifecycle.

In Markusen’s classification, each category of clusters carries certain characteristics, which may be determining factors influencing their growth outlook over the long run. Based on the existing literature, however, it remains unclear whether the evolution of clusters tends to follow certain sequential stages which can be characterized by different cluster typologies. In our study, by examining the longitudinal typology changes for 15 US metropolitan city-

based hi-tech clusters, we seek a better understanding of the effect of cluster typology on the long-term evolution of cluster.

III. METHODOLOGY

A. Data Collection

The first step of our analysis is to identify 15 strong US metropolitan city-based clusters covering information technology, communications equipment manufacturing, and biopharmaceutical sectors. Our cluster performance data is obtained from the Cluster Mapping Project led by Michael Porter [3]. The project assembles a detailed account of the location and performance of industries in the United States, with a special focus on the linkages or externalities across industries that give rise to clusters. Economies are analyzed at various geographic levels, including states, economic areas, metropolitan areas, and counties. The economic performance of a technology cluster in a particular region at a particular year is measured by number of employment, number of job creation, and level of wages. For each of the three industries we are investigating, we select the top 5 best-performing clusters based on the number of cluster employment at year 2004. Geographically, our top-performing clusters are identified at the level of metropolitan area.

After identifying the 15 best-performing hi-tech clusters, we then observe the composition of each cluster and their changes over time. In this study, the composition of a cluster is determined by the distribution of cluster’s patent output by types of companies classified by Markusen [9]. The examination of relative patent contribution by different types of firms allows us to see the degree of importance of a certain type of firm to the regional cluster in innovation.

As suggested by Markusen’s model [9], the typology of a cluster is largely determined by the influencing power of the key cluster members within the region, which in this particular case is measured by the key firms’ relative patent contribution.

Traditionally, patent output has been widely used in many studies to measure the innovation performance of individual firms or geographical regions, though not without debate. A major weakness of this measurement lies in the fact that not all innovation activities can be patented. We do not believe this can be a severe weakness for our analysis, as all of the three industries being examined are high-tech sectors where the firms have a tendency to patent their innovations as much as possible.

The historical patent data in our study is obtained from the Patents BIB dataset issued by United States Patent and Trademark Office in 2006 [16]. Using the USPTO patent classification code, we identify the patents created in each of the three sectors based on the classification approach proposed by Jaffe [7].

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B. Data Analysis Our observations of cluster typology changes are made on

three sequential time windows, each covering 5 consecutive years. We examine the changes in cluster typology with a 5-year time window since that the typology of a cluster usually does not change dramatically in a short period. Observing the typology changes over a longer time period makes it easier to highlight the direction of changes in cluster typology over the long run.

Specifically, in each of the time windows, we identify the role of each category of firms supporting the regional cluster by measuring its 5-year cumulative patent output relative to the entire cluster throughout the same period. Specifically, for each of the 15 clusters, we first identify the top 10 patent-generating organizations on each individual time-window. Each of the identified key cluster members then falls into one the three organization categories based on its nature and relation to the regional cluster – locally headquartered company, externally headquartered company, or stated supported organization. For categorizing the identified key cluster members, we collected the background information for each of the key cluster members via Internet search.

With the data collected as the above, we are able to identify the backbone of a regional cluster and how its structure changes over time.

IV. ANALYSIS RESULTS

Based on the cluster performance data provided by Porter’s Cluster Mapping Project [3], we list the top 5 best-

performing US metropolitan city-based clusters in information technology, communications equipment, and biopharmaceutical sector individually in Table 1. The ranking in Table 1 is based on number of cluster employment measured in 2004. In Figure 2, 3, and 4, we use bar charts to break down the key components of the best-performing clusters in information technology, communications equipment, and biopharmaceutical sector, respectively. As described in Section 3, for each of the geographical technology clusters, the measurement has been made on three sequential time windows for capturing the longitudinal changes in cluster typology*.

Based on the visualization results, we can observe some interesting differences across the three sectors. First, the bars representing state-supported large organizations, appear to be significant only in the biopharmaceutical sector (see Figure 2). For example, in the 2 fastest-growing biopharmaceutical clusters (San Francisco-Oakland-Fremont, CA and Los Angeles- Long Beach-Santa Ana, CA), we note that the local universities including University of California, University of Southern California, and California Institute of Technology play an important role supporting the clusters’ innovation activities. This finding is well consistent with the previous studies which indicate that the development of life science technology is, to a large extent, dependent on public-funded academic research [17]. Compared with the biopharmaceutical clusters, the IT and communications equipment clusters are less dependent on public research organizations (see Figures 2 &3).

