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OWNERSHIP TRANSFORMATION AND ITS INFLUENCE ON REGIONAL ECONOMIESВ’ PERFORMANCE

Vladimir Kolmakov

Abstract

Regional property complex transformations in different territories have several distinguishing features since they follow different patterns and result into a particular structure of a regionВ’s economy. No matter how exactly property ownership transformation took place in a given region, specific features of it would be indicated by structural shifts between the private and public sectors. The shifts determine vector and dynamics of social and economic development of a region, thus stipulating the role of both public sector and private sector in a given regionВ’s economy. Research of a regional property complex institutional transformation and its results was held regarding their influence on regional economic systemsВ’ performance and efficiency. This task requires to determine patterns and conditions in which institutional structure of a regionВ’s economy (taken in different forms of ownership) can become the most (or the least) efficient, thus preferable. To do that, the estimates of interdependence between regional development indicators and capital expenditure of the three generalized sectors (private, public and their combination) were found. The research hypothesis assumed that regional property complex efficiency and performance (indicated by gross regional product, HDI, budget revenue etc.) was the function of structural shifts in investment activity of public and private sectors. The generalized pattern, obtained from the aggregate data modeling, was tested for applicability in particular regions, as well as specific regional models were also obtained and verified to derive a spread of clusters of regions, grouped by the uniformity of influence of different institutions in terms of their contribution to regional development dynamics. The models of regional development dependence on regional property complex transformations were derived using multiple regression (linear and nonlinear), vector autoregressive models, and imitation modeling techniques. The choice of instruments was determined by their operationality and applicability to the tasks of the research, including scenario planning and extrapolation forecasting. The data employed were the open sources like the Russian Federal Statistics Agency, the Russian Government Analytical Center, the World Bank, OECD, etc. The period of analysis was 2000-2016 (2017 where available).

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DOI resolver: doi.org/10.5593/sgemsocial2018/1.4/S04.080

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