Impact assessment of the COVID-19 on trade between Eastern Europe and China

Authors

  • Dr. Nikolay Megits Webster University, St. Louis, MO https://orcid.org/0000-0002-3959-7647
  • Dr. Inna Neskorodieva Kharkiv University
  • Dr. Julian Schuster Webster University, St. Louis, MO

DOI:

https://doi.org/10.15549/jeecar.v7i3.579

Keywords:

China, Eastern Europe, COVID19, international trade

Abstract

The high-risk of the rapidly spreading COVID-19 virus worldwide created a necessity for developing a diagnostic tool designed to predict economic development, considering the risks of spreading the coronavirus epidemic. In the proposed research, China is selected strategically due to the U.S. "Buy American" trade policy. Also, the European Union presents various trade barriers for countries of Eastern Europe. The risk-versus-economic efficiency study is performed based on Fibonacci law utilizing trade-dynamic indicators with incorporating the SIR-model used to predict the dynamics of COVID-19 cases in the region. The research was performed based on data collected for the period of March-July 2020. As a result, a scientific model to predict the dynamics of trade volume between China and selected Eastern European countries is developed. The results obtained have a practical application and can be used for government institutions and economic agencies to determine their nation's short- and long-term international trade strategy. 

References

Amur Info. (2020). Russia is losing 1 bill rubles per day due to coronavirus in China (Article in Russian). Retrieved from https://www.amur.info/news/2020/02/21/168292

Chen, Y.-C., Lu, P.-E., Chang, C.-S., & Liu, T.-H. (2020). A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons. Retrieved from https://arxiv.org/abs/2003.00122.

E.C. (2020b). The impact of the COVID-19 pandemic on global and E.U. trade. Retrieved from https://trade.ec.europa.eu/doclib/docs/2020/april/tradoc_158713.pdf

E.U. (2020). Commission Implementing Regulation (E.U.) 2020/426 of March 19 2020, amending Implementing Regulation (E.U.) 2020/402 making the exportation of certain products subject to the production of an export authorization.

Evans, D. K., Goldstein, M., & Popova, A. (2015). The Next Wave of Deaths from Ebola? The Impact of Health Care Worker Mortality. Policy Research working paper, no. WPS 7344. Washington, DC: World Bank Group. DOI: https://doi.org/10.1596/1813-9450-7344

Imbruno, M. (2020). Importing under trade policy uncertainty: Evidence from China. Journal of Comparative Economics, 47(4), 806-826. DOI: https://doi.org/10.1016/j.jce.2019.06.004

Investing. (2020). ????????? ????????? ? ???????? ???????. Retrieved from https://ru.investing.com/commodities/real-time-futures.

Jin, P., Lu, L., Tang, Y., & Karniadakis, G.E. (2020). Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Neural Networks, 130, 85-99. DOI: https://doi.org/10.1016/j.neunet.2020.06.024

Keogh-Brown, M., Wren-Lewis, S., Edmunds, W. J., Beutels, P., & Smith, R. D. (2009). The possible macroeconomic impact on the U.K. of an influenza pandemic. Health Economics, 19(11), 1345-1360.

Knoema. (2020). Global Markets Moved by coronavirus Outbreak. Retrieved from https://knoema.com/lwdhxyc/global-markets-moved-by-china-coronavirus-outbreak.

Megits, N. (2016). The Impact of Russia-China Trade Relationship on the U.S. Economy. Journal of Eastern European and Central Asian Research, 3(2), 12. Obtained from: DOI: https://doi.org/10.15549/jeecar.v3i2.135 DOI: https://doi.org/10.15549/jeecar.v3i2.135

Neskorodeva, I.?., & Pustovgar, S.?. (2015). An Approach to Predicting the Insolvency of Ukrainian Steel Enterprises Based on Financial Potential. Journal of Eastern European and Central Asian Research, 2(2), 33-43. Obtained from: DOI: https://doi.org/10.15549/jeecar.v2i2.104 DOI: https://doi.org/10.15549/jeecar.v2i2.104

Nesteruk, I. (2020). Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China. Innov Biosyst Bioeng, 4(1), 13-18. Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research. DOI: 10.1016/j.ejor.2020.08.001. DOI: https://doi.org/10.1016/j.ejor.2020.08.001

