ROLAND BERGER STRATEGY CONSULTANTS
UNIVERSITY OF GRONINGEN
Clusters of competence
and Dutch policy
Developing clusters to stimulate the
national economy
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Executive summary
The goal of this research is to provide more insight in how clusters work, what their key factors are and to reflect on cluster building in the Netherlands. The findings are that the output of a cluster is threefold:
1. A cluster stimulates productivity
2. A cluster increases the pace of innovation 3. A cluster increases new businesses formation
What drives a cluster is a mechanism that, through several positive feedback loops, stimulates its own growth. This mechanism consists out of five self-augmenting factors: Interaction, Knowledge development, Labour force, Venture Capitalists and Innovation. This is shown in the figure below.
4 be flexible enough to respond to a changing environment and to cope with new developments. That way, a cluster can remain competitive for many decades.
Both the model of the development of a cluster and the model of the mechanism are applied in four case studies: Silicon Valley, Swiss private banking, Medicon Valley and Detroit automotive. Based upon these case studies several key success factors have been identified. Next the Dutch policy is studied. What is striking is that a long term strategy is lacking. Also coherence and focus between national instruments could be improved. Based on this a seven-step plan is created with which the way clusters are stimulated in the Netherlands could improve:
1. Be aware of what clusters actually are: Geographic hotspots of firms and
institutions working closely together within a geographic scale of approximately 50 km across.
2. Make a small selection of clusters to focus on. Choose these clusters based upon their current strength and upon their potential.
3. Identify factors which are limiting the development of a cluster and identify chances to stimulate development of a cluster.
4. Design regulations and/or stimulation means specifically for the cluster so that the cluster could enjoy maximum benefit.
5. Make sure coherence between national instruments returns so that every instrument serves its specific goals and money gets spent efficiently.
6. Commit to policy for approximately 10 years, so that the clusters are given the time to fully develop into a self-sufficient and competitive cluster.
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Content
1. INTRODUCTION 6
2. IMPORTANCE OF CLUSTERS 8
GENERAL IMPORTANCE 8
IMPORTANCE FOR THE NETHERLANDS 11
CONCLUSION 14
3. DEVELOPING A SUCCESSFUL CLUSTER 15
CLUSTER MECHANISM 15
CLUSTER DEVELOPMENT 23
FOUR CASES 31
SILICON VALLEY 31
SWISS PRIVATE BANKING 39
MEDICON VALLEY 46
DETROIT AUTOMOTIVE 51
CONCLUSION 55
4. DUTCH POLICY 57
KEY-AREA APPROACH 57
ACTUAL USE OF STIMULATION MEANS 58
CONCLUSION 64
5. CLUSTER DEVELOPMENT AND POLICY REVIEWED 65
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1. Introduction
In today's global world competition has also become more global. In this global economy, the most competitive firms are not hindered by national borders in serving their customers. Therefore also economic efficiency instead of just market access has become important for these companies in choosing their location (Porter & Schwab 2008). Because of this, a concept that has gained renewed attention is that of clusters. Clusters have gained in importance as they provide a productivity advantage. (Porter & Schwab 2008, Porter 1998). In 1890 Marshall already mentioned the strategic importance of clusters. However, the subject did not really take off until a century later, starting with the writings of Porter (1990) and Krugman (1991) (Romero-Martínez & Montoro-Sánchez 2008).
According to Romero-Martínez & Montoro-Sánchez (2008) the most widely accepted definition of clusters is the one from Michael Porter (1998): 'Clusters are geographic concentrations of interconnected companies and institutions in a particular field.' Concerning the scale of the geographic concentration, Duranton & Overman (2005) find that this localization mostly takes place at small scales below 50 km. In the title of this thesis the words 'clusters of competence' are used to refer to such clusters, where a certain region excels in a certain field of competence. These words are used in the title to avoid any misunderstanding about what this thesis is about.
Romero-Martínez & Montoro-Sánchez (2008) mention that, even though the area of cluster research has gained renewed interest, more research is still needed about questions like what clusters are, and what their key factors and principal effects are on company efficiency and economic development.
The goal of this research is to provide more insight in how clusters work, what their key factors are and to reflect on cluster building in the Netherlands. Therefore the following research question is formulated:
7 Along with this research question seven questions are formulated. These sub-questions will be discussed sequentially, after which it will be possible to answer the main research question.
1. How do clusters influence the economy of a country?
2. In which way and to what extent can the Dutch economy profit from clusters? 3. What makes a cluster work? What is the mechanism driving a cluster?
4. How does the development of a cluster take place?
5. Applying the newly created models, what key success factors can be identified in real-life clusters?
6. How do the Netherlands perform in stimulating cluster-build-up?
7. Do the hypotheses, key success factors and conclusions about Dutch cluster-stimulation hold up if reflected against the clearly recognizable Dutch cluster of Brainport Eindhoven?
Sub-questions one and two will be discussed in chapter two. The methodology used in this chapter consists out of a qualitative study of theoretical literature on clusters and a qualitative study of analyses on the current Dutch economy. Sub-questions three, four and five are discussed in chapter 3 by conducting a qualitative study of theoretical literature on clusters and case studies of several clusters. Sub-question 6, discussed in chapter 4, will be answered by a qualitative study of previous analyses on the Dutch policy. In chapter 5, sub-question 7 will be discussed by conducting another case study. Finally in chapter 6, the main question will be answered and recommendations will be made on how to improve cluster build-up.
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2. Importance of clusters
This chapter will deal with sub-questions one and two: 1. How do clusters influence the economy of a country
2. In which way and to what extent can the Dutch economy profit from clusters? The reason why these questions are dealt with first is that, before opening up the black-box of a cluster, the importance of a cluster in general and the importance for the Netherlands will be explained to underline the relevance of this research. At first, the general importance of clusters will be discussed, after which this will be linked to the Netherlands.
General importance
Clusters consist out of interconnected companies and institutions in a particular field. It therefore allows each member to benefit as if it had greater scale or as if it had joined with others formally-without requiring it to sacrifice its flexibility. (Porter 1998) This is because clusters can affect competition in three ways (Porter, 1998 & 2000):
1. The productivity of companies situated inside a cluster is improved. 2. Clusters drive the direction and pace of innovation.
