Econometric Analysis of the Labor Market in the North Caucasus Region

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Abstract

The purpose of this study is an econometric analysis of the labor market in the North Caucasus region in the Russian Federation. The article examines the main indicators characterizing the labor market, such as the average monthly nominal accrued wages of workers across the entire spectrum of economic organizations as a whole by business entities, the real average monthly accrued wages of workers, the share of the number of workers employed in work with harmful and/or dangerous conditions labor in organizations, Labor productivity index, Level of innovative activity of organizations, Degree of depreciation of fixed assets in the constituent entities of the Russian Federation of the Russian Federation across the entire spectrum of organizations, Consumer price indices for all goods and services by subject at the end of the period, Number of graduates of higher educational institutions that have a direct impact on the level of unemployment and labor force in the region. The relevance of the chosen topic is due to the study of the role of these indicators in the analysis of the region’s activities in an economic and social key. The structure of the article provides for a consistent presentation of the results of the analysis of each of the models, an assessment of their adequacy and explanatory power, as well as an interpretation of the obtained modeling results. Particular attention is paid to how changes in economic indicators and policies can affect the labor market and unemployment rates in the region. In conclusion, conclusions based on the results of the study are formulated and recommendations are proposed to stimulate economic growth and reduce unemployment in the North Caucasus Federal District.

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About the authors

Ruslan B. Dzgoev

ATB Bank

Email: rusdzgoev@yandex.ru

специалист по работе с крупными иностранными клиентами

Russian Federation, Moscow

Lev A. Krasulin

Financial University under the Government of the Russian Federation; JSC «Norsi-trans»

Email: l.a.krasulin@gmail.com
SPIN-code: 7137-8206

Faculty of Information Technologies and Big Data Analysis, Technical support specialist

Russian Federation, Moscow; Moscow

Ilona V. Tregub

Financial University under the Government of the Russian Federation

Author for correspondence.
Email: itregub@fa.ru
ORCID iD: 0000-0001-7329-8344
SPIN-code: 2192-9453
Scopus Author ID: 57189715735
ResearcherId: A-5855-2017

Dr. Sci. (Econ.), Professor

Russian Federation, Moscow

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Supplementary files

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2. Fig. 1. Confidence interval to check the adequacy of the model 3.

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3. Fig. 2. Confidence interval to check the adequacy of the model 4.

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4. Fig. 3. Confidence interval for checking the adequacy of model 5.

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5. Fig. 4. Confidence interval for checking the adequacy of the model 6.

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