Regions of Russia: Clustering Results Based on Economic and Innovation Indexes
Authors: Zavarukhin V. P. , Chinaeva T. I. , Churilova E. Yu. 
Published: November 2022

Currently, one of the main trends is the study of the features and benefits of regional development, increasing the importance of the role of regions in national and world politics. The differences in technological results that can be observed at the national and regional levels are largely due to the peculiarities of the institutional environment, i.e. the degree of concentration at the regional level of high-tech companies, modern production and innovation infrastructures. The regions of the Russian Federation demonstrate noticeable differences regarding the level of socio-economic development, the availability of human and natural resources, the development of educational, scientific and innovative potentials, depending on the historical development of infrastructure. This study examines the results of clustering Russian regions according to the main indexes characterizing the economic, scientific and innovative activity. The classification of regions was carried out by the method of cluster analysis.

Purpose of the study. The aim of the study was to identify homogeneous groups of regions that are similar in their economic and innovation indexes, statistical analysis of these groups based on non-parametric methods and methods of correlation and regression analysis, the formation of conclusions and recommendations regarding innovation.

Materials and methods. The information base of the study was statistical data and analytical information characterizing the state of economic and innovation activity in the Russian regions. The following statistical methods were used in the study: non-parametric (Spearman’s rank correlation coefficients, Mann-Whitney test), correlation (Pearson’s coefficients, coefficients of determination), regression (non-linear regression models), multivariate classifications (cluster analysis), descriptive statistics (averages, structural averages, indicators of variation, etc.).

Results. As a result of clustering the regions of Russia using the k-means method, 4 cluster groups were obtained, which are statistically homogeneous within the studied indexes. In order to identify the relationships between the considered indexes, paired linear Pearson correlation coefficients were calculated. The study tested three hypotheses about statistically significant differences between the indexes of the third and fourth clusters. The set of indexes was as follows: the coefficient of inventive activity, internal costs of research and development per employee, the average per capita size of innovative goods and services. For these purposes, the nonparametric Mann-Whitney test was used. The analysis showed that the regions of the Russian Federation are extremely diverse and heterogeneous in terms of their economic and innovative development. When analyzing them, it is advisable to first use cluster analysis methods to obtain homogeneous groups of territories with similar social and economic characteristics, which is confirmed in this study by testing hypotheses about statistically significant differences between the indexes of the third and fourth clusters (differences between the first and second clusters with other clusters and between themselves obvious and do not require any mathematical proof).

Conclusion. The leaders in scientific and innovative development are Moscow, St. Petersburg, the Moscow region and the Republic of Tatarstan. They have the highest rates of inventive activity of the population and the volume of production of innovative goods and services. Such regions of the Russian Federation as the Tyumen region, the Republic of Sakha (Yakutia), Magadan region, Sakhalin region and Chukotka formed a cluster group with the highest per capita GRP, investments and fixed assets, but they have almost the lowest rates of innovation activity. The extractive industry is the main engine of the economy of these regions. A separate cluster was formed by 26 regions with average levels of economic and innovative development in the Russian Federation. In particular, it includes the areas: Belgorod, Lipetsk, Smolensk, Arkhangelsk, Vologda, Leningrad, Murmansk, Chelyabinsk, Irkutsk, Tomsk, etc. These regions are promising in terms of innovation, but require significant federal investments for their further development. The fourth group of regions united economically weak territories with low rates of innovation activity. These regions accounted for more than half of the total (47 regions). Statistical analysis within the resulting clusters made it possible to identify the relationship between economic indexes and describe them using regression models.