3. The formation of new businesses is stimulated by clusters.
Next there will be elaborated on each of these three effects. However, since most effects of belonging to a cluster come from cluster key factors (Romero-Martínez & Montoro-Sánchez, 2008) a high level of aggregation will be maintained in this paragraph. The key factors of clusters will be explained in dept in chapter 3.
Productivity
Productivity refers to the relationship between the output of goods and/or services and the inputs of resources used to produce them. In other words, the more efficient resources can be gathered, acquired or used in producing a certain output, the higher productivity is. Some of the main reasons for an increase in productivity of companies situated in a cluster are (Porter 1998):
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Companies experience better motivation and performance measurement because of the high level of local rivalry. For example, because of pride and desire to look good in the local community, employees get extra motivation to work well and hard.
Companies have better access to institutions and public goods. For example investments by government in specialized infrastructure in a cluster can benefit productivity of companies in that cluster.
Companies can be complementary to each other resulting in a productivity increase. All the linkages between companies in a cluster can result in a whole greater than the sum of its parts. A good example can be seen in the tourism industry where tourist experience not only depends on the primary attraction, but also on nearby hotels, restaurants etc. Because of such complementarities it can also become more attractive to buy from companies within a cluster since visiting buyers can see many suppliers in a single trip. Also the perceived risk of buying might be lower because of the alternatives offered by a cluster. This leads to a reduction in the required efforts and resources to attract customers, making the productivity increase.
There has also been empirical research on the link between clusters and productivity. Cortright (2006) made a review on the different empirical studies. He mentions the studies of Henderson (1997 & 2003) and Nakamura (1985). These studies found clear evidence on the increased productivity of companies in a cluster.
Rosenthal & Strange (2003) also mention many different studies on this relationship. The actual number differ per study, which can be contributed to logical differences per industry type, but all studies show a positive influence of clusters on productivity.
Innovation
10 Besides that innovation itself is an output of clusters, innovative activity and output are, as Baptista & Swann (1998) mention, closely associated with firm entry and productivity growth
There has been some quantitative research on the relationship between clusters and innovation.
Baptista & Swann (1998) have analyzed whether firms located in strong industrial clusters or regions are more likely to innovate than firms outside such regions. They studied the innovative record of 248 manufacturing firms in the UK during the years 1975–1982. These records were related to employment in the region where they were located, and to other variables. Their results show that a firm is considerably more likely to innovate if own-sector employment in its home region is strong.
Ibrahim & Fallah (2005) analyzed the importance of a company's environment for inventors of that company. They conducted a survey among 230 inventors in the U.S. telecommunications industry, of whom 109 replied. The survey showed that inventors situated in clusters attribute much of their success to the environment of their organizations that provided opportunities for interactions with other researchers and access to their tacit knowledge. On the other hand inventors who were not in the clusters generally rated the influence of their geographical area of no influence or of very little influence.
Formation of new businesses
Within a cluster the formation of new businesses is stimulated because (Porter 1998):
The barriers to entry are lower than elsewhere. Needed assets, skills, inputs, and staff are often readily available at the cluster location, waiting to be assembled into a new enterprise.
The risk associated with starting a new business is perceived lower in cluster. For new suppliers for example because of the concentrated customer base present in a cluster they will have good access to customers. Financial institutions and investors already familiar with the cluster may also require a lower risk premium on capital.
11 In an empirical study of clustering in the US and UK computer industries, Baptista & Swann (1999) have found that strong clusters are more likely to attract new entrants. Besides Romero-Martínez & Montoro-Sánchez (2008) underline in their article the existence of this relationship.
One might argue as an argument against clusters that because of the many companies gathering resources in a cluster, these resources will become scarcer and therefore more expensive. Companies however have the alternative of outsourcing inputs from other locations. This puts a limit to these cost penalties. Besides that, clusters do not only increase the demand for specialized inputs, but they also increase their supply (Porter 1998).
Importance for the Netherlands
Because of their output, clusters are especially important for countries at an advanced stage of development, where the more basic sources of productivity improvements have been exhausted to a large extent (Porter & Schwab 2008). Currently, as is shown in table 1, the Netherlands are rated eighth out of 134 countries in the Global Competitiveness Index (GCI). Thus it is obvious that the Netherlands are at an advanced stage of development.
Rank Country Score (scale 1-7)
1 US 5,74 2 Switzerland 5,61 3 Denmark 5,58 4 Sweden 5,53 5 Singapore 5,53 6 Finland 5,50 7 Germany 5,46 8 Netherlands 5,41 9 Japan 5,38 10 Canada 5,37
12 Back in 2000 however, the Netherlands were rated fourth. As is shown in figure 1, they were ranked above Switzerland, Denmark and Sweden. Since 2002 however, these countries, which are as well as the Netherlands located in Western Europe and relatively small in size, have been outperforming the Netherlands. Since there is no apparent structural reason why the Netherlands could not outperform these countries, they are not taking full advantage of its potential.
Figure 1: GCI ranks over time (Global Competitiveness Reports 2000-2008)
The GCI as a tool to analyse a country's competitiveness has its limitations though, which will be discussed below.
The methodology has undergone some major changes during the past eight years. The GCI has evolved as an instrument, taking more variables in account after every change in order to make it more accurate. Because of this a study on the actual score of the Netherlands over time would be irrelevant since the actual score changes along with the changes in the methodology. However as is shown in figure 1, the rankings over time are still interesting, since it represents a benchmark between countries which all have been subject to these changes in methodology.
Besides hard data, a major source of data is the World Economic Forum‟s Executive Opinion Survey. In this survey top management business leaders are asked to provide their expert opinions on various aspects of the business environment in which they operate. The number of respondents in the Netherlands in the 2008 survey was 89, slightly below the average of 91
13 respondents per country. Because of this, the GCI does not provide a watertight explanation for differences between countries. However, the GCI score is determined by the score on twelve pillars and the score of each pillar is determined by on average nine data sources or Executive Opinion Survey question. Since variance drops with the increase in sources, the GCI allows analysis to a certain level. In this thesis analysis will be restricted to the pillar level to see on which aspect the Netherlands are slacking in comparison to other countries.
To find out what the major reason is why the Netherlands are not ranked at the top a comparison is made to the scores of Switzerland, Denmark and Sweden. These countries are chosen for the comparison since these countries share basic characteristics (size, culture, continent) with the Netherlands. The US for example would be less interesting in a comparison to the Netherlands since their score is strongly influenced by their enormous domestic market. Singapore would be less interesting because of the different culture in Singapore and the totally different location of Singapore in the world.
