USING SOCIAL INDICATORS TO MONITOR THE LEVEL OF REGIONAL DEVELOPMENT

  • M.A. Barinov Vladimir State University named after Alexander and Nikolay Stoletovs, Vladimir, Russia
DOI: https://doi.org/10.34680/BENEFICIUM.2024.4(53).74-82

Abstract

The scientific article develops and examines methodological approaches to monitoring and evaluating basic criteria that allow diagnosing medical and demographic factors at the regional level that characterize the quality of human resources and their impact on the mesoeconomical development of individual territories. The relevance of the research topic is explained by the interest of researchers and a sufficiently large number of methods for diagnosing administrative-territorial entities at the regional level, which would identify social factors taking into account the spatial parameter. The study was conducted on data from the Volga Federal District. The aim of the study is to identify regional economic systems based on cluster tools, which made it possible to group mesoterritoriums by medical and demographic factors. The division into groups was carried out: favorites, catching up and outsiders, according to the K-averages method for indicators of social orientation, which take into account demographic and medical components, which allows us to characterize and econometrically substantiate the ongoing transformations in regional systems. The assessment of factor characteristics on economic processes and the results of the functioning of administrative-territorial entities was carried out with a graphical representation of the dependence of social orientation factors and Gross regional product in the form of a scattering diagram, which displays the distribution of elements of the set within the planes of two-dimensional space according to the boundaries of the selected confidence interval, taking into account the degree of significance. The author identified and justified the components of the regional socio-economic system, which are associated with medical and demographic orientation, confirming and justifying their correlation with the quality of life and the resulting indicator of the development of the region's economy. A reliable econometric model has been formed, on the basis of which it is possible to make forecasts and monitor the functioning of regions. The proposed and justified tools, as well as reliable models, can be used during the formation of strategic priorities by executive authorities at the regional level.

