Clustering Macroeconomic Impact of Covid-19 in OECD Countries and China

Abstract

The coronavirus pandemic (COVID-19) has caused the biggest economic contraction in global economy since the Second World War. COVID-19 pandemic has forced governments to take unprecedented measures to prevent the spread and to protect their economies that presented a dilemma because of their conflicting outcomes. This paper investigates the presumption of health-economy trade-off due to COVID-19 by comparing the GDP declines and deaths in per million population in OECD countries and China. The empiric data shows the countries with the highest death rates have seen the largest economic downturns. The clustering analysis by using k-means algorithm finds that there are three partitions of countries for current account balances, GDP growth rate, and deaths in per million population. The countries with current account surpluses above 2.5% of GDP managed to limit their GDP decline below -15% and are in the same cluster. On the other hand, the countries with higher death rates and current account deficits group another cluster and saw GDP declines as above 15% except for USA and Brasil.

Keywords

• Clustering
• K-Means clustering
• Regression Analysis
• COVID-19

Covid-19’un OECD Ülkeleri ve Çin’de Makroekonomik Etkisinin Kümeleme Analizi

Abstract

Koronavirüs salgını (COVID-19), İkinci Dünya Savaşı'ndan bu yana küresel ekonomideki en büyük ekonomik daralmaya neden oldu. COVID-19 pandemisi, hükümetleri hem bu hastalığın yayılmasını önlemek hem de ekonomilerini korumaya çalışmak gibi birbiri ile çelişki içinde görünen amaçlar için benzeri görülmemiş önlemler almaya zorladı. Bu makale, OECD ülkeleri ve Çin’de GSYİH düşüşlerini ve milyon kişi başına düşen ölümleri karşılaştırarak COVID-19 nedeniyle bir sağlık-ekonomi değiş tokuşu olup olmadığı varsayımını araştırmaktadır. Ampirik veriler, en yüksek ölüm oranlarına sahip ülkelerin en büyük ekonomik gerilemeleri yaşadığını göstermektedir. K-ortalamalar algoritması kullanılarak yapılan kümeleme analizi, cari hesap dengesi, GSYİH büyümesi ve bir milyon kişi başına düşen ölüm sayısı açısından ülkelerin üç bölüme ayrıldığını bulmuştur. Cari hesap fazlası GSYH’nin %2,5'inin üzerinde olan ülkeler, GSYİH düşüşlerini % -15'in altında sınırlamayı başardılar ve aynı kümede yer almaktadırlar. Öte yandan, ölüm oranları ve cari açıkları yüksek olan ülkeler başka bir kümede yer alırlar ve bu ülkelerin GSYİH, ABD ve Brezilya dışında, %15'in üzerinde düşmüştür.

Keywords

• COVID-19
• Kümeleme
• K-Ortalama kümelemesi
• Regresyon analizi

References

1. Acemoglu, D., Chernozhukov, V., Werning, I. and Whinston, M.D. (2020). Optimal targeted lockdowns in a multi-group SIR model (NBER Working Paper No. w27102). Retrieved from: https://www.nber.org/w27102
2. Anderson, R. M., Heesterbeek, H., Klinkenberg, D. and Hollingsworth, T. (2020). How will country-based mitigation measures influence the course of the covid-19 epidemic? The Lancet, 395(10228), 931-934. https://doi.org/10.1016/S0140-6736(20)30567-5
3. Arthur, D. and Vassilvitskii, S. (2006, June). How slow is the K-means method?. In N Amenta and O Cheong (Eds.), Proceedings of the 22. Annual Symposium on Computational Geometry (pp.144-153). Papers presented at 22nd ACM Symposium on Computational Geometry (SoCG), Arizona. New York: Association for Computing Machinery.
4. Atkeson, A. (2020). What will be the economic impact of COVID-19 in the US? Rough estimates of disease scenarios (NBER Working Paper No. w26867). Retrieved from https://www.nber.org/w26867
5. Aydin, N. and Yurdakul, G. (2020). Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms. Applied Soft Computing, 97, 106792. https://doi.org/10.1016/j.asoc.2020.106792
6. Brodeur, A., Gray, D., Islam, A. and Bhuiyan, S. (2020), A literature review of the economics of Covid-19 (IZA Discussion Paper, No. 13411). Retrieved from https://www.econstor.eu/bitstream/10419/222316/1/GLO-DP-0601.pdf
7. Chaudhry, R., Dranitsaris, G., Mubashir, T., Bartoszko J. and Riazi, S. (2020). A country level analysis measuring the impact of government actions, country preparedness and socioeconomic factors on COVID-19 mortalityand related health outcomes. EClinical Medicine, 25, 100464. doi:10.1016/j.eclinm.2020.100464
8. Coibion, O., Gorodnichenko, Y. and Weber, M. (2020). How US consumers use their stimulus payments. VoxEU & CEPR Coverage of the Covid-19 Global Pandemic. Retrieved from https://european.economicblogs.org/voxeu/2020/gorodnichenko-weber-us-consumers-stimulus-payments

