Ryazantsev S.V., Vartanova M.L., Vazirov Z.K., Garibova F.M. Small ethnic groups in Russia: demographic trends and economic determinants (case study of the North Caucasus and Siberia). The Eurasian Scientific Journal. 2025;17(4): 84ECVN425. Available at: https: ...

Текст статьи на сайте журнала URL: https://esj.today/84ECVN425.html (дата обращения 09.10.2025)

Abstract

The purpose of the study is to analyze key demographic trends among the indigenous minorities of Russia using the example of the ethnonational regions of the North Caucasus and Siberia and identify economic factors influencing their dynamics. The research methodology is based on official data from Rosstat (2010, 2020 Censuses), materials from the national project «Demography» and regional programs. Methods of comparative analysis, classification and tabular data visualization were used. The results of the study. It has been established that the demographic trends of small indigenous peoples are divergent. Three groups of ethnic groups are identified: growing (Abazins, Tuvans-Todzhins), with stable numbers (Nanais, Mansi) and shrinking (Chulyms, Entsy, Kereks). In the North Caucasus, the strengthening of titular groups (Chechens, Avars) prevails due to high birth rates and state support, while in Siberia there is a depopulation of the Russian population and the assimilation of indigenous peoples (Khakass, Telengits). The key factor determining demographic dynamics is the economic situation. It was revealed that high unemployment leads to marginalization and migration outflow, while the development of ethno-tourism creates positive incentives for the preservation of identity and sustainable development of communities. Recommendations are offered for targeted support ofsmall ethnic groupsthrough the creation of micro-grant fundsfor ethno-startups, the integration of tourism initiatives with programs for the preservation of languages and traditional nature management. Research prospects are related to an in-depth analysis of the impact of extreme weather events on traditional farms and the assessment of the effectiveness of state programs using big data.