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Forecasting of Socio-Economic Development of the Russian Regions

Author

Listed:
  • Galina Gagarina

    (Plekhanov Russian University of Economics)

  • Evgeniy Dzyuba

    (Division of All-Russia People’s Front in Republic of Bashkortostan)

  • Roman Gubarev

    (Russian University of Economics named after G. V. Plekhanov)

  • Fanil Faizullin

    (Institute of Social and Economic Research of the Russian Academy of Sciences, Ufa Scientific Center)

Abstract

The regional differentiation makes impossible the sustainable socio-economic development of the subjects of the Russian Federation without the monitoring public governance results in space and time. Despite the comprehensive approach of the current procedure, approved by the federal government, it does not adequately assess the executive authorities effectiveness. Its main problem is the impossibility to assume such important administrative function as forecasting the social and economic development of Russian territorial subjects. The authors propose an alternative methodology on the basis of the system economic theory. This technique is implemented in several consecutive stages. Firstly, we develop the system of 30 indicators. Secondly, we normalize the values of the indicators using the method of pattern. Thirdly, we calculate the index of the social and economic development of Russian regions for 2011–2015 assuming that the indicators are equal. Last, we group Russian regions into clusters according to the level of their social and economic development using neural network technologies (Kohonen selforganizing maps). Only 9 in 80 subjects of the Russian Federation (RF) had the degree of realizing the social and economic potential higher than 40 % during the period under consideration. In 2011–2015, the most of regions had a low and lower than average level of social and economic development (with an aggregate share about 64.3 %). It means that, under current conditions, the majority of the RF regions have considerable reserves for realizing their social-economic potential. In particular, the absence of the territorial subjects with a high level of social and economic development proves that. The authors have simulated the social and economic situation of the RF subjects by means of an adequate Bayesian neural networks. The obtained results can be used as the basis for further research in the field of evaluating executive authorities effectiveness and forecasting the level of social and economic development of Russian regions.

Suggested Citation

  • Galina Gagarina & Evgeniy Dzyuba & Roman Gubarev & Fanil Faizullin, 2017. "Forecasting of Socio-Economic Development of the Russian Regions," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(4), pages 1080-1094.
    • Handle: RePEc:ura:ecregj:v:1:y:2017:i:4:p:1080-1094
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    References listed on IDEAS

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    Cited by:

    1. Darya S. Bents, 2021. "The effectiveness of a territory’s spatial development as an indicator of regional authorities performance: The case of Chelyabinsk oblast," Upravlenets, Ural State University of Economics, vol. 12(6), pages 49-66, October.
    2. K. G. Sorokozherdyev & K. A. Khodosov, 2020. "The Influence of the Regional Sectoral Structure on the Socio-Economic Development of Primorye Region," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 19(1), pages 60-78.

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    More about this item

    Keywords

    efficiency of public administration; evaluation methodology; spatial development; interregional differentiation; regional socio-economic development; systemic approach; clustering of regions; neuromodulation; multilayer perceptron; Bayesian neural networks;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics

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