OCHA. (2020). COVID-19 Notes on Humanitarian Crises - An ICMHD Health Policy Contribution. Retrieved from https://reliefweb.int/report/world/COVID-19-notes-humanitarian-crises-icmhd-health-policy-contribution

OECD. (2020a). Tackling coronavirus (COVID?19) Contributing to a global effort. Retrieved from http://www.oecd.org/coronavirus/en/

OECD. (2020b). COVID-19 crisis response in Eastern Partner countries. Retrieved from https://www.oecd.org/coronavirus/policy-responses/COVID-19-crisis-response-in-eu-eastern-partner-countries-7759afa3/

?ahin, U., & ?ahin, T. (2020). Forecasting the cumulative number of confirmed cases of COVID-19 in Italy, U.K. and USA using fractional nonlinear grey Bernoulli model. Chaos, Solitons & Fractals, 138, 109948.

Song, M-L., Cao, S.-P., & Wang, S.-H. (2019). The impact of knowledge trade on sustainable development and environment-biased technical progress. Technological Forecasting and Social Change, 144(C), 512-523. DOI: https://doi.org/10.1016/j.techfore.2018.02.017

The Economist (August 2020). Video presentation: COVID-19: The road ahead for eastern Europe, Retrieved from https://economist.zoom.us/rec/play/vMUvduH8pzw3EtSStASDA6R6W9XreK2sgCce8qYMmR3nBiJXYAevYrUTMLaLInOLr65w2tSuVkVliZ5W?startTime=1597917927000&_x_zm_rtaid=D5Ay_Mb9R-2aSOamOY2HYA.1598969136108.03de3f0bb39d1e023876ce6eb5a516bc&_x_zm_rhtaid=520

Tian, J., Sim, N., Yan, W., & Li, Y. (2020). Trade uncertainty, income, and democracy. Economic Modelling, 90, 21-31. DOI: https://doi.org/10.1016/j.econmod.2020.04.022

Trend Economy. (2020). Trade Data Insights. Retrieved from https://trendeconomy.ru/trade

Vaishnav, V., & Vajpai, J. (2020). Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling. Chaos, Solitons & Fractals, 140, 110191. DOI: https://doi.org/10.1016/j.chaos.2020.110191

Vasiljeva, M., Neskorodieva, I., Ponkratov, V., Kuznetsov, N., Ivlev, V., Ivleva, M., Maramygin, M., & Zekiy, A. (2020). A Predictive Model for Assessing the Impact of the COVID-19 Pandemic on the Economies of Some Eastern European Countries. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), 92. DOI: 10.3390/joitmc6030092 DOI: https://doi.org/10.3390/joitmc6030092

WIIW. (2020). Eastern Europe Coronavirus tracker: Preparing for the worst. Retrieved from https://wiiw.ac.at/eastern-europe-coronavirus-tracker-preparing-for-the-worst-n-430.html

WTO. (2020a). Trade set to plunge as COVID-19 pandemic upends global economy. Retrieved from https://www.wto.org/english/news_e/pres20_e/pr855_e.htm

WTO. (2020b). Goods barometer confirms steep drop in trade but hints at nascent recovery. Retrieved from https://www.wto.org/english/news_e/news20_e/wtoi_19aug20_e.htm

WTO. (2020c). Documents for meetings. Retrieved from https://docs.wto.org/dol2fe/Pages/FE_Search/FE_S_S006.aspx?DataSource=Cat&Query=@MeetingId=160705&Language=English&Context=ScriptedSearches&languageUIChanged=true.

Yates, C. (2020). How to model a pandemic. The Conversation. Retrieved from https://theconversation.com/how-to-model-a-pandemic-134187.

Zhou, Y., Chen, S., & Chen, M. (2019). Global value chain, regional trade networks and Sino-EU FTA. Structural Change and Economic Dynamics, 50, 26-38. DOI: https://doi.org/10.1016/j.strueco.2019.04.010

Published

2020-12-01

How to Cite

Megits, N., Neskorodieva, I., & Schuster, J. (2020). Impact assessment of the COVID-19 on trade between Eastern Europe and China. Journal of Eastern European and Central Asian Research (JEECAR), 7(3), 385–399. https://doi.org/10.15549/jeecar.v7i3.579

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