Cluster analysis of the Russian labour market sectors
Abstract
Subject. In the face of significant change, with companies suspending or completely terminating their operations in the Russian Federation, there are supply and logistical challenges, and planning issues arise. It is still necessary to plan financial support for industries that are most likely to be affected by these changes. The changes affect not only the output, but also the employment and wages in the sectors. A comprehensive analysis of the developments is required to make the most effective decisions. In this study, we considered the relationship between sectors of the economy in terms of average wages, which is an important factor reflecting the development vector in the sector.
Objectives. The purpose of the study was to analyse the specific features of the Russian labour market, based on average wages by sector and to identify similar sectors.
Methodology. In the study, we used the classification of economic sectors based on the methodology developed by the Federal State Statistics Service. Throughout the study, the terms “economic sector” and “industry” are used as synonyms. The following scientific methods were used: measurement, description, and modelling. The research is based on reviewing topical scientific literature, both Russian and foreign.
Results. We grouped economic sectors based on characteristics such as chained rate of increase, average rate of increase, minimum and maximum value, standard deviation, and the range of the studied time series. The resulting clusters reflect the specific features of the industries included, which supports the results of the analysis.
Conclusions. The analysis revealed three clusters with industries sharing a common development dynamic. The first cluster includes industries that are part of the primary sector of the economy. The second cluster includes state-supported industries, while the third cluster represents industries that are part of the manufacturing sector.
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References
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Funding data
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Russian Science Foundation
Grant numbers № 22-78-10150
