4. Konstantin I. Shaklein, Marina V. Shakleina

Strategic Classification of the Russian Federation’s Citizens According to Economic and Social Development

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Konstantin I. Shaklein

Postgraduate Student of Lomonosov Moscow State University

Leninskie Gory 1, Moscow, Russian Federation, 119991

Marina V. Shakleina

Senior Lecturer at the Department of Lomonosov Moscow State University, Ph.D. in Economics

Leninskie Gory 1, Moscow, Russian Federation, 119991

Russia’s vast territories and its unique geographical features make it rather difficult to classify its citizens. However, classifying regions is crucial to identify consistent trends in their development. The purpose of this study is to develop a consistent classification of the Russian Federation’s regions.
Aim. This study aims to develop a consistent classification of regions according to their levels of economic and social development.
Tasks. This study reviews the literature on the subject of developed classifications and identifies flaws in existing methods. It then proposes a methodology for developing a consistent classification, determines the set of features that characterizes the economic and social development of regions, collects data from 2010 to 2014 and presents it in a structured format, applies the proposed methodology for regional classification to the collected data, and interprets the obtained results.
Methods. This study proposes to construct an internal indicator of economic and social development of territories based on selected statistical figures and then perform a spatial and temporal classification of its values using the coefficients of auto- and transregional dynamics. A comparative analysis of the processes of economic and social regional development from 2010 to 2014 is performed based on the internal indicator values.
Results. The study employs a spatial–temporal panel of statistical data (23 indicators for the years 2010–2014). The analyzed indicators are arranged into two blocks: social and economic. The proposed methodology takes the proportion of explained dispersion of the first principal component as a weighting coefficient. The social and economic blocks have weight ratios of 51% and 49%, respectively. The performed analysis shows an improvement in social and economic regional development from 2010 to 2014 since the average value of the indicator increased by 0.86 points and scored 3.64 out of 10 in 2014. The Moscow region (7.37), the Tyumen region (6.44), and the Republic of Tatarstan (5.36) had the largest indicator values in 2014. The least developed regions in 2014 were the Republic of Tuva (1.82), the Kurgan region (2.37), and the Transbaikal territory (2.43).
Conclusion. The proposed classification, based on the calculated coefficients of auto- and transregional dynamics, makes it possible to arrange regions into three major groups. The first group has the most progressive development and insignificant systemic risks; the second group includes regions with less progressive development, which are inferior in terms of dynamics, i.e., the systemic risk is higher; and the third group comprises regions that lag in terms of development and have high systemic risk.
The classification is performed in a spatial–temporal field, which makes it more consistent and reliable. In the future, it should be possible to notice similar behavior by analyzing how certain socio-economic indicators change within each group of regions and, thus, identify common trends in the development of entire regional clusters.

Keywords:strategic classification of regions, economic and social development, spatial and temporal classification


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