Abstract:

Objective. To determine the relationship between the parameters of institutional and innovation activity in the Russian regions at different phases of the economic cycle. Methods. Methods of comparative and cluster analysis were used. Results. Five groups that differ in the levels of institutional and innovation activity can be distinguished with a reasonable degree of reliability in the list of the Russian regions. Regions form groups unstable in their composition (virtual clusters), so we can consider that the regions of the country do not have stable innovation and institutional parameters for a long period. Two indicators have similar trends in all clusters. These are high values of the propensity to consume with different variants of dynamics and abrupt change in the propensity to immobilize savings. The institutionally active regions are characterized by a decrease in the values of propensity to consume during the crisis period; the regions with average institutional activity - by a constant growth; the least active regions demonstrate stable values of propensity to consume. The maximum values of the propensity to immobilize savings in all clusters fall on the crisis year of 2015. The propensity to save in the most active group of regions is very dynamic and varies widely depending on the phases of the economic cycle. The propensity to save has the highest values in the lagging clusters "D" and "E". The propensity to monetize assets varies fundamentally across different groups of regions. In general, the indicator is unstable in time and space. It can be stated that there is an increase in the propensity to monetize assets in active clusters. The propensity to materialize assets varies over a wide range. There are contradictory trends in the dynamics of the indicator in different groups of regions. The propensity to innovate also fluctuates in most clusters over a wide range. At the same time, the values of the indicator are quite stable over the entire period of time in the outsider cluster. It should be noted the opposite dynamics of the results of innovation activity even in the clusters which are close in a rate of activity.