INVESTIGATION OF THE SIBERIAN FEDERAL DISTRICT OF THE RUSSIAN FEDERATION SOCIO-ECONOMIC DEVELOPMENT LEVEL FOR 2018-2021 USING THE METHODS OF MULTIVARIATE DATA ANALYSIS
УДК 332.05:519.237
Abstract
The paper analyzes the socio-economic development of the regions dynamics level of the Siberian Federal District of the Russian Federation for the period from 2018 to 2021. The basis of the study was the Federal State Statistics Service for monitoring the socio-economic situation in the regions of the Russian Federation. The following indicators are considered: average monthly nominal accrued wages of employees (rubles) per year, per capita monetary income of the population (rubles) on average per year, labor force (thousand people) on average per year, number of unemployed (thousand people) on average per year, the volume of investments in fixed assets (million rubles) per year, retail trade turnover (million rubles) per year, goods of own production were shipped, works and services performed on their own per year (excluding VAT, excises and similar mandatory payments) million rubles. The methodology used includes: bringing monetary indicators for the period under review to the prices of 2021 in accordance with the deflator indices of the gross domestic product of the Russian Federation;
reduction of the original set of non-orthogonal variables using factor analysis to a small-sized orthogonal factor space; determination of the Russian Federation federal districts centroids by the center of mass method for each year of the period under review; analysis of the Siberian Federal District internal structure dynamics and its position.
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Факторный, дискриминантный и кластерный анализ / пер. с англ. Дж.-О. Ким, Ч. У. Мьюллер, У. Р. Клекка и др.; под ред. И. С. Енюкова. М., 1989.
Brown T. A. Confirmatory factor analysis for applied research. Guilford Press, 2006.
API documentation — factor_analyzer 0.4.0 documentation (factor-analyzer.readthedocs.io) URL: https://factor-analyzer.readthedocs.io/en/latest/factor_analyzer.html (дата обращения: 31.03.2023).
Перова В. И., Незнакомцева О. Ю. Исследование динамики социально-экономического развития регионов Российской Федерации // Вестник Нижегородского университета им. Н. И. Лобачевского. Серия: Социальные науки. 2016. № 4 (44). URL: https://cyberleninka.ru/article/n/issledovanie-dinamiki-sotsialno-ekonomicheskogo-razvitiya-regionov-rossiyskoy-federatsii/ (дата обращения: 31.03.2023).
Пискун Е. И., Хохлов В. В. Экономическое развитие регионов Российской Федерации: факторно-кластерный анализ // Экономика региона. 2019. № 2. URL: https://cyberleninka.ru/article/n/ekonomicheskoe-razvitie-regionov-rossiyskoy-federatsii-faktorno-klasternyy-analiz/ (дата обращения: 31.03.2023).
Viktor Soltes, Katarina Repkova Stofkova, Milan Kutaj. Socio-economic Analysis of Development of Regions // November 2016 Global Journal of Business Economics and Management Current Issues 6 (2):171 URL: https://www.researchgate.net/publication/315989267_Socio-economic_Analysis_of_Development_of_Regions/ (дата обращения: 31.03.2023).
Regional socioeconomic developments — statistics. URL: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Regional_socioeconomic_developments_-_statistics#Relative_size_of_the_working-age_population/ (дата обращения: 31.03.2023).
Зубаревич Н. В. Социальная дифференциация регионов и городов России. URL: http://gtmarket.ru/laboratory/expertize/5278/ (дата обращения: 31.03.2023).
Латышева М. А. Статистическое исследование дифференциации российских регионов по уровню социально-экономического развития // Вестник Волгоградского университета. Серия 3: Экономика. Экология. 2010. № 1. С. 89-92.
Псарев В. И., Юдинцев А. Ю., Трошкина Г. Н. Исследование социально-экономических различий субъектов Сибирского федерального округа методом кластерного анализа // Известия Алтайского государственного университета. 2015. Т. 1. № 2 (86). С. 128-134.
Трошкина Г. Н., Юдинцев А. Ю., Межов С. И. Исследование динамики уровня экономической безопасности регионов Сибирского федерального округа Российской Федерации за период 2014-2017 год методами многомерного анализа данных // Российский экономический интернет-журнал. 2019. № 4. URL: https://www.e-rej.ru/Articles/2019/Yudintsev_A.pdf/ (дата обращения: 31.03.2023).
Юдинцев А. Ю., Трошкина Г. Н. Формирование пространства показателей для анализа динамики уровня экономической безопасности регионов Российской Федерации за период 2014-2017 год // Российский экономический интернет-журнал. 2019. № 4. URL: https://www.e-rej.ru/Articles/2019/Yudintsev.pdf / (дата обращения: 31.03.2023).
Yudintsev A. Y., Troshkina G. N. Socio-economic Development of Russian Federal Entities in 2019: Multivariate Data Analysis // In: Maximova, S. G., Raikin, R. I., Chibilev, A. A., Silantyeva, M. M. (eds) Advances in Natural, Human-Made, and Coupled Human-Natural Systems Research. Lecture Notes in Networks and Systems. 2023. Vol. 234. Springer, Cham. https://doi.org/10.1007/978-3-030-75483-9_14
Юдинцев А. Ю., Трошкина Г. Н. Исследование уровня социально-экономического развития регионов Российской Федерации методами многомерного анализа данных // Известия Алтайского государственного университета. 2023. № 1 (129). С. 145-149 URL: http://izvestiya.asu.ru/article/view/%282023%291-24/ (дата обращения: 31.03.2023). DOI: 10.14258/izvasu (2023) 1-24.
