Supply chain and networking structure of clusters in developing economies

socially proximate and vertically disintegrated production structure at a certain level of the production chain (Hans, 2009; Geldes et al, 2015; Sonobe and Otsuka, 2016). These relationships in these clusters often drive the activities along the supply chain from the raw material stage to the consumption stage. This is because clusters may have close relationships with each other through which they share ideas and information (Sonobe and Otsuka, 2016; Knorringa and Nadvi, 2016). The aluminium and steel clusters in South Africa, Lake Victoria fishing cluster in Kenya, and clustered fish farmers and mango growers in Chile and Peru, respectively, have all provided compelling evidence to this effect (Visser, 1999; Alfaro et al, 2012).

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In the tomato processing cluster and mango cluster in Chile and Peru respectively, the relationship among small and medium-sized producers and exporters ensures trust building through constant interaction with one another. This has facilitated the flow of information on the production structure and specification required by the market into the cluster, thereby making them competitive (Clarke and Ramirez, 2014). The Argentinean wine clusters demonstrate how network ties built relationships between grape producers, wine makers, intermediary institutions and policy agencies operate to produce the current successes enjoyed by the cluster (McDermott and Rocha, 2010). Additionally, these network firms are further linked to other subsidiary institutions and government agencies to produce a web of interconnected structures that ensures the functioning of the cluster. For example the presence of universities (engineering workshops) and other research institutions has been observed in most of the clusters in developing economies and has provided an essential link to the flow of new knowledge into the cluster (Perez-Aleman, 2005; Alfaro et al, 2012; Rasiah and Vinanchiarachi, 2013; Knorringa and Nadvi, 2016).

From the observed literature in Table 2.1 clustering may be based broadly on the nature of product, and network relationship. Based on the nature of product, we may have agro-based and non-agro-based clusters. However, given that clusters are networks of firms competing and cooperating, clusters may be categorised as vertical or horizontal based on their network structure (Porter, 1990; Guo and Guo, 2011). For instance, in the case of the Peru mango clusters and the salmon clusters in Chile, the vertical relationship between small, medium and large producers of agricultural aqua-cultural product processing and exporting firms has provided the springboard to promote and improve the product for the international market. As a result, product knowledge and innovation, standards and designs are transmitted into the cluster through their supply network (Nadvi, 1999; Giuliani, 2007; Gereffi and Lee, 2016). In the case of the Shangyi cooling tower clusters in China, similar relationships have been observed. In such situations, the leading firms serve as the gatekeeper of knowledge and innovation within the cluster and request information needed to sustain the cluster’s operations (Bathelt et al, 2004; Morrison, 2008; Guo and Guo, 2011). Where the relationship is horizontal, such as the metal works clusters in Kenya and Ghana, and warp-knitting cluster in Zhejiang, China, firms adopt similar technology, labour force skills and common resources to produce for a common market. In such a relationship, knowledge and innovation may be

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minimal and may occur as a result of spinoffs by past employees who imitate these production processes to set up rival firms (Guo and Guo, 2011; Sonobe et al, 2011).

Clusters in developing economies may also be linked to multinational enterprises, as categorised in Table 2.1. Multi-nationally-driven clusters may be made up of small, medium and/or large-scale production units which have an orientation towards external markets. The multinational clusters operate with relatively high levels of technology and innovation either directly in an economy or rely on domestic micro, small/medium enterprises to complete the production process. The situation may be observed in the automobile cluster in South Africa, a multi-nationally-based cluster, which is linked to steel, aluminium, leather, rubber, plastic and glass subsidiary firms that provide the inputs for the automobile industry (Alfaro et al, 2012). A vertical relationship exists between the automobile firms and their subsidiary firms in the cluster, and a horizontal relationship exists among subsidiary firms on one hand and between the various automobile-producing firms on the other hand (Alfaro et al, 2012). A similar situation has been observed in the automotive (Argentina), electronics (Malaysia), buttons (China) and salmon (Chile) clusters, as summarised in Table 2.1.

These networks of firms in a cluster, aside from offering a pool of skilled and unskilled labour, provide an avenue for knowledge and technological transfer, through their interactions with government agencies, producers associations and other intermediate institutions (Nadvi, 1999; Giuliani, 2007; Gereffi and Lee, 2016). The salmon cluster in Chile provides a pool of labour for fish farms, feed producers and processing enterprises. Aside from this, joint action assisted by government export and standardising agencies has led to the transfer of knowledge on the processing and preparation of salmon to serve the international market (Rasiah and Vinanchiarachi, 2013). In the Peru mango cluster, the presence of clustered mango producers has attracted buyers within the domestic economy and beyond (Clarke and Ramirez, 2014). The presence of automobile clusters in South Africa has promoted the establishment of various distribution units which supply auto parts to customers and financial institutions which support customers in the acquisition of automobiles in the market (Alfaro et al, 2012). These observations conform with Krugman’s (1991) discussion of external economies. It is worth noting that, while these external economies may be present in developing economies’ clusters, not all clusters have the three types of externalities espoused by Krugman. Weak intermediate input effect and technological spillover have been observed in the metal works cluster in Kenya and the Leon’s footwear cluster in Mexico (Sonobe et al,

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2011; Martınez et al, 2012). The nature of clusters may produce gains. However, the successes of these clusters in achieving these gains depend not only on the network relationships within the cluster; the institutional environment that supports a cluster’s operation facilitates collective learning, cooperation and innovative capabilities, as observed in the Peru mango clusters (Clarke and Ramirez, 2014).