9. Danielli, S., Patria, R., Donnelly, P., Ashrafian, H. and Darzi, A. (2020). Economic interventions to ameliorate the impact of COVID-19 on the economy and health: an international comparison. Journal of Public Health, 104. https://doi.org/10.1093/pubmed/fdaa10
10. Elgin, C., Basbug, G. and Yalaman, A. (2020). Economic policy responses to a pandemic: developing the Covid-19 economic stimulus index. Covid Economics, 1(3), 40-53. Retrieved from http://www.ceyhunelgin.com/
11. Fernandes, N. (2020). Economic effects of Coronavirus outbreak (COVID-19) on the world economy (IESE Business School Working Paper No. WP-1240-E). Retrieved from https://ssrn.com/abstract=3557504
12. Imtyaz, A., Haleem, A. and Javaid, M. (2020). Analysing governmental response to the COVID-19 pandemic. Journal of Oral Biology and Craniofacial Research, 10(4), 504–513. https://doi.org/10.1016/j.jobcr.2020.08.005
13. International Monetary Fund. (2020). Policy-responses-to-COVID-19. Retrieved from https://www.imf.org/en/Topic s/imf-and-covid19/Policy-Responses-to-COVID-19#U
14. John Hopkins Coronavirus Resource Center. (2020). John Hopkins University & Medicine [Dataset]. Retrieved from https://coronavirus.jhu.edu.
15. Lloyd, S. P. (1982). Least squares quantization in pcm. IEEE Transactions on Information Theory, 28(2), 129-136. doi:10.1109/TIT.1982.1056489
16. Naeem S. and Wumaier, A. (2018). Study and implementing K-mean clustering algorithm of English text and techniques to fine the optimal value of K. Industrial Journal of Computer Applications, 182, 31, 7-14. Retrieved from https://www.ijcaonline.org/
17. Organisation for Economic Co-operation and Development. (2020). OECD economic outlook. Retrieved from https://www.oecd-ilibrary.org/
18. R Core Team. (2018). R: A language and environment for statistical computing. Retrieved from https://www.R-project.org
19. Seetharaman, P. (2020). Business models shifts: Impact of Covid-19. International Journal of Information Management, 54, 102173. https://doi.org/10.1016/j.ijinfomgt.2020.102173
20. Serafini, G., Parmigiani, B., Amerio A., Aguglia, A., Sher L. and Amore M. (2020). The psychological impact of COVID-19 on the mental health in the general population. QJM: An International Journal of Medicine, 113(8), 531–37. https://doi.org/10.1093/qjmed/hcaa201.
21. Statista. (2020). Number of cumulative cases of coronavirus (COVID-19) worldwide from January 8 to October 7, 2020, by day [Dataset]. Retrieved from https://www.statista.com/statistics/1103040/cumulative-coronavirus-covid19-cases-number-worldwide-by-day.
22. Sun, Z., Zhang, H., Yang, Y., Wan, H. and Wang, Y. (2020). Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China. Science of The Total Environment. 746, 141347. https://doi.org/10.1016/j.scitotenv.2020.141347,
23. Tisdell, C. A. (2020). Economic, social and political issues raised by the COVID-19 pandemic. Economic Analysis and Policy 68, 17–28. https://doi.org/10.1016/j.eap.2020.08.002
24. World Health Organization. (2020). World health organization data [Dataset]. Retrieved from https://who.int/data