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Special Economic Zones of Russia: Forecasting Decisions of Potential Residents and Resident Generation Process Modeling

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  • Alexander E. Plesovskikh

Abstract

Modern studies widely discuss the role of special economic zones in stimulating the economic growth and development of Russia, generating the necessary investment flows and increasing the country's innovative potential by expanding production in high-tech sectors of the economy with high added value. The purpose of the study is to model the process of generating residents and to determine quantitative factors that have a statistically significant effect on the average annual growth rate of companies participating in special economic zones in the Russian Federation. The paper describes modern approaches to predicting the choice of potential residents to start doing business in the territory of the SEZ using classification approaches (Support Vector Machines, Decision Trees, Random Forest, Naive Bayes, K-Nearest Neighbor, Gradient Boosting) and regression approaches (logistic regression). A modern classification algorithm was applied in practice - Histogram-based Gradient Boosting Classification Tree, which is stable for analyzing large data with missing variable values and does not require preliminary sample transformation. The paper confirms the hypothesis that there is a positive relationship between the location of the organization and its financial result forming by the end of the year. On average, in the sample, resident companies located near the centers of the constituent entities of the Russian Federation are more successful in terms of generated revenue. The hypothesis that there is a strong relationship between indicators of spatial differentiation of the regions of the Russian Federation and indicators characterizing the process of generating residents and private investment has not been fully confirmed. From a practical point of view, the results of the study could be applied by both resident organizations, potential residents, and SEZ management companies. The theoretical significance of the study lies in the specification of the proposed binary choice model for potential residents, which can be expanded and generalized in future works. At present, there are all the necessary prerequisites for creating conditions for the development of industry, high-tech sectors of the economy and the production of high value-added products in order to increase the stability of the Russian economy.

Suggested Citation

  • Alexander E. Plesovskikh, 2023. "Special Economic Zones of Russia: Forecasting Decisions of Potential Residents and Resident Generation Process Modeling," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(2), pages 323-354.
    • Handle: RePEc:aiy:jnjaer:v:22:y:2023:i:2:p:323-354
    DOI: http://dx.doi.org/10.15826/vestnik.2023.22.2.014
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    References listed on IDEAS

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    More about this item

    Keywords

    Russian special economic zones; resident generation; machine learning; regression and classification; binary choice models.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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