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Experimental System-Dynamic Model of an Influence of a Level of Education on a Spatial Differentiation of a Population of Russian Regions

Author

Listed:
  • Vladimir N. Timokhin
  • Dmitry B. Berg
  • Andrei G. Shelomentsev

Abstract

The study is devoted to the problem of the spatial differentiation of population income in Russia's regions. The objective of the study is the development of a system-dynamic model for the calculation of spatial differentiation trajectories of income parameters due to various scenarios. Regional features of the human capital development level influencing the spatial differentiation of population income in the Russian regions are assumed. The formulation of the mathematical problem is based on the results of regression analysis of the influence of time series values of socio-demographic factors on population incomes differentiation (Gini coefficient). A specialist application, PowerSim Studio Express 10, was used for model design. Rosstat data on households in the Russian regions were used for calculation of the model experimental trajectories. The main research methods are the following: dynamic analysis of time series; econometric and system-dynamic modeling. As a result of the study, a system-dynamics experimental model was proposed. It was tested in application to eight regions with the most reliable statistical relationship between socio-demographic factors and the Gini index. Numerical experiments were used to simulate real economic processes of convergence and divergence in order to identify the main trends and features of territorial income differentiation depending on local priorities of vocational education development. It was shown that an increase in the level of education, both higher professional and secondary vocational, mainly leads to increased income differentiation. The theoretical significance of the results obtained lies in the deepening of the understanding of the regional features of human capital development influencing the spatial differentiation of population income. The practical significance of the study lies in the expansion of instrumental support for decision-making in the implementation of state policy in the field of regulating the population incomes differentiation at the regional level.

Suggested Citation

  • Vladimir N. Timokhin & Dmitry B. Berg & Andrei G. Shelomentsev, 2023. "Experimental System-Dynamic Model of an Influence of a Level of Education on a Spatial Differentiation of a Population of Russian Regions," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(4), pages 861-891.
  • Handle: RePEc:aiy:jnjaer:v:22:y:2023:i:4:p:861-891
    DOI: https://doi.org/10.15826/vestnik.2023.22.4.035
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    References listed on IDEAS

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    1. Mark D. Partridge, 2005. "Does Income Distribution Affect U.S. State Economic Growth?," Journal of Regional Science, Wiley Blackwell, vol. 45(2), pages 363-394, May.
    2. Alberto Alesina & Dani Rodrik, 1994. "Distributive Politics and Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 109(2), pages 465-490.
    3. Altunbaş, Yener & Thornton, John, 2019. "The impact of financial development on income inequality: A quantile regression approach," Economics Letters, Elsevier, vol. 175(C), pages 51-56.
    4. Jay W. Forrester, 2016. "Learning through System Dynamics as Preparation for the 21st Century," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 187-203, July.
    5. Ruslan Grigoryev & Marat Kramin & Timur Kramin & Asiya Timiryasova, 2015. "Inequality of Income Distribution and Economics Growth in the Regions of Russia in the Post-Crisis Period," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(3), pages 102-113.
    6. Barro, Robert J, 2000. "Inequality and Growth in a Panel of Countries," Journal of Economic Growth, Springer, vol. 5(1), pages 5-32, March.
    7. Vyacheslav Lokosov & Yelena Ryumina & Vladimir Ulyanov, 2015. "Regional Differentiation of Human Potential Indicators," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(4), pages 185-196.
    8. Nie, Haifeng & Xing, Chunbing, 2019. "Education expansion, assortative marriage, and income inequality in China," China Economic Review, Elsevier, vol. 55(C), pages 37-51.
    9. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    10. S. A. Suspitsyn, 2021. "Set of Methods and Procedures for Analyzing and Forecasting the Development of the Eastern Regions of the Russian Federation," Regional Research of Russia, Springer, vol. 11(1), pages 65-77, December.
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    More about this item

    Keywords

    system dynamics; simulation modeling; development scenarios; experimental trajectories; territorial disproportions; differentiation of living standards; convergence/divergence of income of the population.;
    All these keywords.

    JEL classification:

    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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