TABLE 1. RANKING OF THE BEST-PERFORMING TECHNOLOGY CLUSTERS IN 2004

Rank Information Technology Communications Equipment Biopharmaceuticals

1 San Jose-Sunnyvale-Santa Clara, CA

Los Angeles-Long Beach-Santa Ana, CA

�ew York-�orthern �ew Jersey-Long Island, �Y-�J-PA

2

Seattle-Tacoma-Bellevue, WA San Jose-Sunnyvale-Santa Clara, CA

Chicago-�aperville-Joliet, IL-I�-WI

3 San Francisco-Oakland-Fremont, CA

Chicago-�aperville-Joliet, IL-I�-WI

Los Angeles-Long Beach-Santa Ana, CA

4 Dallas-Fort-Oakland-Fremont, CA

�ew York-�orthern �ew Jersey-Long Island, �Y-�J-PA

Philadelphia-Camden-Wilmington, PA-�J-DE-MD

5 Boston-Cambridge-Quincy, MA-�H Los

Dallas-Fort Worth-Arlington, TX

San Francisco-Oakland-Fremont, CA

Second, we observe that the externally headquartered

large firms represent an important component for many fast-growing communications equipment manufacturing clusters (see Figure 3). For example, the Los Angeles-based communications equipment cluster, in its early stage of development, owe much of its performance to the presence of some large externally headquartered defense contractors including Hughes Electronic Devices, TRW, and Boeing, etc. Over time, it has successfully evolved into a more diversified cluster which consists of a large number of small or medium firms in multiple fields related to communications equipment

manufacturing. It actually became the largest US metropolitan city-based cluster based on level of cluster employment measured in 2004. *

* Since the charts in Figure 2, 3, and 4 are created using number of relevant patents granted to the clusters’ residents, the ranking of cluster performance indicated by the charts are different from the ranking shown in Table 1, which was measured by level of cluster employment.

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Figure 2. Breakdown of the top-performing IT clusters

Figure 3. Breakdown of the top-performing communications equipment clusters

0 4000 8000 12000 16000 20000

San Jose-Sunnyvale-Santa Clara, CA, 1991-1995San Jose-Sunnyvale-Santa Clara, CA, 1996-2000San Jose-Sunnyvale-Santa Clara, CA, 2001-2005

Seattle-Tacoma-Bellevue, WA, 1991-1995Seattle-Tacoma-Bellevue, WA, 1996-2000Seattle-Tacoma-Bellevue, WA, 2001-2005

San Francisco-Oakland-Fremont, CA, 1991-1995San Francisco-Oakland-Fremont, CA, 1996-2000San Francisco-Oakland-Fremont, CA, 2001-2005

Dallas-Fort Worth-Arlington, TX, 1991-1995Dallas-Fort Worth-Arlington, TX, 1996-2000Dallas-Fort Worth-Arlington, TX, 2001-2005

Boston-Cambridge-Quincy, MA-NH, 1991-1995Boston-Cambridge-Quincy, MA-NH, 1996-2000Boston-Cambridge-Quincy, MA-NH, 2001-2005

Number of patents by locally headquartered large firms Number of patents by externally headquartered large firms

Number of patents by state-supported large organizations Number of patents by small firms

0 1000 2000 3000 4000 5000

Los Angeles-Long Beach-Santa Ana, CA, 1991-1995Los Angeles-Long Beach-Santa Ana, CA, 1996-2000Los Angeles-Long Beach-Santa Ana, CA, 2001-2005


Chicago-Naperville-Joliet, IL-IN-WI, 1991-1995Chicago-Naperville-Joliet, IL-IN-WI, 1996-2000Chicago-Naperville-Joliet, IL-IN-WI, 2001-2005

New York-Newark-Edison, NY-NJ-PA, 1991-1995New York-Newark-Edison, NY-NJ-PA, 1996-2000New York-Newark-Edison, NY-NJ-PA, 2001-2005




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Another important observable finding from the charts is that the long-term growth of a hi-tech cluster is largely determined by the growth of small-and-medium size firms within the cluster. As can be seen in Figure 2, 3 and 4, most of the fast-growing clusters follow this trend, regardless of their type of industry. In practice, it suggests that none of the

extreme typologies proposed by Markusen [9] (e.g. hub-and-spoke, satellite platform, state-anchored) is able to maintain the cluster’s long-term growth, unless it evolves into a more dynamic structure which encourages small-and-medium size firms to grow within the cluster.

