Abstract
The article examines the essence of competitive immunity and gives the main stages of evaluating the competitive immunity of the region. There are three blocks for assessing the competitive immunity of the region: information and digital approach; information and digital technologies; value and reputation management. In accordance with the selected blocks, the objects of management influence necessary for assessing the competitive immunity of the region and bringing it to sustainable functioning are defined, namely: production sustainability, financial sustainability, socio-economic sustainability, informational and psychological sustainability, market sustainability, sustainable innovative development, growth of business value, balanced innovative infrastructure and reputation of the region. For one of the important objects of managerial influence, namely, financial stability, a system of indicators of a comprehensive analysis of the financial stability of the local budget as a component of competitive immunity is proposed, which should be considered in three groups: analysis of the balance of the local budget, analysis of financial stability and analysis of budget efficiency. The proposed ratio complex consists of twelve indicators, namely: coefficient of budget coverage, coefficient of budget sustainability, coefficient of general tax stability, coefficient of cost coverage by interbudgetary transfers, coefficient of budget dependence, coefficient of tax independence, coefficient of stability of the income base, the share of equalization grants in the total amount of transfers, local budget deficit ratio, coefficient of budgetary efficiency, coefficient of budgetary support, indicator of stability of the revenue part of the budget. The essence and normative values of the proposed indicators were considered. Within the framework of the proposed approach, the components of the integral indicator of the financial stability of the local budget are considered. The essence and algorithm of the work of Kohonen's neural networks with the aim of its further use for the segmentation of regions based on indicators of financial stability of regions are given.
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