The role of interregional relationships in research talent development

Ilya V. Naumov, Anna Z. Barybina


The relevance of research. Workforce quality is paramount to the development of innovative economy and socio-economic development of territorial systems. Not all regions, however, are able to train sufficient R&D personnel to meet the needs of their innovative economies. The lack of research talent can be compensated by establishing cooperative relationships with other territorial systems. Therefore, it is important to study the existing interregional interconnections in the development of research talent and to identify the key priorities in this sphere.

The aim of the study is to demonstrate the relationship between the indicators of development of research talent in different regions and their innovative activity.

Data and Methods. The study uses spatial econometric modeling tools and methods for calculating global and local spatial autocorrelation indices of Moran P. and their dispersion diagrams. The spatial autocorrelation was calculated by using a standardized matrix of distances along the roads between the regional administrative centers.

As a result of the analysis, a close relationship was found between the indicators of development of research talent in Russian regions and their innovative activity. The constructed regression model based on spatial data lead us to the conclusion that efficient innovative development requires a pool of STEM talent in the regions, which means that it is necessary to provide sufficient opportunities for training and education in this sphere.

Conclusions. The study of the interconnections between the regions using the improved method of spatial autocorrelation of P. Moran revealed a cluster of closely interconnected regions (Moscow - St. Petersburg - Moscow region - Nizhny Novgorod region - Ryazan region - Ivanovo region - Tver region - Kostroma region - Tula region) and three potential clusters: ‘Volga’, ‘Ural’, and ‘Siberia’.


regional system, research talent, innovative economy, inter-regional relations, spatial autocorrelation, spatial autoregressive analysis

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