Keywords: gross regional product, development, social indicators

References

  • Usacheva E.V., Kulikova O.M., Nelidova A.N., Nakonechnaya E.E., Anipir I.V. Modelirovanie v razrabotke regional`ny`x programm, napravlenny`x na povy`shenie e`ffektivnosti funkcionirovaniya sistemy` zdravooxraneniya. [Modeling in the development of regional programs aimed at improving the efficiency of the health care system] // Cardiovascular Therapy and Prevention. 2021. Vol. 20(S1). Pp. 87. (In Russ.). DOI: 10.15829/1728-8800-2021-IS
  • Budarin S.S., Elbek Yu.V. Efficiency of Functioning of Health Care Systems as a Subject of State Financial Control: a Review of Russian and Foreign Experience // The Problems of Social Hygiene, Public Health and History of medicine. 2022. Vol. 30(S1). Pp. 976-982. (In Russ.). DOI: 10.32687/0869-866X-2022-30-S1-976-982
  • Neradovskaya Yu.V. Evaluation of effectiveness of regional health care systems // Economics and Management: Scientific and Practical Journal. 2022. Vol. 1(163). Pp. 125-132. (In Russ.). DOI: 10.34773/EU.2022.1.24
  • Pochinok N.B., Kodzokov R.L. Ways to Improve the Socio-Economic Efficiency of the Functioning of the Service Sector on the Example of Health Care in Periods of Instability // Social Policy and Sociology. 2020. Vol.19(2-135). Pp. 39-47. (In Russ.). DOI: 10.17922/2071-3665-2020-19-2-39-47
  • Ulumbekova G.E. Zdravoohranenie Rossii 2022-2023 gg.: neotlozhnye mery v usloviyah osobogo polozheniya v ekonomike i social'noj sfere. Problemy i predlozheniya [Health care in Russia 2022-2023: urgent measures in the conditions of special situation in the economy and social sphere. Problems and proposals] // ORGZDRAV: news, opinions, training. Bulletin of the Higher School of Public Health. 2022. Vol. 8(2). Pp. 4-21. (In Russ.). DOI: 10.33029/2411-8621-2022-8-2-4-21
  • Grudina S.I. Theory of Entropy Development of Regional Socio-Economic Systems // Kazan Economic Vestnik. 2021. Vol. 3(53). Pp. 24-28. (In Russ.).
  • Vorobeva G.R., Vorobev A.V. An Approach to the Recovery of Spatial Data Time Series Based on Information-Correlation and Geostatistical Analysis // Tomsk State University Journal of Control and Computer Science. 2022. Vol. 61. Pp. 37-46. (In Russ.). DOI:17223/19988605/61/4
  • Bazarsky O.V., Kochetova Zh.Yu., Kul`nev V.V., Panteleev D.A. Entropy Model of Dynamic Chaos of an Abiotic System // Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki. 2023. Vol. 165(2). Pp. 281-294. (In Russ.). DOI: 10.26907/2542-064X.2023.2.281-294
  • Regiony Rossii. Social'no-ekonomicheskie pokazateli [Regions of Russia. Socio-Economic Indicators] (2023). Federal State Statistics Service (In Russ.). URL: https://rosstat.gov.ru/folder/210/document/13204 (accessed on 09.25.2024).
  • Shestakov R.B., Lovchikova E.I. Clustering of Regions Using Basic Agricultural and Economic Criteria // Economy of Regions. Vol. 19(1). Pp. 178-191. (In Russ.). DOI:17059/ekon.reg.2023-1-14
  • Rakhmetova R., Kaliyeva S., Andekina R., Maxyutova A.Сlustering by the level of demographic potential of the regions of Kazakhstan based on SPSS // Bulletin of Karaganda University. “Economy” Series. 2022. Vol.107(3). С. 129-136. DOI: 10.31489/2022Ec3/129-136
  • Amanulla Khan M., Sithi Shameem Fathima S.M.H. Multi Gait Recognition Using Clustering Based Faster Regions-Convolutional Neural Network // Journal of Intelligent and Fuzzy Systems. 2023. Vol. 44(5). Pp.8597-8606. DOI: 10.3233/jifs-224114
  • Guan Ju., Li Sh., Zhu J., He X., Chen J. Fast Main Density Peak Clustering within Relevant Regions Via a Robust Decision Graph // Pattern Recognition. 2024. Vol.152(11). Pp. 110458. DOI: 1016/j.patcog.2024.110458
  • Haedo Ch., Mouchart M. Two-Mode Clustering through Profiles of Regions and Sectors // Empirical Economics. 2022. Vol. 63(4). Pp. 1971-1996. DOI:1007/s00181-022-02201-z
  • Kaneva M.A., Untura G.A. Regional Growth Rates Dependence on Healthcare Spending: Threshold Regression Estimation // Regional Economicsy: Theory and Practice. 2022. Vol. 20(2-497). Pp. 355-381. (In Russ.). DOI: 10.24891/re.20.2.355
  • Mahmud A., Cushing-Haugen K., Wellman R., Brown M.C., Lewis C.C. Understanding the Relationship between Social Risk Factors and Covid-19 Contacts // The Permanente Journal. 2023. Vol. 27(2). Pp. 18-22. DOI: 10.7812/TPP/22.146

About the Author

Mikhail A. Barinov – Cand. Sci. (Economics), Docent; Associate Professor, Vladimir State University named after Alexander and Nikolay Stoletovs, Vladimir, Russia. E-mail: 3lf84@mail.ru. SPIN РИНЦ 2608-6260. ORCID 0000-0002-7849-742X. Scopus Author ID 57209224971

For citation: Barinov M.A. Using Social Indicators to Monitor the Level of Regional Development // Beneficium. 2024. Vol. 4(53). Pp. 74-82. (In Russ.). DOI: 10.34680/BENEFICIUM.2024.4(53).74-82

Published
2024-11-29
Issue
No 4 (2024)
Section
REGIONAL SUSTAINABLE DEVELOPMENT