Figure 4. Breakdown of the top-performing biopharmaceutical clusters

Interestingly, although the fast-growing clusters owe

much of their success to the growth of small-and-medium size firms within the clusters, we observe that, in many cases, the local headquartered large firms remain important or become even more dominating within the regional clusters. Instead of showing the development of clusters in absolute patent numbers, Figure 5, 6, and 7, which are based on the normalized bars scaled to 100%, demonstrate the relative weights of different components within a cluster. When the

two sets of figures are put together for interpretation, we conclude that the development of a cluster is quite like a win-win game for both large and small firms. The recent development of the communications equipment manufacturing cluster in San Jose-Sunnyvale-Santa Clara, CA represents such an example, where both the key players and smaller ones were fast-growing. In practice, it suggests that an increasingly dynamic cluster brings no less benefit to large firms than to smaller ones.

0 1000 2000 3000 4000

New York-Newark-Edison, NY-NJ-PA, 1991-1995



Chicago-Naperville-Joliet, IL-IN-WI, 1991-1995



Los Angeles-Long Beach-Santa Ana, CA, 1991-1995



Philadelphia-Camdem-Wilmington, PA-NJ-DE-MD, 1991-1995



San Francisco-Oakland-Fremont, CA, 1991-1995





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Figure 5. Breakdown of the top-performing IT clusters – normalized scale

Figure 6. Breakdown of the top-performing communications equipment clusters – normalized scale

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Seattle-Tacoma-Bellevue, WA, 1991-1995Seattle-Tacoma-Bellevue, WA, 1996-2000Seattle-Tacoma-Bellevue, WA, 2001-2005

San Francisco-Oakland-Fremont, CA, 1991-1995San Francisco-Oakland-Fremont, CA, 1996-2000San Francisco-Oakland-Fremont, CA, 2001-2005


Boston-Cambridge-Quincy, MA-NH, 1991-1995Boston-Cambridge-Quincy, MA-NH, 1996-2000Boston-Cambridge-Quincy, MA-NH, 2001-2005

Number of patents by locally headquartered large firms Number of patents by externally headquartered large firmsNumber of patents by state-supported large organizations Number of patents by small firms

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

Los Angeles-Long Beach-Santa Ana, CA, 1991-1995Los Angeles-Long Beach-Santa Ana, CA, 1996-2000Los Angeles-Long Beach-Santa Ana, CA, 2001-2005


Chicago-Naperville-Joliet, IL-IN-WI, 1991-1995Chicago-Naperville-Joliet, IL-IN-WI, 1996-2000Chicago-Naperville-Joliet, IL-IN-WI, 2001-2005

New York-Newark-Edison, NY-NJ-PA, 1991-1995New York-Newark-Edison, NY-NJ-PA, 1996-2000New York-Newark-Edison, NY-NJ-PA, 2001-2005


Number of patents by locally headquartered large firms Number of patents by externally headquartered large firmsNumber of patents by state-supported large organizations Number of patents by small firms

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Figure 7. Breakdown of the top-performing biopharmaceutical clusters – normalized scale V. CONCLUSION AND POLICY IMPLICATIONS

Our analysis results suggest that the real-world clusters rarely feature any single type of typology; a mixed type of typology is much more prevalent in reality. As clusters go through different stages of their lifecycle, their typologies may change as well. Our study indicates that a cluster may characterize one of the prototypical models proposed by Markusen [9], for example a hub-and-spoke type, at its initial stage of development. However, over the long run, the success of a cluster is largely determined by the growth potential of its small-and-medium firms. Indeed, the growth of small-and-medium firms could in turn promote the development of the pre-existing hub-firms thus making the cluster more dynamic. Therefore, for cluster policy makers, one of the top priorities is to help develop the local entrepreneurship and enable the smaller firms, we refer to as “toddlers” to benefit from the available resources within the cluster. In this regard, the significant success of Silicon Valley and the recent trouble for Detroit confirms our observation.

This paper represents a work in progress. As described in section 2, the development of a cluster is influenced by several internal and external factors. In this study, we only examined the changes in cluster typology and its implication for clusters’ long-term evolution. Another key factor we plan

to incorporate in our future study is the patterns with which cluster members interact and cooperate with each other.

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Philadelphia-Camdem-Wilmington, PA-NJ-DE-MD, …








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[13] Saxenian, A., 1994. Regional advantage: culture and competition in Silicon Valley and Route 128. Harvard University Press, Cambridge, MA.

[14] Scott, A. 1986. High tech industry and territorial development: The rise of the Orange Count complex, 1955-1984. Urban Geography 7:3-45

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