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Scenario forecasting of the socio-economic consequences of the COVID-19 pandemic in Russian regions

Author

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  • Ilya V. Naumov

    (Institute of Economics of the Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia; Ural Federal University named after the first President of Russia B. N. Yeltsin, Ekaterinburg, Russia)

  • Sergey S. Krasnykh

    (Institute of Economics of the Ural Branch of Russian Academy of Sciences)

  • Yulia S. Otmakhova

    (Central Economic and Mathematical Institute of the Russian Academy of Sciences, Moscow, Russia)

Abstract

Relevance. There is a perceived lack of methods that can accurately, reliably and comprehensively reflect the epidemiological situation in regions and its impact on their socio-economic development. The approaches that are currently described in research literature do not take into account the multivariance of scenarios of the COVID-19 pandemic, both in time and space. Research objectives.The article aims to present a methodological framework that could be used to predict the socio-economic consequences of the COVID-19 pandemic in regions and to detect the most vulnerable regions. Data and methods. The study relies on a set of methods, including the methods of regression modeling, ARIMA forecasting and spatial correlation analysis. Results. The panel regression analysis has confirmed the negative impact of the pandemic on socio-economic development, in particular, the growth of overdue wage arrears, unemployment, arrears, the number of liquidated organizations, and the industrial production index. We have also identified the most vulnerable regions that need to be prioritized for government support. Conclusions. The resulting models and scenarios can be used by policy-makers to set the priorities of state policy for the economic support of the regions and stabilization of the epidemiological situation in the country.

Suggested Citation

  • Ilya V. Naumov & Sergey S. Krasnykh & Yulia S. Otmakhova, 2022. "Scenario forecasting of the socio-economic consequences of the COVID-19 pandemic in Russian regions," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 8(1), pages 5-20.
    • Handle: RePEc:aiy:journl:v:8:y:2022:i:1:p:5-20
    DOI: https://doi.org/10.15826/recon.2022.8.1.001
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    References listed on IDEAS

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    1. Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan & Nadarajah, Saralees, 2021. "Count regression models for COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
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    More about this item

    Keywords

    scenario forecasting; COVID-19; regression analysis; ARIMA forecasting; spatial correlation analysis;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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