Industrial Cluster as a Policy Tool

Porter’s industrial cluster strategy also takes its roots from the Marshallian tradition of industrial location. The cluster strategy recognises the importance of proximity and the interaction of economic agents which are not limited to only firms. According to the OECD (2007), “a number of other terms are used by academics and policy makers to describe related phenomena, such as industrial districts, networking, and systems of production or, for the broader environment, a regional innovation system”. This makes the acceptance of a unique definition of clusters very difficult. However, the definitions of clusters project two

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dimensions in its conceptualisation – spatial and network dimensions. These classifications, which have been equally observed by Motoyama (2008) and Prejmerean (2012), have influenced the identification of the cluster phenomenon in space. The cluster strategy revolves around Porter’s ‘competitive diamond’, which relates industrial clusters to competition and comparative advantage. Porter argues that clusters are dynamic and their prosperity is driven by the firm’s productivity, which emanates from competition and complementary behaviour. Among other definitions of cluster offered by Porter (1998, P. 226), clusters represent “a geographically proximate group of interconnected companies and associated institutions in a particular field, linked by commonalities and complementarities”. Alternatively, Porter explains that a cluster is a network that occurs within a geographic location in which the proximity of firms and institutions ensures certain forms of commonality and increases the frequency and impact of interaction (Porter, 1998, P. 226). These definitions, according to other scholars, remain largely static (Cooke, 2001; Motoyama, 2010). As a result, Cooke (2001) proposes, among others, that dynamism may emerge with the addition of other features such as:

 Clusters sharing an identity and future vision

 Clusters exhibiting turbulence as firms spin-out, spin off and start from other firms  Clusters revealing, over time, the features of emergence, dominance and decline

The cluster construct emanates from Porter’s competitive diamond. This competitive diamond, according to Martin and Sunley (2003), is the driving force in cluster development; at the same time, the cluster is the spatial representation of the competitive diamond. Porter’s competitive diamond, in Figure 2.1, puts forward four determining factors of competition at the firm level. The first determinant focuses on local demand conditions, which postulates that increasingly demanding home customers cause firms to upgrade and differentiate their products and services to serve local as well as foreign markets. The factor input conditions in the form of the quantity and quality of natural, human, capital, administrative, scientific, technological, and infrastructural resources constitute the second determinant. The third determinant is made up of quality, and capability of locally based suppliers and the presence of competitive-related industries. The final determinant is the strategy and rivalry context of the firm and the rules, incentives and norms governing the type and intensity of local rivalry (Porter, 1998).

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Porter believes these four elements of the diamond are essential to the understanding of the role of the cluster in competition. This is because competitive firms are not scattered helter- skelter throughout an economy but are clustered either ‘vertically’, where firms or industries are linked through buyer-seller relationships, or ‘horizontally’, in which industries might share a common market for the products, use a common technology, labour-force skills and similar resources (Porter, 1990). Therefore, factor inputs – tangible and intangible assets – especially those concerned with innovation and upgrading must improve inefficiency to cause an increase in productivity. This affects the firm’s local-level strategy and rivalry. In effect, the competitive nature of a country is seen as a bottom-up policy orientation (Porter, 1998).

From the definition cluster, there exists interplay of several economic agents not restricted to the interaction of firms only. Clusters may include regional resources and infrastructure, governmental and private institutions (universities, vocational training providers, standards- setting agencies, and trade associations) that provide specialised training, education, information, research, and technical support (Porter, 1998; Rocha, 2004; Isaksen and Trippl, 2016). The interaction of these socioeconomic agents may produce collective results for the cluster, or what Schmitz and Nadvi (1999) term ‘collective efficiency’. As a result, a social network of trust in the location emerges that facilitates increased productivity, drives the

FIRM STRATEGY AND RIVALRY

vigorous competition among locally based rivals

FACTOR INPUT CONDITION

Local labour, capital and natural resources; physical, administrative, information and technical

infrastructure; specialised input

DEMAND CONDITIONS sophisticated and demanding local customers; customer needs anticipating those elsewhere; specialised local demand

RELATED AND SUPPORTING INDUSTRIES Presence of capable locally

based suppliers & competitive related industries

Local content that stimulates investment and sustained

upgrading

Figure 2.1 Porter’s competitive diamond Source: Porter (1998)

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direction and pace of innovation, and stimulates and strengthens the formation of new businesses (Tan et al, 2013). In effect, a cluster generates externalities or benefits from the social capital created to reinforce the competitive structure and provide rejuvenation to the entire location. Maine et al (2011) identify three types of cluster benefits: market benefits, spill over benefits and diversity benefits:

 The market benefits are obtained through market transaction and, among others, include reduced transaction costs and information search cost

 The spillover benefit constitutes knowledge spillovers from competing co-located firms, public infrastructure, and suppliers and customers

 Diversity benefit rests within the urbanisation economies

However, it is important to note that firm-specific cluster benefits will depend not only on the resources, knowledge and capabilities available in the cluster, but on the ability of a firm to absorb these resources, and in particular on their ability to absorb knowledge (Maine et al, 2010). Therefore, the advantages of being in a cluster may not be evenly distributed as a firm’s location, size and distance from the centre may affect its benefits (Maine et al, 2010; Klumbies and Bausch 2011).

