Changing the Influence of Economic Factors on Education in the Russian Federation for 2000–2022

The article examines the analysis of the relationship between the economic condition and the level of education of the regions of the Russian Federation over the past 20 years. The gross regional product, the share of pensioners, total expenses and the share of utility costs, the share of the population with incomes below the subsistence level, etc. were selected as economic indicators of the regions. As indicators characterizing the level of education in the regions, the number of teachers, students, teachers, graduate students, the share of costs for basic and applied research, etc. were taken. The main research methods are factor, correlation and cluster analysis, as well as the principal component method. Using cluster analysis, 4 groups of regions were obtained, differing in economic indicators. All indicators were standardized and for each year the method of main components between two groups of indicators was carried out both for all regions of the Russian Federation and for each cluster separately. After selecting the first main components, a linear relationship is constructed between the two groups of indicators under consideration for different federal districts of the Russian Federation. It is concluded that for the first two clusters, from 2000 to 2003, there was no connection between the two groups of indicators. However, since 2004, a linear relationship between the two groups of indicators has been revealed, which tends to increase. For the third and fourth clusters, the absence of this dependence has been noted over the past 20 years. Based on the constructed linear regression model, a forecast is made for the next 3 years on the strengthening of the influence of the economy on the field of education for the regions included in the first and second clusters. The analysis confirms the assumption that for industrial regions there is a significant relationship between economic indicators and the level of education, whereas for raw material regions there is no such relationship. The proposed method can be used both to analyze the impact of economic factors on the education of regions and to analyze the reverse effect, which is especially important for making strategic decisions in the field of education.

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