In figure 2 the origin of the lower score of the Netherlands is shown. This is calculated by subtracting the average of the total scores of Switzerland, Denmark and Sweden from the total score of the Netherlands, giving a difference of -0,16. Next, the average weighted scores of Switzerland, Denmark and Sweden on all twelve pillars were subtracted from the Dutch scores on all pillars. These calculations, shown in appendix 1, show that the low score on innovation is most responsible for the lower
14 score of the Netherlands. So in other words, boosting innovation in the Netherlands would be the best way to increase the competitiveness of the country. As is described in the previous paragraph this is exactly what happens in clusters: the pace of innovation increases. Because of this, because clusters especially important for countries at a more advanced stage of development and because in today's world economic efficiency instead of just market access has become important for companies in choosing their location, clusters are very important for the Netherlands nowadays. Keep in mind that, although clusters are important for the Netherlands mainly through the boosting of innovation, this does not imply that clusters are the only means to achieve higher levels of innovation in the Netherlands.
Conclusion
In this chapter, questions one and two were discussed. The answer to sub-question one is that clusters influence the economy of a country in three ways:
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3. Developing a successful cluster
After having underlined the importance of clusters in the previous chapter, this chapter will focus on what a cluster actually is, how it works and how a cluster develops. Therefore sub-questions three to five will be answered in this chapter:
3. What makes a cluster work? What is the mechanism driving a cluster? 4. How does the development of a cluster take place?
5. Applying the newly created models, what key success factors can be identified in real-life clusters?
At first a model explaining the mechanism driving a cluster will be developed, making clear what a cluster actually is. After that, the way a cluster, with this mechanism, is developed will be grasped in a model. Finally, in this chapter, these models will be applied in four case studies to test the models and to gain more insight into how clusters develop in real-life.
Cluster mechanism
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Self-Augmenting processes
1. Buyer-supplier relations
2. Choice of co-location with other firms 3. Cooperation among firms
4. Interaction with public education and research 5. Accumulation of local human capital
6. Interaction with local public opinion 7. Interaction with local policy makers 8. Interaction with local venture capitalists 9. Inter-industrial spillovers
10. Intra-industrial spillovers 11. Spin-offs
12. Support of start-ups by firms
Table 2: 12 self-augmenting processes involved in cluster development (Brenner & Mühlig, 2008)
As the word self-augmenting already reveals, in a working cluster the factors driving a cluster occur in a positive feedback loop. This is already pointed out by Porter in 1998 when he mentioned that the formation of new businesses is stimulated within a cluster, while at the same time the cluster itself is also stimulated by the formation of new businesses. This is because an expanded cluster amplifies all the benefits. It increases the collective pool of competitive resources, which benefits all the cluster's members (Porter, 1998). Baptista & Swann (1998) also mention the reinforcing nature of the process: 'clusters are generated and reinforced by a positive feedback process based on a set of advantages that arise from the geographical agglomeration of industrial activities. Benefits from locating in an industrial centre increase as more new firms locate there, then a process of positive feedback and lock-in will result.' In literature however many authors have filtered out different processes in defining what these reinforcing processes are. Brenner & Mühlig (2008) identified twelve processes, shown in table 2. Engelsoft et.al. (2005) contribute the success of clusters mainly to three factors:
1. local pool of skilled labour,
2. pecuniary external economies arising from specialisation and from economies of scale in supplying firms,
17 Romero-Martínez & Montoro-Sánchez (2008) filter out the following four factors as especially important:
1. specialized workforce, 2. knowledge spillovers,
3. competition between companies and institutions 4. cooperation between companies and institutions
In this thesis, the model shown in figure 3 is used to describe the processes in a cluster. This model covers the processes mentioned by the authors discussed above. Besides, innovation as a process is added. This is because innovation is one of the most important outputs of a cluster and innovation also has a major positive influence on a cluster. Further explanation on the role of innovation and of the other factors is given below.
Figure 3: Cluster mechanism
Interaction
In a cluster there are many possible kinds of interaction between many different actors. Therefore six major forms of interaction are highlighted to clarify what is meant by interaction and how it is a self-augmenting process.
18 (2008) at point one. As they describe, in Marshall's work (Marshall 1920) it is already mentioned that the presence of many similar firms in an area attracts suppliers to this area. This is because of the large customer base present in a cluster and because the proximity of these customers eases the supply of complementary services. It also works the other way around since using nearby instead of distant suppliers lowers transaction costs, reduces inventory size and lowers the risk of opportunistic behaviour since local reputation is important (Porter 1998). This leads to growth of a cluster, which in turn will amplify these advantages causing a positive feedback loop
Interaction with competitors. Compared to the interaction in the supply chain described above, which mentions vertical connections, this interaction is focused on horizontal interaction: Cooperation and competition with competitors and interaction with unrelated firms. First of all the high level of competition between competitors in clusters stimulates the performance of companies (Porter 1998). This increase in performance will cause companies in a cluster to outperform companies outside a cluster, stimulating the cluster itself. The bigger a cluster gets, the bigger competition in a cluster gets. Therefore another positive feedback loop exists with this interaction.
Interaction with public institutions. Another form of interaction within clusters is that between companies and public institutions like universities, colleges, hospitals, etc. For example, existing public education and research facilities can be valuable partners for firms. Besides, the interaction between such public education and research facilities and companies might positively influence public spending or it might direct the focus of education and research to a relevant one for these firms (Brenner & Mühlig 2008). When this happens, the cluster will become more attractive for other firms active in the same field causing a positive feedback loop.
19 also given by Brenner & Mühlig (2008), is that the existing firms might be role models for new entrepreneurs in the region. Again, along with the growth of a cluster do these processes intensify, causing another positive feedback loop.
Interaction with policy makers. The same way as public opinion can get influenced by a cluster can local policy makers get influenced. If there are more or larger firms of a specific sector in their region, this sector might receive increased priority from the policy makers, since the sector is beneficial for the region. This way it gets more likely that investments by the local government will be especially beneficial for the sector and that the sector receives increased support from the local government (Brenner & Mühlig 2008). Along with the growth of a cluster does this process strengthen.
Interaction with new firms. This interaction is also mentioned by Brenner & Mühlig (2008). Actively supporting start-ups through various ways will stimulate growth of a cluster, besides it is more likely to occur in well-developed clusters.