In addition, the extent of a firm’s competitive involvement in the continuous networking of economic actors in a cluster generates spillovers that constantly reinforce the clustering process (Popescu, 2010). The strength of these spillovers and their importance to productivity and innovation are perceived by Porter (1998) as often the ultimate boundary-determining factors. These boundaries nurture innovation as a result of competition. To Porter (1998), networking of buyers, suppliers, and other institutions in a cluster is important, not only to efficiency but also to the rate of improvement and innovation. This is because there is a concentration of information on buyers’ tastes and sophistication in production from which firms in a cluster are able to perceive buyers’ needs and access new technology (Parrilli, 2009).

In terms of industrial policy implications, Porter’s work (1998) outlines the role that the public sector can play in moulding and strengthening clusters. The role of government may serve as a help or hindrance to investment and innovation activities in an economy. Parrilli (2009) points out that government support for cluster development and growth over the years has produced mixed results. In line with this, Porter (1998) calls for private public partnership, arguing that deregulation and privatisation on their own will not succeed without

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vigorous domestic rivalry (Porter, 2003). Variation in the role of government in inward- and outward-looking economies misses the notion of competiveness because there is the need for a competitive policy. Porter argues that a government’s role may include ensuring political and macroeconomic stability, improving the economy’s microeconomic capacity (policy at cluster level), and developing and implementing a long-term economic action programme. These may have implications for cluster competiveness and productivity in local and international markets (Porter, 1998).

The cluster strategy and the supposed advantages of co-location have made the cluster concept more attractive to policy makers (Martin and Sunley, 2003; OECD, 2007). This is evident in recent industrial policy thinking in developed and developing economies, and international and regional development agencies (OECD, 2007). In addition to the easy adaptability of the theory, Sepulveda (2008) argues that the cluster concept is ideologically compatible and malleable to dominant market structures. However, the move from theorising to the identification of clusters comes with its own difficulty. Therefore, Motoyama (2008) argues that, “at the theoretical level, the theory is descriptive and static in nature despite Porter’s insistence that a cluster is dynamic”, because he presents only the developed and successful clusters and provides no historical analysis of cluster development. He further stipulates that the theory is limited in identifying and promoting the interconnected aspect of the cluster and more dialogue with network theory will strengthen its application.

Martin and Sunley (2003) question the broad definition of a cluster offered by Porter since it lacks clear industrial and geographical boundaries. The extents of technology and information spillover make geographical limitation seem vague, making it difficult to exactly measure the size of a cluster. This vagueness is compounded by the varying definitions of clusters, which make generalisation to local and regional agglomeration problematic. In effect, Porter’s description of the organisation of cluster activities in a location becomes more questionable when he attempts to draw a link between clusters showing schematic cluster interactions and overlaps since cluster boundaries are not precise (Martin and Sunley, 2003; Kim, 2015). In addition, the general perception and information in support of superior firm performance in clusters is based on success stories and case studies (Malmberg, 1996). As noted by Porter (1998), such a cluster may evolve accidentally and the development of a well-functioning cluster is one of the essential steps in moving to an advanced economy as firms become more competitive. This view ignores the dynamisms of place that bring about successes, and how

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unique spatial attributes influence success. For instance, developing clusters from scratch without a binding condition or commonality poses problems for the success of any cluster policy (Motoyama, 2010). These binding factors are locked in socio-cultural behaviour of the place and do not accidentally evolve; the absence of these binding factors renders the cluster strategy as an intellectual frame rather than an applicable benchmark for cluster development (Motoyama, 2008; Kim, 2015)

It is worth noting that, despite its wide adoption, there seems to be no agreed method for identifying and mapping clusters, either in terms of the key variables that should be measured or the procedures by which the geographical boundaries of clusters should be determined (Martin and Sunley, 2003; Motoyama, 2008; Tsakalerou, 2015). Several authors have identified clusters in different ways by using different data and methods. For instance, while some adopt input-output data in the industrial sector, others use quantitative and qualitative surveys (Spencer et al, 2010; Stejskal and Hajek, 2012). An alternative measure has been adopted by Nesata et al (2004) based on two dimensions of cluster, as a geographic component (proximity, locality, etc.) and as an interaction component (vertical and horizontal). On the other hand, Porter’s adoption of average wages, employment growth and patent right to show the classification and distribution of industrial clusters across US regions is limited since it is based on the unique industrial structure of the US (Martin and Sunley 2003; Spenser et al, 2010). This lack of a generally accepted way of identifying a cluster has led to heuristic approaches which water down the significance of cluster strategy. However, the theoretical and empirical significance of co-location makes the study of clusters worthwhile.

In summary, despite these problems, the cluster construct presents a more friendly way of analysing the advantages of firm spatialisation. As a result, it has been widely adopted by countries and international institutions as a policy construct. However, the cluster strategy has several limitations, ranging from acceptable definition and geographical dimension to the absence of place-bound socio-cultural determining factors. Despite the wide adoption and challenges of the cluster construct, Spencer et al (2010) argue that evidence of a cluster’s impact in a region remains remarkably scarce. In addition, clusters in developing economies have received minimal attention (Han, 2009).

The next section examines the nature and performance of clusters in developing economies where clusters are actively internationalising.

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