COVID-19 Pandemisinin Sağlık Yönetimi Tarımsal Emtia Fiyatlarının bir Nedeni midir? Bootstrap Fourier Nedensellik Testinden Yeni Kanıtlar

Abstract

Amaç: Koronavirüs hastalığı 2019 (coronavirus disease 2019, COVID-19) pandemisi ulusal ve uluslararası düzeyde hem sağlık yönetiminde hem de ekonomik hayatta olumsuz sonuçlar doğurmuştur. Bu araştırmanın amacı, COVID-19 salgını ile dünya için tarımsal emtia fiyatları arasındaki nedensellik ilişkisini incelemektir. Gereç ve Yöntemler: Bu amaçla, 24 Ocak 2020 - 22 Ocak 2021 dönemi için Toda-Yamamoto ve Fourier Toda-Yamamoto nedensellik testleri kullanılmıştır. Değişkenler arasındaki nedensellik ilişkisini test etmeden önce, maksimum entegrasyon derecesini belirlemek için her bir seriye augmented Dickey Fuller (ADF) ve Fourier ADF birim kök testleri uygulanmıştır. Bulgular: Bulgular, tüm değişkenlerin ilk farklarında durağan olduğunu ve maksimum entegrasyon derecesinin 1 olarak belirlendiğini göstermiştir. Nedensellik testlerinden elde edilen sonuçlar, COVID-19 yeni vakalarının kahve, şeker, pamuk, mısır ve soya fiyatlarının Granger nedeni olduğunu gösterirken COVID-19’a bağlı yeni vakaların buğday ve yulaf fiyatlarının Granger nedeni olmadığını göstermiştir. Ayrıca COVID-19’a bağlı yeni ölümler kahve, şeker ve pamuk fiyatlarının Granger nedeni olurken COVID-19 ölümlerinin ise mısır, soya fasulyesi, buğday ve yulaf fiyatlarının Granger nedeni olmadığı sonucuna varılmıştır. Sonuç: Bu çalışmada, Toda-Yamamoto ve Fourier Toda Yamamoto nedensellik testlerine dayalı zaman serisi analizi dünyadaki COVID-19 toplam yeni vakalarının ve toplam yeni ölümlerin tarımsal emtia fiyatlarını tahmin etme gücüne sahip olduğunu vurgulamaktadır. Bu nedenle sağlık yönetimi açısından politika yapıcılar, COVID-19 pandemisi döneminde COVID-19 ile ilgili sağlık politikaları ve tarım politikalarının birlikte uygulanmasına büyük önem vermelidir.

Keywords

• Koronavirüs
• Halk Sağlığı
• Emtia Piyasaları
• Granger Nedensellik

Is Health Management of the COVID-19 Pandemic a Cause of Agricultural Commodity Prices? New Evidences From Bootstrap Fourier Causality Test

Abstract

Aim: Coronavirus disease 2019 (COVID-19) pandemic have included negative consequences both in health management and economic life at national and international level. The aim of this research is to examine the causal relationship between COVID-19 pandemic and agricultural commodity prices for the world. Material and Methods: To this end, we employ Toda-Yamamoto and Fourier Toda-Yamamoto causality tests for the period of January 24, 2020 to January 22, 2021. Before testing the causal relationship between variables, we apply augmented Dickey Fuller (ADF) and Fourier ADF unit root tests to each series to determine maximum order of integration. Results: The findings show that all variables are stationary in their first difference and the maximum order of integration is determined as 1. The results obtained from causality tests show that COVID-19 new cases Granger cause to coffee, sugar, cotton, corn, and soybean prices while COVID-19 new cases do not cause wheat and oats prices. It was also concluded that new deaths based on COVID-19 Granger cause to coffee, sugar, and cotton whereas COVID-19 new deaths do not cause to corn, soybean, wheat and oat prices. Conclusion: In this study, time series analysis based on Toda-Yamamoto and Fourier Toda-Yamamoto causality tests highlight that the COVID-19 total new cases and total new deaths in the world has predictive power to predict further prices of agricultural commodities. Therefore, in terms of health management, policy makers should give substantial significance to the implementation of COVID-19 related health policies and agricultural policies together during the COVID-19 pandemic period.