Labour force
A local pool of a skilled and specialized workforce in a region was already mentioned back in 1890 (Marshall 1920) as one of the key factors of a clusters. Companies in a cluster benefit from this local pool of skilled and specialized workers through a reduction in sourcing and recruitment cost and through the good availability of the qualified workforce (Romero-Martínez & Montoro-Sánchez 2008). On the other hand, clusters also attract individuals with the necessary skills. They are drawn to a cluster because the region offers many employment opportunities and the risk of relocating when they change jobs is strongly reduced (Romero-Martínez & Montoro-Sánchez 2008).
20 Knowledge development
In a cluster there is an increased flow of information. Back in 1890 Marshall (1920) referred to this phenomenon as 'mysteries in the air'. Nowadays it is often referred to as knowledge spillovers (Engelsoft et.al. 2005). This flow of information is increased due to the high level of interaction within clusters (Engelsoft et.al. 2005). Besides, it is reinforced by the afore mentioned high level of job mobility (Brenner & Mühlig 2008). There has also been empirical research on these knowledge spillovers. Although there has been a discussion on methodological issues, Henderson et. al. (2005), Thompson & Fox-Kean (2005) and Thompson (2006) do find the evidence to agree on the existence of these spillovers. The effect of knowledge spillovers on clusters, as Henderson et.al. 2005 describe, is that they provide incentives to co-locate since information is more easily available. On the other hand, the existence of co-location itself may encourage 'cross-pollination'.
In this thesis however, instead of referring to knowledge spillovers, the term knowledge development will be used. Although a more general term, it is more suitable since the increased knowledge development in clusters is much more a driving force then the knowledge spillovers. It is true that, since these knowledge spillovers make information more easily available, it will draw new companies to the cluster. However, the knowledge spillovers themselves do not provide major advantages to existing companies. These spillovers make sure that the right information will reach the right persons, making it more likely that the knowledge will also be developed. In other words, knowledge spillovers is the means through which interaction and knowledge development are linked.
Moreover, a cluster also consists out of research institutions like universities and laboratories.
This knowledge development represents an augmenting process, since it will stimulate growth of the cluster while at the same time, the bigger a cluster gets, the higher the level of knowledge development will get through the increase in knowledge spillovers and through the increase in (the size of) research institutions
Venture capital
21 will be high. It also works the other way around, since when the available venture capital in a region increases, it will be easier to acquire the required capital to start or to expand a new company. Brenner & Mühlig (2008) also note that venture capitalists often focus on a certain industry that fits the industrial structure in a region. All in all, when a cluster grows, more venture capitalists are attracted and when more venture capitalists locate in a cluster, the more growth of the cluster is stimulated. Innovation
As is described in chapter 2, innovation is one of the most important outputs of a cluster. Main arguments brought up were that companies can experiment at lower costs, they can acquire certain resources required for the implementation of innovations easier and more quickly since suppliers, partners and/or customers are more nearby. Also the competitive pressure within clusters itself stimulates companies to innovate. In this paragraph the relationship between clusters and innovation will be handled in more depth, by looking at five stylised facts about the fundamental nature of the innovation process, presented by Dosi (1998) and used by Feldman (1994) and Baptista & Swann (1998). These five stylised facts are uncertainty, complexity, reliance upon basic research, importance of learning-by-doing and cumulativeness. Below there will be elaborated on these stylised facts and their relation to clusters.
Uncertainty and complexity. As Baptista & Swann (1998) describe, the fact that the outcome of an innovative process lies in the future and cannot be known in advance makes the process uncertain and complex. By the formation of channels for exchange of information, such as networks of innovators, this uncertainty can be reduced. Besides, by joining in on such a network a firm can timely exploit developments in technology and facilitate problem-solving tasks by the sharing of experience in dealing with similar technologies. Baptista & Swann (1998) also mention that localised networks will be more durable than formal, international strategic alliances. This is because the proximity and frequent contact with people in such networks causes social aspects of relationships or partnerships to be more important, preventing opportunistic behaviour.
22 knowledge forms the base of industrial innovation. As described above, in a cluster there is increased knowledge development since the proximity within clusters to research institutions and other companies gives quick access to individuals that can develop information into usable knowledge in a timely manner. This makes having commercial control over a technology easier and faster (Baptista & Swann 1998).
Importance of learning-by-doing. The fourth stylised fact about innovation is the importance of learning-by-doing. As described in chapter 2, the proximity of competitors, suppliers, customers and providers business services makes it easier to experiment. Besides, these people or companies might have certain experiences themselves which, because of the knowledge spillovers, are more likely to be shared in clusters.
Cumulativeness. Finally, technological innovation often builds upon opportunities that arise around previous scientific advances. The direction of innovation is therefore often influenced by the latest successful technologies. Therefore in regions that accumulate high levels of innovative success information for the next round of innovation is already assembled (Baptista & Swann 1998).
Especially the stylised fact cumulativeness shows the positive feedback loop that also occurs with innovation: regions with high levels of innovation are likely to, because of that, maintain this high level. Besides, the second way a cluster profits from innovation is that, since innovation is the source for future productivity growth, the companies themselves profit from their innovations.
One might notice that Brenner & Mühlig's augmenting factors two 'Choice of co-location with other firms' and three 'Spin-offs' do not seem to be covered by the model. This is because these factors represent ways through which a cluster can grow and not necessarily processes stimulating cluster growth. Brenner & Mühlig themselves also mention that if other factors are considered the location of competitors does not show up in the list of relevant factors (Brenner & Mühlig 2008).
23 positively stimulated in clusters and they all positively influence the cluster itself. The logical question that might arise now is what does it start with? For example: does a certain local pool of skilled labour starts cluster development or is it the other way around? This causality dilemma however is one like: 'what came first, the chicken or the egg?' Therefore the next paragraph will focus on the development of clusters. Most important in this paragraph is to understand the mechanism driving a cluster and that as soon as all the gearwheels are spinning, many reinforcing processes stimulate the growth of a cluster.
