Regional aspects of financial behavior: Detection of key factors and development of adaptive strategies

Aim. The work aimed to identify key socio-economic factors that shape patterns of financial behavior of the population in the regions of the Russian Federation (RF), as well as assess their dynamics for the development of targeted support measures and regional educational programs.

Objectives. The work seeks to cluster regions of the Russian Federation based on socio-economic indicators to detect typical development models, in particular, to assess the dynamics of changes in the level of financial literacy of the population for  2019 and  2023; to construct multi-level models that take into account the influence of individual and regional factors on financial behavior; to develop recommendations for adapting public policy taking into account regional specifics.

Methods. The study employed cluster analysis (k-means using the elbow method), variance analysis (ANOVA), scenario modeling, and multi-level modeling (MLM).

Results. The study revealed stable differences in the financial behavior of the population of Russian regions, induced by socio-economic factors. Cluster analysis identified four groups of regions, while each of them demonstrates characteristic development patterns. Multilevel modeling confirmed the importance of the regional context, where  15% of variations in the level of financial literacy can be due to territorial specifics. Individual factors, including age, income, and education, also have a significant impact, but their effect varies across regions. The revealed positive relationship between the level of digitalization and financial literacy is of particular interest, as residents of regions with developed digital infrastructure demonstrate higher results, regardless of individual characteristics.

Conclusions. The results confirm the need for a differentiated approach to the implementation of the task of improving financial literacy, taking into account regional characteristics. Measures for each cluster from infrastructure development in depressed regions to advanced educational programs in developed ones are recommended. 

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