This paper presents a statistical method to identify regions where similar manufacturing enterprises exist toward developing partner regions as a countermeasure against possible disasters. Following a major disruptive event (e.g., earthquake, hurricane, or influenza pandemic), a manufacturing enterprise may be heavily damaged forcing a temporary cessation of production. An effective counter action available to maintain business involves relocating production and shipment procedures to alternative sites or consigning some portion of the business activities to other companies with similar or almost identical industrial techniques and production assets. This typical approach is generally referred to as a business continuity plan or business continuity management (BCP/BCM). However, adopting such a strategy is difficult for many small- and medium-sized enterprises; hence, the author has recommended some public organizations or establishments to plan “regional BCP” in preparation for a potential disastrous event. An important concept of the regional BCP is the development of an interregional mutual assistance partnership for quick recovery of a damaged industry in the affected region following a disaster. Therefore, local governments may be appropriate as the primary agency for such activities. Because proper and sufficient resources should be sent to the affected area for recovery, its partner region should have a similar industrial structure and scale. Further, cooperating regions should be geographically separated, but within an appropriate distance between each other, to avoid being struck simultaneously while maintaining a partnership that allows low-cost and rapid access after an event. In an attempt to establish an adequate relationship between two regions, 71 variables from the establishment and enterprise census, population census, and industrial statistics were chosen as indices for identifying industrial structures of regions in a wider area. Principal component analysis was applied to these variables, and correlation coefficients were calculated from the principal component scores with eigenvalues greater than one. As a case study, we focused on Ojiya City, Niigata, which was affected by a massive earthquake in 2004, resulting in heavy damages to many manufacturing facilities in the region, and attempted to find its partner regions using the aforementioned method. As a result, higher correlation coefficients indicated greater similarity of industrial structures and commonality among customers between companies in different regions. Finally, regions having relatively high correlation coefficients to each other have a strong incentive to form mutually cooperative and reciprocally beneficial partnerships for disaster reduction.
JEL Classification:C43, H77, H84, L38, R12
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