Cluster development
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Figure 4: Four phases of cluster development
Substantial mass build-up
The roots of a cluster can often be traced back to historical circumstances (Porter 1998, Ketels 2003). As an example Porter mentions the Dutch harbour in Rotterdam, which has its roots in Holland's long maritime history, Holland's central location within Europe and the extensive network of waterways. So over time a certain mass of companies, institutions/knowledge and skilled labour in a specific sector has been formed in a region. As Enright (1998) points out such a mass is a prerequisite in order for a competitive cluster to be developed. In other words, in order for triggering events in a region to have effect, a certain mass of companies and institutions is needed. In literature such a mass is usually referred to as critical mass (OECD 1999, Andersen et. al. 2006). However, the term is also used to refer to the point where some augmenting factors start occurring (Menzel & Fornahl 2007, Ketels 2003) and to the point where a cluster has become self-sustaining (Maggioni 2004, Brenner & Mühlig 2008, Romero-Martínez & Montoro-Sánchez 2008). Enright (1998) can be considered to be in between these last two groups.
25 During this first phase of cluster development, besides the build up of the substantial mass, other prerequisites for clusters also develop or grow. As mentioned in the beginning of this chapter, Brenner & Mühlig (2008) also identified a number of prerequisites for clusters. These are shown below in table 3.
Prerequisites 1. Qualified labour 2. Networks
3. Universities and public research 4. Tradition and historical preconditions 5. Industrial structure 6. Local policies 7. Culture 8. Geographical location 9. Local demand 10. National policies 11. Suppliers 12. Transportation infrastructure 13. Quality of life
14. Local capital market 15. Wages
16. Type of region 17. Technology parks
Table 3: 16 prerequisites required for cluster development (Brenner & Mühlig, 2008)
Of these 17 prerequisites, numbers 1,2,3,5,9,11,12,14 and 17 comprise signals of a substantial mass. This leaves open the following prerequisites. Before discussing them it is important to notice that the term 'prerequisites' is a little misleading. Instead of these factors all being necessary for a cluster to develop, it is more like the more of these factors are present, the bigger the chance of successful emergence of a cluster. Besides it may be obvious that the importance of each of these prerequisites differs per industry, region or country.
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Culture. The norms and social institutions at the time of the emergence of the local cluster are also important (Brenner & Mühlig 2008). For example, in a region with a culture conducive to innovation and entrepreneurship a cluster is more likely to emerge (Engelsoft et.al. 2006, OECD 1999). Porter (1990) also argued that attitudes towards self-employment, cooperation or innovation are important for local clusters (Brenner & Mühlig 2008).
Local and national policies. Focus here is on policies that were in place before the emergence of a local cluster and that influenced this emergence. An example is the public support by either local or national government of the sector through advantageous regulations, subsidies, etc. Policies that were specifically aimed at triggering cluster growth are excluded here (Brenner & Mühlig 2008).
Geographical location. Certain specific geographic characteristics of a region also have its influence on cluster development. Such factors are the presence of natural resources, the access to a natural transport infrastructure like waterways and the location in relation to other regions. It seems that natural resources played an important role for the location of industries in the past, for example for the metal industry in the Ruhr area in Germany, but that they have lost most of their importance in recent years. (Brenner & Mühlig 2008)
Quality of life. The quality of life in a region is an important factor in attracting people to a region. This is because highly skilled people prefer to live in regions with a high quality of life. Therefore areas with a high quality of life also attract firms. Brenner & Mühlig (2008) describe the example of Munich, which is considered in Germany as the best location for people to live in. This strongly stimulated the growth of several sectors in the region.
Wages. The general level of wages is especially important for cluster development in developing countries. In these countries the emergence of certain clusters were mostly contributed to the low wages in the region (Brenner & Mühlig 2008).
27 Triggering events
A certain substantial mass in a region does not automatically grow out into a cluster, it merely provides the potential for a cluster. Certain events or actors are needed to make use of this potential. These events are called trigger events (Brenner & Mühlig 2008). As such events Porter (1998) mentions that clusters may arise around one or two innovative companies. He also mentions a certain chance event as a possible trigger to cluster growth, like the telemarketing cluster in Omaha, Nebraska which owes much to the decision by the US Air Force to locate the Strategic Air Command there (Porter 1998). Brenner & Mühlig (2008) identified six triggering events, including these two. They are shown in table 4.
It is important to notice that cluster development takes a relatively long time. As Porter (1998) points out: 'Numerous case studies suggest that clusters require a decade or more to develop depth and real competitive advantage'. Ketels (2003) also confirms this. He also points out that actions of regional leaders, that spotted potential for a cluster, have speeded up this process with some clusters.
Triggering Events
1. Promoting activities 2. Specific policy measures 3. Historical events
4. Specific innovations 5. Founding of leading firms 6. Chance
Table 4: 6 triggering events stimulating cluster development (Brenner & Mühlig, 2008)
Promoting activities. Brenner & Mühlig (2008) describe that when people or entrepreneurs develop a vision for the region or only for their own businesses and put this into practice, this can play a crucial role in the emergence of local clusters. They do mention however that the theoretical discussion on this issue is restricted on the issue of entrepreneurs shaping their local environment. Nevertheless, activities to promote a sector in a region does positively influence the emergence of a cluster
28 subject of cluster has gained renewed attention, as was already mentioned in the introduction, it has also become popular among policy makers to actively create or support the emergence of clusters (Brenner & Mühlig 2008).
Historical events. Historical events, like wars, have influence on the emergence of local clusters. Such events can change global or local conditions forcing firms to relocate. At their new locations they might function as initiators of further developments (Brenner & Mühlig 2008). Moreover events like wars have major influence on technological innovation and demand for technical products.
Specific innovations. As is mentioned in the previous paragraph, one of the stylised facts of innovation is cumulativeness. Therefore large initial innovations often trigger successive innovations in the same region. These successive innovations provide firms a competitive advantage causing the emergence of a local cluster (Brenner & Mühlig 2008).
Founding of leading firms. Also mentioned by Porter (1998) this trigger event refers to the fact that the emergence of a cluster also can be due to one or two successful firms, from which many new companies spin off. Besides, these major companies can feed the growth of the smaller ones (Brenner & Mühlig 2008).
Chance. Brenner & Mühlig (2008) mention this as a trigger event since in some case studies there was no detailed description of the trigger events. In those case studies mostly chance was appointed as the reason of the emergence of the cluster. As mentioned on the previous page Porter (1998) also mentions chance events, referring to the telemarketing cluster in Omaha, Nebraska. Based on this example this trigger event can be explained as that sometimes a certain event can have as a 'lucky' side effect that it unintentionally triggers a cluster.