Информация для ведения мониторинга социально-экономического положения субъектов Российской Федерации в январе — декабре 2022 года. Федеральной службы государственной статистики. URL: https://rosstat.gov.ru/storage/mediabank/info-stat-12-2022. rar/ (дата обращения: 31.03.2023).
Федеральная служба государственной статистики. Национальные счета. Валовой внутренний продукт. URL: https://rosstat.gov.ru/storage/mediabank/VVP_god_s_1995-2022.xls (дата обращения: 31.03.2023).
REFERENCES
Factor, discriminant and cluster analysis: Per. from English/J.-O. Kim, C. W. Muller, W. R. Klekka and others; Ed. I. S. Enyukov. Moscow, 1989.
Brown T. A. Confirmatory factor analysis for applied research. Guilford Press, 2006.
API documentation — factor_analyzer 0.4.0 documentation (factor-analyzer.readthedocs.io). URL: https://factor-analyzer.readthedocs.io/en/latest/factor_analyzer.html/ (date of access: 31.03.2023).
Perova V. I., Neznakomtseva O. Yu. Study of the dynamics of socio-economic development of the regions of the Russian Federation. Bulletin of the Nizhny Novgorod University. N. I. Lobachevsky. Series: Social Sciences. 2016. No. 4 (44). URL: https://cyberleninka.ru/article/n/issledovanie-dinamiki-sotsialno-ekonomicheskogo-razvitiya-regionov-rossiyskoy-federatsii/ (date of access: 31.03.2023).
Piskun E. I., Khokhlov V. V. Economic development of the regions of the Russian Federation: factor cluster analysis. Economics of the region. 2019. № 2. URL: https://cyberleninka.ru/article/n/ekonomicheskoe-razvitie-regionov-rossiyskoy-federatsii-faktorno-klasternyy-analiz/ (date of access: 31.03.2023).
Viktor Soltes, Katarina Repkova Stofkova, Milan Kutaj. Socio-economic Analysis of Development of Regions. November 2016 Global Journal of Business Economics and Management Current Issues 6 (2):171. URL: https://www.researchgate.net/publication/315989267_Socio-economic_Analysis_of_Development_of_Regions/ (date of access: 31.03.2023).
Regional socioeconomic developments — statistics. URL: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Regional_socioeconomic_developments_-_statistics#Relative_size_of_the_working-age_population/ (date of access: 31.03.2023).
Zubarevich N. V. Social differentiation of regions and cities of Russia. URL: http://gtmarket.ru/laboratory/expertize/5278/ (date of access: 31.03.2023).
Latysheva M. A. Statistical study of the differentiation of Russian regions in terms of socio-economic development. Bulletin of the Volgograd University. Series 3: Economy. Ecology. 2010. No. 1. Pp. 89-92.
Psarev V. I., Yudintsev A. Yu., Troshkina G. N. Study of socio-economic differences of the subjects of the Siberian Federal District by the method of cluster analysis. Proceedings of the Altai State University. 2015. Vol. 1. No. 2 (86). Pp. 128-134.
Troshkina G. N., Yudintsev A. Yu., Mezhov S. I. Study of the dynamics of the level of economic security of the regions of the Siberian Federal District of the Russian Federation for the period 2014-2017 using the methods of multivariate data analysis. Russian Economic Internet Journal. 2019. No. 4. URL: https://www.e-rej.ru/Articles/2019/Yudintsev_A.pdf/ (date of access: 31.03.2023).
Yudintsev A. Yu., Troshkina G. N. Formation of the space of indicators for analyzing the dynamics of the level of economic security of the regions of the Russian Federation for the period 2014-2017. Russian Economic Internet Journal. 2019. No. 4. URL: https://www.e-rej.ru/Articles/2019/Yudintsev.pdf/ (date of access: 31.03.2023).
Yudintsev A. Y., Troshkina G. N. Socio-economic Development of Russian Federal Entities in 2019: Multivariate Data Analysis. In: Maximova, S. G., Raikin, R. I., Chibilev, A. A., Silantyeva, M. M. (eds) Advances in Natural, Human-Made, and Coupled Human-Natural Systems Research. Lecture Notes in Networks and Systems. 2023. Vol. 234. Springer, Cham. URL: https://doi.org/10.1007/978-3-030-75483-9_14/ (date of access: 31.03.2023).
Yudintsev A. Yu., Troshkina G. N. Study of the level of socio-economic development of the regions of the Russian Federation by methods of multivariate data analysis. Bulletin of the Altai State University. 2023. No. 1 (129). Pp. 145-149 DOI: 10.14258/izvasu (2023) 1-24. URL: http://izvestiya.asu.ru/article/view/%282023%291-24/ (date of access: 31.03.2023).
Information for monitoring the socio-economic situation of the constituent entities of the Russian Federation in January — December 2022. Federal State Statistics Service. URL: https://rosstat.gov.ru/storage/mediabank/info-stat-12-2022.rar/ (date of access: 31.03.2023).
Federal State Statistics Service. National accounts. Gross domestic product. URL: https://rosstat.gov.ru/storage/mediabank/VVP_god_s_1995-2022.xls/ (date of access: 03/31/2023).
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