Analysis of Regional Cluster Structure By Principal Components Modelling in Russian Federation

In this paper it is demonstrated that the application of principal components analysis for regional cluster modelling and analysis is essential in the situations where there is significant multicollinearity among several parameters, especially when the dimensionality of regional data is measured in tens. The proposed principal components model allows for same-quality representation of the clustering of regions. In fact, the clusters become more distinctive and the apparent outliers become either more pronounced with the component model clustering or are alleviated with the respective hierarchical cluster. Thus, a five-component model was obtained and validated upon 85 regions of Russian Federation and 19 socio-economic parameters. The principal components allowed to describe approximately 75 percent of the initial parameters variation and enable further simulations upon the studied variables. The cluster analysis upon the principal components modelling enabled better exposure of regional structure and disparity in economic development in Russian Federation, consisting of four main clusters: the few-numbered highest development regions, the clusters with mid-to-high and low economic development, and the "poorest" regions. It is observable that the development in most regions relies upon resource economy, and the industrial potential as well as inter-regional infrastructural potential are not realized to their fullest, while only the wealthiest regions show highly developed economy, while the industry in other regions shows signs of stagnation which is scaled further due to the conditions entailed by economic sanctions and the recent Covid-19 pandemic. Most Russian regions are in need of additional public support and industrial development, as their capital assets potential is hampered and, while having sufficient labor resources, their donorship will increase.