Knowledge Geography for Measuring the Divergence in Intellectual Capital of Russia

Abstract

Knowledge is becoming a paramount resource of innovation economies. The efficient management of generation, use, accumulation and transfer of knowledge within a non‑linear innovation process plays a critical role in economic growth. Knowledge geography registers the uneven landscape of the national innovation system and captures the key excellence clusters at different hierarchical levels – from local nodes to cities and regions. While the spatial patterns of knowledge commercialization are primarily considered via production processes at the regional level (regional innovation systems, regional innovation clusters), knowledge generation has to be monitored and assessed at the level of cities. Urban settlements accommodate communities of people and a population of firms that form unique configurations of innovation ecosystems that sculpture the intellectual capital of regions and states. This paper presents the distribution of knowledge‑generating centres in Russia by undertaking an in‑depth evaluation of bibliometric data for 440 settlements across the country for a period of 2013‑2017. Methods of spatial scientometrics enable to register the intellectual capital accumulated in a certain locality and analyse development trajectories of urban settlements. Russia is an interesting case of studying the spatial patterns of knowledge generation. The large territorial extent of the country, the remoteness of individual cities from each other, their heterogeneity in size, level of development, and knowledge specialization makes it a highly diverse context. Research results suggest that knowledge domain characteristics are formed irrespective of the population figures, whereas the development dynamics of small and medium‑sized cities are specific. Smaller cities strive to be integrated into inter‑regional and international collaboration in order to overcome the shortage of local resources. A limited gross volume of research output generated by small and medium‑sized cities creates extreme indicator values as compared to the major cities and the national average. The study concludes with a typology of cities taking into account the specific features of knowledge generation dynamics.