Russia has been experiencing a demographic crisis since the 1990s. The most obvious manifestations include an excess of mortality over fertility rates, population decline and an ageing population. The last 20 years have seen considerable activity to come up with new demographic policy measures to mitigate these adverse trends, with single solutions developed for all regions in Russia. This paper presents the results of a study where cluster analysis was applied to enable the identification of groups of regions with significant differences in the dynamics of socio-demographic indicators. We used hierarchical cluster analysis to classify and group Russian regions on the basis of social and economic development indices for 2002 and 2008. The validity of the profiling was confirmed using parametric and nonparametric tests. The analysis identified three clusters of Russian regions. These clusters have significant differences in socio-demographic indicators and the associated dynamics. The results of our analysis identified 'growth points' for each cluster: the fertility correlates that should be factored into the development of effective demographic policy measures.

In recent years, Russia has been grappling with a serious economic crisis. The slowing pace of economic development is accompanied by adverse demographic trends. The purpose of our study is to assess the demographic potential of Russian regions and identifying groups that require the implementation of specific measures aimed at its development. We used hierarchical cluster analysis to model Russia's demographic space and segment regions with comparable problems related to forming demographic potential. Clustering was based on the indicators describing the demographic potential at the macro-level (regional) and meso-level (family level). The analysis identified the groups of regions that have the best and the worst conditions for the development of demographic potential. We proposed a set of measures that would be most relevant to the needs of specific groups of regions and could directly drive the development and actualisation of demographic potential. The analysis showed the need to use multifactor classification in the demographics of countries that have a high level of regional differentiation. Modelling the demographic space on the basis of cluster analysis can be seen as an element of the system of supporting administrative decision-making and the development of effective demographic policy.

Finding determinants of demographic processes is a highly topical issue in countries with negative demographic trends. Our research was aimed at studying the relationships between fertility and income indicators in Russia. The period under review was 2000 to 2016. To explore the correlation between the time series, we used the methodology of estimating trend deviation. We applied analytical smoothing to model trends, estimating regression models. To assess the strength of relationship between the time series, we analysed correlation between regressions' residuals. The results of our analysis showed no relationship between people's incomes and fertility rates. The research we carried out into time series dynamics did not confirm the results of other studies based on static data. Accordingly, this raises questions about the methodology for analyzing the relationship between dynamic processes with a high volatility of input data. Evidently to receive reliable and stable results, multidimensional analysis methods should be integrated into the study of relationships between dynamic time series, including preliminary multi-dimensional data classification. This will enable carrying out analysis on homogenous territorial or temporal segments, which would be more methodologically sound.

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