Industry Clustering - Essay Example

Industry Clustering A. Why might firms in an industry cluster together? Clustering together of firms in a particular industry around a specific location occurs as an economic consideration from different perspectives within and outside the various firms. Firms’ management teams may decide to set up establishments near similar firms as favoured by internal forces and estimations of the operations. Alternatively, external factors such as market and government forces on investment and business location dictate to a certain extent the most favourable location of the firm (Humphrey and Schmitz 1996, p1860). The determination and characterization of the specific causes of clustering phenomena is an important economic metrics tool that can be used in managerial economics. Some of the general deductions that can be made from the clustering phenomenon in business location involve synergy in operations where conglomeration facilitates efficient industry-specific microeconomics. To illustrate this, it is an economic fact that an industry relies on a support economy including supplies and other secondary support services. In a clustered environment, it is certainly possible for the entire cluster, working as a system, to influence the market forces of the ancillary system. The influence exerted increases the bargaining power of the firms in attracting the relevant support systems in that location. Among the most definitive determinants of clustering, include supplies and labour as briefly discussed below. Despite the fact that there are different levels of dependence on suppliers across firms as dictated by the nature of business engaged, firms may cluster together o take advantage of supply dynamics. It can be projected that firms with a higher reliance on supplies in running of their operations would be in a favourable business opportunity if they were located together. Pulling suppliers’ attention towards a centralized market reduces costs for their operations and facilitates in increasing the firms’ bargaining power thereby creating a favourable environment for the players. In addition, labour intensity in the operations of industries is varied across business types, but it affects the location considerations by firms across the various levels of their labour demand. Labour supply is influenced towards the clusters as a centre of interest for the labour market where high specificity across the skills levels is likely to be met. Such a setting dramatically changes the dynamics of the labour market by attracting its availability and quality. Several other factors determine the location of a firm within related centralized operations for related firms (Simpson 2007, p4). In the determination of the impact of clustering to specific industries, several tools of analysis can be employed. According to the diamond model of clustering as postulated by Porter, competitive reasons compel similar businesses to choose a common location for their businesses. A consideration on the growth opportunity that a firm has within different settings is made to arrive at a favourable setting among related firms. According to Porter (1990, p.24), there are three important strength areas of clustering that force firms to aggregate together in a common location. Firstly, productivity is enhanced within the competitive setting established by deciding to set up shop closer to one another. Additionally, it is possible for competitive forces to drive innovation high while operating within the same location when compared to a different version of locating business. Alternatively, it is possible to stimulate the sprouting of incidental businesses, particularly those that support or rely on the particular type of business in which the firms engage. Porter’s diamond model of business clustering highlights four main determinants of the impacts of clustering to individual businesses. Industry related demand factors increase forcing firms to continually improve their products through various competitive enhancement perspectives. Support industry is also involved in the model in that the proximity to appropriate support from other ancillary industries determines the ease of obtaining information and keeping up-to date with innovation. The applicable firm strategy in operations coupled to its internal structure and level of competition from rival firms must be approached from industry competition needs to remain sustainable. Additionally, key production factor conditions emerge from operations and require accuracy in establishment and nurturing, which requires learning from each other. Among the specific determinants of the clustering phenomenon, input/output models where relationships between industries can be projected using financial information. To illustrate this using the information presented in the Welsh Input-Output 2007 Project, it is possible to identify various clustering trends in Irish firms (Bryan, Jones, Munday and Roberts, 2010). However, technicalities involved in the reading of the data to generate relevant conclusions may hinder the analysis and transposing into actual managerial economics lessons. Alternatively, the lack of data for specific companies for closer comparison in the determination of how each company benefits for being closer to a related company hinders clearer projections. In the following section, the Welsh Input-Output 2007 Project is discussed to generate the appropriate insights in terms of economic impacts of clustering industries in Wales. B. Please provide two examples of industry clusters to illustrate some of the points raised above. According to Bryan et al. (2010, p16) the Welsh Input-Output 2007 Project data shows clearly that whereas industries make cross-industry transactions as represented by the demand, there are certain aspects of the demand that illustrate high demand from related firms and support industries. As an illustration, Quadrant 1 showing total intermediate demand ranks manufacturing and extraction to have obtained close to half of the demand from similar firms (£2808m out of (£4945m). Support industries for