Analysis of the socio-demographic situation of young people in Primorsky Krai in the context of modern challenges: a statistical approach

• Кочева Екатерина Викторовна

  Ekaterina V. Kocheva.Vladivostok State University. Vladivostok. Russia

• Мазелис Андрей Львович

  Andrey L. Mazelis. Vladivostok State University. Vladivostok. Russia

• Nikita D. Bikeshchenko

  Nikita D. Bikeshchenko. Vladivostok State University. Vladivostok. Russia

• Alexandra V. Skoblikova

  Alexandra V. Skoblikova. Vladivostok State University. Vladivostok. Russia

• Valery O. Pershikov

  Valery O. Pershikov. Vladivostok State University. Vladivostok. Russia

The article is devoted to a statistical analysis of the socio-demographic situation of young people in the Primorsky Territory in the context of modern socio-economic transformations. The relevance of the study is due to the strengthening of interregional differentiation of the socio-economic development of the constituent entities of the Russian Federation and the continuing migration outflow of young people from the Far Eastern regions. The purpose of the study is to identify the features of the socio-demographic situation of young people in the Primorsky Territory and to determine the factors affecting the formation of the youth potential of the region. The information basis of the study was the official statistical data of the Federal State Statistics Service and the territorial body of state statistics for the Primorsky Territory. The paper uses methods of descriptive statistics, comparative analysis, analysis of dynamic series, as well as methods of multivariate statistical analysis, including clustering of regions by socio-economic indicators, and econometric modeling. The results of the study made it possible to identify trends in the number of young people, determine the place of Primorsky Krai among the regions of Russia and establish key factors affecting the consolidation of young people in the region. The findings can be used in the development of regional socio-economic policies and youth support programs.

Keywords: youth, cluster, modeling, machine learning models, Far East, challenges, statistics