Self-augmenting growth
29 Congestion
Clusters can remain centres of innovation and prosper for decades. Some clusters even remain competitive locations for centuries (Porter 1998, 2000). However, clusters do have their threats. As a cluster grows, so do effects of congestion in the region. These effects are, for example, that the infrastructure in a region gets busier and that land prices rise. Because of this transaction costs and costs for the location increase. These effects increase along with growth of the cluster, causing a negative feedback loop and eventually slowing down cluster growth (Maggioni 2004, Menzel & Fornahl 2007).
30
Figure 5: Decline as the fifth phase of cluster development
This decline can be avoided however. When a cluster remains heterogenic and diverse enough, they can avoid such a negative lock-in and remain flexible enough to respond to new, changing circumstances. Therefore, the disruptive technologies can be embraced by companies within the cluster giving a new impulse to the cluster. Besides, the negative effects of congestion can also partially be dealt with by investments in infrastructure. Therefore, as soon as a cluster gets older new impulses can stimulate the growth of a cluster again making it enter a new cycle, as is shown in figure 6.
31
Four cases
In this paragraph the two models presented in the previous paragraph will be applied to existing clusters. Out of the following three clusters the process of development and the current augmenting factors at work will be discussed.
Silicon Valley in California, USA,
Private Banking in Zurich, Switzerland
Medicon Valley, Øresund region, Denmark & Sweden
These three clusters are chosen since they are well known in the world and clearly identifiable. Moreover these three clusters belong to the top four countries in the 2008 GCI, as was discussed in chapter 2.
A fourth cluster that will be studied is the automotive cluster in Detroit, USA. However, of this cluster not the development itself will be discussed, but just its decline over the past 50 years. This cluster is chosen since it is also a well known and well identifiable example of a cluster and it is also a very well known cluster to be experiencing difficulties.
Silicon Valley
The region in Santa Clara County from Palo Alto to San Jose was first given the name Silicon Valley by Electronic News editor Don Hoefler in 1971 (Moon Lee et.al. 2000, Norr 1999). So by that time, the cluster 'Silicon Valley' was already thriving as an innovative region. It took some time however for the region to get there. Below at first it will be discussed how a substantial mass was developed in that region. Next the trigger events that accelerated the growth and made the region grow out into a thriving cluster will be described. After that the evolution that Silicon Valley went through since then will be discussed. Finally there will be looked at the augmenting factors that are currently forming the mechanism driving the cluster.
Substantial mass build-up
32 Telegraph Company (FTC). The product with which Elwell started was the Poulsen Arc, an originally Danish invention, which he brought to the US. This became a very successful innovation in the US, making the FTC quickly grow into the area's largest electronics business. The FTC later scored some other significant advances in the development of radio technology. One of these developments was the vacuum tube, by Dr. Lee deForest, Elwell's assistant during the founding of the FTC. This tube, which he perfected in 1912, was also a very successful innovation, succeeding the Poulsen arc as the dominant radio technology and forming the basic technology of earliest computers. (Sturgeon, 2000, Norr 1999) As may be obvious, Stanford University played a major role in the development of the region. Besides the fact that they provide high quality education, they also stimulated entrepreneurship among their students and they actively cooperated with local industry. This is because, as AnnaLee Saxenian (1994) describes: 'Stanford's leaders had, unlike MIT, no corporate or government ties or even easy proximity to Washington.' In 1946 this resulted in Terman founding the Stanford Research Institute where applied and contract research was conducted, strengthening the cooperation with Stanford and the local industry. Along with the region profiting from these innovations and the entrepreneurship taught at Stanford, the region also profited from the US Navy, which was situated in the Bay Area. This is because the navy was a major customer and because the navy demanded increasingly better products, stimulating innovation. In 1939 a federal agency known as the National Advisory Committee on Aeronautics, forerunner of NASA, also began building its West Coast laboratories in the region fulfilling the same role as the navy. (Sturgeon, 2000, Norr 1999)
So by the end of the 1940s the region had a well developed industry in electronics, some major customers for these products and several research institutes including a high quality university like Stanford, altogether constituting a substantial mass.
Trigger events
33 \electronics manufacturer, Raytheon, to commercialize the invention. This drove him back to his place of birth, Palo Alto, where he gained support from Beckman Instruments, a Southern California manufacturer of biomedical devices, to set up Shockley Semiconductor Laboratory in 1955. Shockley however had a very unpleasant management style with, for example, the use of lie-detector tests. This made eight of his team members leave Shockley Semiconductor in 1957 and set up a new company Fairchild Semiconductor. This company revolutionized the semiconductor industry and finally became the first commercial firm to introduce high frequency silicon transistors. Later, in 1959, Fairchild also developed what is generally considered the first commercially practical integrated circuit. Initially this provided them major contracts from the US military, which were big customers of this new technology due to the Korean War. The biggest commercial successes however, were achieved not by Fairchild semiconductor, but by companies like Intel, who much later managed to develop the integrated circuit into a microprocessor.
To conclude, the major trigger event making Silicon Valley grow was the invention of the transistor, which was made into a successful innovation in Silicon Valley during the late 1950s. This can also be seen in figure 7 which shows that between 1955 and 1965 the employment in electronic component manufacturing more than tripled, from about 3.000 employees to over 10.000 employees.
34 4 cycles through history
As described on the previous page, during the late 1950s the growth of Silicon Valley took off, starting with products mainly for the US military. However later on, the commercialisation of the integrated circuit also took off. As Moon Lee et. al. (2000) describe, these were the first two out of four waves that Silicon Valley went through. Figure 8 shows the four waves or cycles that Silicon Valley went through.
