Assessing region and industrial cluster mutual influence: System and spatial approach - Dmitry Aleksandrovich Koshcheev; Tatyana Vasilevna Miroliubova (Вестник Пермского университета: Серия Экономика, 2022)
An industrial cluster as an integrated tool for an area development is extensively applied all over the world. The purpose is to retranslate the practices of some successful cluster initiatives. Meanwhile, actual outcomes of industrial clustering are often negative or neutral. This can be explained with insufficient knowledge about the interaction of two territorial economic systems: a region and a cluster. Here, we take one of the first attempts to comprehensively evaluate this process and to develop some conceptual mechanism which can simulate the implications of a cluster policy both for the region and for an industrial cluster. To do this, the literature was reviewed. This reflected quite a complicated structure of theoretical and methodological background covering the interaction between a region and a cluster and methods of its study. The analyzed background describes six approaches to the interpretation of an industrial cluster (system, institutional, network, agglomeration, classical, and administrative ones) and four evaluative methodologies for a cluster impact on a region and a region impact on a cluster (statistical, regional, marketing, and case ones). In their practical studies, researchers combine methodologies with the approaches and develop a kind of conceptual systems (a theoretical approach + methodology). However, these systems are focused on either a territorial geographical aspect of an industrial cluster or its social economic field. As a result, the comprehensive assessment which is based on these conceptual systems doesn’t show the broad picture. To solve this problem, the authors offer a conceptual system which includes a system spatial approach and a statistical methodology with the cluster’s binary nature problem solved. In this case, the assessment of region and cluster interaction helped develop a visual model which predicts changes in the region’s impact on a cluster under the changes of cluster’s influence on a region and vice versa. The application of the model decreases the detrimental effects of a cluster policy and maximizes positive externalities both for a cluster and a region.