the manufacturing and extraction constitute a huge proportion of the demand to illustrate their importance in determining possible impacts of firm location (Devereux, Griffith and Simpson 2007, p420). From such information on the manufacturing and extraction sector, setting up of firms will heavily rely on demand, as one of the factors in Porters diamond model. Apparently, manufacturing component of firms may be flexible in determination of a suitable location than it would be for extraction component due to the source of raw material. However, considering transportation of raw materials to a particular site could be a viable alternative if it outweighs the costs of transportation. According to the domestic use data provided in the Welsh Input-Output 2007 Project (Bryan et al. 2010, p16), the distribution of domestic use for the various sectors determines the projected demand for the industry. This implies that the largest consumer bears an important location determination influence close to related industry members for the benefits of taking advantage of the proximity to core market. Perhaps competitive advantage forces as discussed in the Porter’s diamond model contribute to location decision better than mere proximity to core markets. Another cluster determinant in the presented data include the support industry which constitute a huge domestic use for the construction industry presented in Bryan et al. (2010, p16). As mentioned above, the largest proportion of demand for construction in Quadrant 1 is attributable to the same industry at £819m out of £1816m. Distribution of the remainder across the support industries supports the perspective in Porter’s diamond model in that demand is also shaped by their use patterns for construction services. According to Porter’s diamond model, competitiveness is driven by the firm’s ability to navigate around variables of proximity to support industries, which facilitates increased innovation and obtaining of relevant information. Despite internal forces for the various Welsh firms to find clustering as a preferred location factor, the Welsh government may be forced to intervene and influence clustering due to the importance of the sectors in national economy. From the input-output data provided in Bryan et al. (2010, p19), the gross value added per sector may compel the government to advice and influence investment into the most productive sectors such as manufacturing (£8350m) as well as financial and business services (£10620m). The overall contribution to the national economy contributes to policy formulation regarding investment and location of business to maximise national productivity such as through value addition (Devereux, Griffith and Simpson 2004, p534). Using the data in the input-output analysis, Wales’s economic performance can be predicted to predict the contribution of the firms clustering tendencies to the competitiveness of the national economy. As illustrated above, local competition can be enhanced based on the factors that push up innovativeness and competitiveness (ITD 2009, p14). Within the Porter’s diamond model, production possibility frontier is enhanced through attraction of favourable facilities through clustering and involvement of government policy in making investment more appealing in the efficient setting. Through competitive production clusters, quality and high technology practices nurtured by a competitive setting, support industries are likely to benefit from the technological advancement thereby benefiting the overall economy (Nadvi and Schmitz 1999). The Welsh economy is poised for tangible and intangible benefits including employment of local populations into the feeder industries that support the cluster industries and the advancement of technology across the industries. In terms of the marketability of Welsh products abroad due to attained quality spurred by heightened competitiveness, cross border trade is likely to be boosted. The difficulty in determination of the actual contribution cluster organization in manufacturing and extraction as well as in construction could be experienced at both local and international levels (Dilling-Hansen, Madsen and Smith 2003, p5). At the local level as illustrated, it is not directly possible to quantify the data for a specific company against another. At the international level, several factors of quantification and contextualization of cross-border trade against specific control companies may also not be directly possible. References Bryan, J., Jones, C., Munday, M., & Roberts, A. (2010) Input Output Tables for Wales 2007, Cardiff, UK: Cardiff Business School. Devereux, M. P., Griffith, R., & Simpson, H. (2004) The geographical distribution of production activity in the UK, Regional Science and Urban Economics, vol. 34, no. 5, pp: 533-564. Devereux, M. P., Griffith, R., & Simpson, H. (2007) Agglomeration, regional grants and firm location, Journal of Public Economics, vol. 91, no. 3-4, pp:413-435. Dilling-Hansen, M., Madsen, E. S., & Smith, V. (2003) Industrial clusters, firm location and productivity – Some empirical evidence for Danish firms, [Online] Available from [Accessed 19 March 2012]. Guiliani, E., Pietrobelli, C., & Rabellotti (2005) Upgrading in global value chains: lessons for Latin American clusters, World Development vol. 33, no. 4. pp: 549–573. Humphrey, J., & Schmitz, H. (1996) The triple C approach to local industrial policy, World Development, vol. 24, no. 12, pp: 1859–1877. ITD (2009) Clusters for competitiveness: a practical guide and policy implications for developing cluster initiatives, [Online] Available from [Accessed 19 March 2012]. Nadvi, K., & Schmitz, H. (1999) Clustering and industrialization, World Development. Special Issue, vol. 27, no. 9, September 1999. Porter, M. E. (1990) The Competitive Advantages of Nations, New York NY: Free Press. Schmitz, H. (1999) Collective efficiency and increasing returns, Cambridge Journal of Economics vol. 23, no. 4, pp: 465–483. Simpson, H. (2007) An analysis of industrial clustering in Great Britain: Final report, [Online] Available from [Accessed 19 March 2012]. Read More