Figure 8: the four cycles in the evolution of Silicon Valley (Moon Lee et. al. 2000)
35 companies were forced to innovate to meet these requirements. Cutbacks in defence spending in 1969-1970 caused this first wave to come to an end. Besides it stimulated the commercialisation of the products offered by companies in the region, making Silicon Valley enter its next cycle. (Moon Lee et. al. 2000)
Second wave: Integrated circuit. Silicon Valley got its name during this period. The integrated circuit, invented in 1959, led to a major growth of the semiconductor industry in the 1960s – 1970s. From Fairchild Semiconductor all kinds of new companies spun off including Intel, Advanced Micro Devices, and National Semiconductor. Henton et al. (2001) add that only five of the forty-five independent semiconductor firms started in the US between 1959 and 1976 were outside Silicon Valley. The invention of the microprocessor by Intel in 1971 gave an extra stimulation to this wave. Besides, this invention provided a basis for a new wave. Especially since during the 1970s foreign competition in the commodity chip business increased. (Moon Lee et. al. 2000)
Third wave: Personal computers. The increased foreign competition in the commodity chip forced companies in the region to shift to specialized chips like microprocessors. This created the opportunity for a new wave, that of the personal computer. In Silicon Valley, the Homebrew Computer Club, where young talent met each other, was founded in 1975. From this club eventually more than twenty computer companies, including Apple, were erected. The enormous growth during this wave made the number of firms in the region increase from 830 in 1975 to 3,000 in 1990 and the employment from 100,000 to 267,000. At the end of the 1980s however, foreign competition in personal computers increased. At the same time, because of the end of the cold war, defence spending was decreased even further. This presented the greatest challenge for the cluster until then and it increased the need for the cluster to begin a new wave/enter a new cycle. (Moon Lee et. al. 2000)
36 there was an explosive growth of internet-related firms like Netscape, Cisco, and 3Com. Between 1992 and 1998 jobs in the software industry increased with over 150% and jobs in computer networking doubled. Older Silicon Valley companies like Hewlett-Packard and Intel also managed to profit from the growth in the internet market. (Moon Lee et. al. 2000)
Self-augmenting growth
In Silicon Valley, all five gearwheels of the cluster seem to be spinning. Below on each of these factors will be elaborated
Labour force. Moon Lee et.al. (2000) mention the high quality workforce in the region as one of the key elements of the success of Silicon Valley. This is also shown in figure 9. This graph shows that in Silicon Valley, in comparison to the US, the workforce has generally enjoyed a higher level of education.
Figure 9: Educational attainment in Santa Clara & San Mateo Counties and US in 2006 (Silicon Valley Index 2008)
37 universities' needs, to take short-term leaves of absence. The companies, in turn, stimulate this interaction by sponsoring research. The results of the interactions with Stanford are remarkable: During the entire lifetime of Silicon Valley many successful companies have spun out of Stanford, for example: Hewlett-Packard, Varian & Associates, SUN (Stanford University Network) Microsystems, SGI, Cisco Systems, Netscape, Yahoo and Google. (Moon Lee et. al. 2000)
Knowledge development. Silicon Valley is a place where many ideas for new products, services, markets, and business models emerge. They come from entrepreneurs, people in established firms, people at universities, venture capitalists, and people elsewhere in the world who move here. Due to the high level of interaction there is also much sharing of knowledge that is not company-secret. Individuals are open to win-win exchanges of knowledge (Moon Lee et. al. 2000). All the interactions lead to all the ideas and knowledge also to be developed. This is shown in figure 10 which shows that a major share of California's and the US' patents have their origin in Silicon Valley
Figure 10: Silicon Valley’s share of patents in California and US (Silicon Valley Index 2008)
38 Silicon Valley. For example in 2007 was it 27%, which is more than one in every four investments.
Figure 11: Silicon Valley Share of US Venture Capital Investments (Silicon Valley Index 2008)
Innovation. During its history, several innovations made Silicon Valley run through different cycles. But due to the cumulativeness of innovation many other, smaller innovations also took place in Silicon Valley, product innovations as well as process innovations. (Sturgeon 2000, Norr 1999, Lécuyer 2001). Figure 12 shows that the productivity of employees in Silicon Valley is much higher than the productivity of employees throughout the US. Since innovation underpins future productivity growth (Porter 1998) it may be obvious that all kinds of innovations stimulate the cluster. This is also reaffirmed by figure 10 that shows that many US patents come from Silicon Valley.
39 Conclusion
After applying the previously developed models several key factors for Silicon Valley stand out:
A high class university like Stanford can not only be a valuable source of knowledge and highly skilled people, but it also can stimulate an entrepreneurial culture.
The government can, acting as a customer of innovative products, stimulate the growth of a substantial mass and of a cluster itself.
A region with high levels of knowledge development and valorisation also attracts knowledge from outside that region.
Major, influential innovations can strongly stimulate and trigger the development of a cluster.
Continuity of a cluster can occur through various cycles in which different innovations play vital roles.
Job hopping is a way of interaction that leads to a big flow of information between organisations.
Swiss private banking
The Swiss economy profits a lot from the well-developed financial sector. In 2003 12,90 % of the Swiss Gross Domestic Product (GDP) came from the financial sector of which nearly 10% originated from the banking sector (Geiger et al 2006). The financial centre or cluster is mainly situated around the city of Zurich. This centre really came into being during the 1960s. At the end of the 1990s however, competition had increased, causing the financial centre to experience some difficulties. This led to several actions in the beginning of the 21st century to increase performance again which, so far, seem to have had positive effects. Below on the development of the cluster over time will be elaborated.
Substantial mass build-up
40 Especially if you keep in mind that most of these bankers were French citizens, chased out of France as a result of the Edict of Nantes by Louis XIV in 1685. The importance of this discretion led to the first regulations on banking secrecy, which date back to 1713 when the great council of Geneva adopted regulations which obligated banks to 'keep a register of their clients and their transactions. They are however prohibited from revealing this information to anyone other than the client concerned, except with the expressed agreement of the City Council.' (Jovanović 2006) Besides these banking regulations, Switzerland had already began with its neutral foreign policy, since back in 1515 Swiss troops had suffered a major defeat against armies of France. This banking secrecy and political neutrality made Switzerland, as Jovanović (2006) puts it, a 'political and financial asylum'.
Trigger events
41 Especially in the first fifteen to twenty years after WW II the banking sector really took off, since Switzerland was not only a safe haven because of its banking secrecy but also because it was a safe haven due to its political stability and the stability of the Swiss Franc. (Vogler 2006).
Self-augmenting growth
During the 1970s and 1980s, due to the very favourable government regulations the Swiss banking sector continued to grow, especially in Zurich. Keeping low levels of inflation however was difficult since the more popular Swiss banks became, the bigger the inflow of capital into Switzerland and the higher inflation would get, decreasing the competitive advantage of the Swiss banks. Especially with the transition to floating exchange rates in 1973, with the exchange rates based on purchasing power parity, inflation would increase due to an inflow of capital. However, the authorities took measures to prevent the rise of inflation. Mainly they limited the inflow of capital from abroad by introducing investment prohibitions, negative interest rates on bank balances of people from outside Switzerland and restrictions on borrowing abroad (Abegg et.al. 2007).
