Modeling Economic Prosperity of Russian Regions Depending on Demographic Potential Through Methods of Machine Learning

The article provides the author’s approach to getting comparable quantitative estimations of multi-dimensional index of economic prosperity in Russian regions taking into account specific features of standing and trends of regional development. Index was built by the method of factor analysis done on the basis of statistic data aggregated for the long period from 1955 to 2022. This solution gives an opportunity not only to minimize the impact of random fluctuations in economy on weighted factors of indicators but also to eliminate correlation links between indicator components, which helps overcome consequences of multi-collinearity effect typical of social and economic indicators. Estimated values of index converted into response variable, where ‘1’ is given to regions with the level of economic prosperity higher than average one in Russia and ‘0’ – in the opposite case were included as a resultant variable in the classification model of random forest trained on panel data for the whole concerned period with regard to fixed effect. On the preliminary stage of the research clusterization of entities was carried out by the level of their demographic development. The obtained cluster marks were converted into a set of binary variables, which helped cut the number of included in the model of panel data dummy variables used for modeling fixed effects. Age-specific births were used in this model as explanatory variable. As a result of building up the model by Russian regions for the period from 1955 to 2022 potential estimations of sensitivity of the multi-dimensional index of economic prosperity to changes in demographic processes in Russian regions with regard to specific features and current conditions of their development were obtained and analyzed.

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