CLUSTER ANALYSIS OF THE RUSSIAN REGIONS BY THE LEVEL OF INNOVATIVE ACTIVITY

The article provides a cluster analysis of the constituent entities of the Russian Federation by indicators of innovative activity. Such methods of hierarchical cluster analysis as single link (“nearest neighbor”), paired links (“far neighbor”), Ward’s method) and the k-means method were used in the work. Euclidean distance was used as a measure of distance between objects. Data processing was carried out in the Statistica program. The empirical base of the study is the data characterizing the innovative activity of the constituent entities of the Russian Federation in 2016 (83 constituent entities). The following indicators characterizing the innovative activity of the constituent entities of the Russian Federation were used: the number of organizations that carried out research and development; volume of innovative goods, work, and services, as a percentage of the total volume of goods shipped, work and services performed; the volume of innovative goods, work, and services; the number of personnel engaged in research and development; internal costs for research and development; advanced production technologies being used; granted patents for inventions; issued patents for useful models; costs of technological innovation; the proportion of organizations implementing technological, organizational, and marketing innovations in the total number of surveyed organizations. Cluster analysis made it possible to classify the constituent entities of the Russian Federation into the following clusters: an innovative region with very high absolute indicators (Moscow), innovative regions with high absolute indicators (Moscow region, St. Petersburg), leading regions (mainly include, subjects of the Volga Federal District), regions with a level of innovation activity above average (Khabarovsk Territory, Perm Territory, Stavropol Territory, Ulyanovsk Region, Sevastopol, Republic of Mari El, Novosibirsk Region, Republic of Mordovia, Lipetsk Region, Penza Region, Chuvash Republic ), the average level of innovation activity (most of the subjects of the Central Federal District and the Northwestern Federal District, as well as the Krasnodar Territory, the Tomsk Region, the Chelyabinsk Region, the Saratov Region, the Astrakhan Region), the level of innovative activity is below average (most of the RF subjects) and lagging regions (mainly the republics of the North Caucasus Federal District and the Siberian Federal District).

innovative activity, cluster analysis, hierarchical clustering, “nearest neighbor” method, “far neighbor” method, Ward’s method, k-means method, cluster, constituent entities of the Russian Federation, federal districts.

Shamray-Kurbatova L. V., Ledeneva M. V. Cluster analysis of the Russian regions by the level of innovative activity. Business. Education. Law, 2021, no. 1, pp. 88—97. DOI: 10.25683/VOLBI.2021.54.174.