Also the banking secrecy faced its challenges. Especially since 1977, due to the 'Chiasso affair' when Credit Suisse's office in Chiasso had committed fraud, the banking secrecy became a point of discussion. This led to a referendum about the abolishment of banking secrecy in 1984. However, the results of this referendum were very clear: 73% voted in favour of maintaining banking secrecy.
Since it is clearly mentioned in literature and explained above, the main reasons for the rise of the financial centre are to be found in government regulations which were unique in the world. Therefore and due to limited availability of data, the five augmenting factors of the mechanism driving a cluster will not be further discussed in this phase of development of the financial centre around Zurich.
Congestion
42 partly due to new remuneration schemes of executives with stock grants and stock-options and partly due to the general stock market boom. Due to this increase in wealth for the first time since a hundred years, there were more new, 'self-made' billionaires than old billionaires. These 'newly wealthy' were younger people and, having acquired their wealth in a shorter period of time, they were more aggressive in their expectations and in their willingness to take risks. Focus shifted from wealth preservation to wealth creation making demand in alternative investments, like private equity, hedge funds and venture financing increase. (Geiger & Hürzeler 2003) The Swiss banks however were rather late to follow this changing trend. While the Swiss financial sector was still ranked second in the world in growth in the 1980s with an average growth-rate of 6,8%, they dropped to the fourth place in the 1990s and to a sixth place in the period 2000-2006. This final drop was mainly due to low growth in the first two years, 2000-2002 (Swiss Financial Market Services 2007). These lower growth-rates led to the financial centre in Zurich having lost position in comparison to centres in London, New York, Tokyo, Frankfurt, Dublin and Luxembourg (Swiss Financial Market Services 2007, Financial Center Initiative 2003). Both the Financial Market Services (2007) and the Financial Center Initiative (2003) provide additional reasons on the slow-down of the financial centre.
Missed opportunity to build up foreign exchange trading in the 1970s
Lost global gold trade in the 1980s and missed opportunity to build up trading in other commodities.
The absence of any focused and collaborative initiative to support the Finanzplatz Zurich as a cluster.
New impulses
In 1995 plans were made to transform the Swiss financial centre from a banking centre to a financial services centre (Geiger & Bretschger 2008). Focus in this financial services centre would be on private banking, wealth management and asset management. This resulted in the founding of several organisations and the start of several projects:
National Centre of Competence in Research: Financial Valuation and Risk
management (FINRISK). In 2001 the Swiss National Science Foundation
43 and Risk Management‟ (FINRISK). This forum promotes cutting-edge finance research, education of highly qualified finance specialists at the doctoral level and knowledge transfer between finance academics and practitioners. It initiated a network consisting of thirty Finance professors and their research staff, which included the majority of Finance research resources at the Swiss universities. (Geiger & Hürzeler 2003, http://www.nccr-finrisk.unizh.ch)
Financial Center Initiative (FCI). In 2003 the Canton Zurich, the University of Zurich and ETH Zurich initiated the initiative. The goal behind this is to make academia, public and private sector, work together to maintain Zurich‟s position and the financial centre's ability to compete with other global players (Delley et. al. 2004, http://www.standort.zh.ch) Activities include mapping and promoting the cluster, influencing policies, stimulating and supporting education in finance and analyses on the cluster through the project Swiss Financial Center Watch.
Center of Competence Finance in Zurich (CCFZ). In 2004 ETH Zurich and the University of Zurich started this cooperation network. Its goal is to promote and coordinate the activities of the University and the ETH in research and education in finance (http://www.ccfz.ch).
All these projects make the financial centre transform into a financial knowledge centre. The positive effects that this has had on the Swiss banks is shown in figure 13 which shows the total of all balance sheet totals of the Swiss Banks.
44 The first striking rise around 1995 has got to do with, as was previously explained, the developments in the 1990s and the major increase of global wealth. It is important to notice that even though the trend seems to be positive since the 1980s, during the 1980s and 1990s other financial centres in the world managed to profit more from the increase in global wealth and therefore managed to maintain higher levels of growth. The second striking rise around 2004 is due to the activities described above. Because all of this, the Swiss financial centre is still number one in the world in private banking, as is shown in figure 14.
Figure 14: Assets Under Management (AuM) in international private banking in 2005 (Swiss Financial Market Services 2007)
Self-augmenting growth
In Zurich's financial centre especially the gearwheels labour force, interaction and knowledge development seem to be spinning. The other two, Venture capital and innovation, are definitely also of importance, but they are less visible. Venture capital is less visible since banks themselves are suppliers of capital, making all the flows of capital less visible. Innovations are also needed to remain globally competitive as a financial centre, but it is less visible through patents for example. Below the factors labour force, interaction and knowledge development will be elaborated.
45 Competitiveness Yearbook 2008 in the availability of finance skills. The score of the Netherlands is added to provide a perspective.
Figure 15: Availability of finance skills (IMD World Competitiveness Yearbook 2008)
Interaction. The organisations mentioned under 'trigger events' have a strong focus on interaction. This itself is already a signal that interaction is a major process in the cluster, but it is also partially reflected in Switzerland's score on knowledge transfer between companies and universities in the IMD World Competitiveness Yearbook 2008, shown in figure 16. Again the score of the Netherlands is added to provide a perspective.
Figure 16: Knowledge transfer between companies and universities (IMD World Competitiveness Yearbook 2008)
46 Conclusion
The application of the models shows the following key factors in the development of the financial cluster in Zurich:
A stable government policy which is unique in its environment can provide a sector with such a competitive advantage that it may trigger growth of a cluster.
Being flexible enough as a cluster to cope with a changing environment is essential for the continuity of a cluster.
Stimulating interaction and knowledge development through cluster-organisations can stimulate the growth of a cluster.
Medicon Valley
The term 'Medicon Valley' refers to a life-science cluster situated in the Øresund region. This region stretches from Copenhagen in Denmark, to Malmö and Lund in Sweden. The name 'Medicon Valley' stems from 1997, when an organisation called the Medicon Valley Academy (MVA) was launched. But what preceded this event? What led to the start of this organisation and what happened since then? To answer these questions, at first there will be discussed how a substantial mass was developed in that region. Next there will be described what trigger events occurred that made the life-science sector in the region grow out into a cluster will be described. Finally there will be looked at the augmenting factors that are currently driving the recently formed cluster.
Substantial mass build-up