Keywords

• Coronavirus
• Public Health
• Commodity Markets
• Granger Causality

References

1. who.int [Internet]. World Health Organization. Listings of WHO’s response to COVID-19. [Cited: 2020 Dec 31]. Available from: https://www. who.int/news/item/29-06-2020-covidtimeline
2. Ji Y, Ma Z, Peppelenbosch MP, Pan Q. Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob Health. 2020;8(4):e480.
3. Mas-Coma S, Jones MK, Marty AM. COVID-19 and globalization. One Health. 2020;9:100132.
4. Qian M, Jiang J. COVID-19 and social distancing. J Public Health (Berl). 2020. doi: 10.1007/s10389-020-01321-z 1-3.
5. oecd.org [Internet]. Organization for Economic Co-operation and Development. The territorial impact of COVID-19: Managing the crisis across levels of government. [Cited: 2020 Dec 30]. Available from: https://read.oecd-ilibrary.org/view/?ref=128_128287-5agkkojaaa&title=The-territorial-impact-of-covid-19-managing-the-crisis-across-levels-of-government
6. oecd.org [Internet]. Organization for Economic Co-operation and Development. Coronavirus (COVID-19): SME policy responses. [Cited: 2021 Jan 01]. Available from: https://read.oecd-ilibrary.org/view/ ?ref=119_119680-di6h3qgi4x&title=Covid-19_SME_Policy_Responses
7. oecd.org [Internet]. Organization for Economic Co-operation and Development. Tax and fiscal policy in response to the coronavirus crisis: Strengthening confidence and resilience. [Cited: 2020 Dec 31]. Available from: https://read.oecd-ilibrary.org/view/ ?ref=128_128575-o6raktc0aa&title=Tax-and-Fiscal-Policy-in-Response-to-the-Coronavirus-Crisis
8. Legido-Quigley H, Mateos-García JT, Campos VR, Gea-Sánchez M, Muntaner C, McKee M. The resilience of the Spanish health system against the COVID-19 pandemic. Lancet Public Health. 2020;5(5):e251-2.

9. Armocida B, Formenti B, Ussai S, Palestra F, Missoni E. The Italian health system and the COVID-19 challenge. Lancet Public Health. 2020;5(5):e253.
10. Blumenthal D, Fowler EJ, Abrams M, Collins SR. COVID-19 implications for the health care system. N Engl J Med. 2020;383(15):1483-8.
11. National Academy of Engineering and Institute of Medicine. Building a better delivery system: a new engineering/health care partnership. Washington, DC: The National Academies Press; 2005.
12. Gao X, Yu J. Public governance mechanism in the prevention and control of the COVID-19: information, decision-making and execution. J Chin Gov. 2020;5(2):178-97.
13. oecd.org [Internet]. Organization for Economic Co-operation and Development. COVID-19 and the food and agriculture sector: issues and policy responses. [Cited: 2020 Dec 31]. Available from: https://read.oecd-ilibrary.org/view/?ref=130_130816-9uut45lj4q&title=Covid-19-and-the-food-and-agriculture-sector-Issues-and-policy-responses
14. Jámbor A, Czine P, Balogh P. The impact of the coronavirus on agriculture: first evidence based on global newspapers. Sustainability. 2020;12(11):4535.
15. worldbank.org [Internet]. The World Bank. Most commodity prices to drop in 2020 as coronavirus depresses demand and disrupts supply. [Cited: 2021 Jan 03]. Available from: https://www.worldbank.org/ en/news/press-release/2020/04/23/most-commodity-prices-to-drop-in-2020-as-coronavirus-depresses-demand-and-disrupts-supply
16. Bakas D, Triantafyllou A. Commodity price volatility and the economic uncertainty of pandemics. Econ Lett. 2020;193:109283.
17. Andersen TM, Hansen NL. Price adjustment in open economies. Open Econ Rev. 1995;6(4):303-21.
18. fao.org [Internet]. Food and Agriculture Organization of the United Nations. Migrant workers and the COVID-19 pandemic. [Cited: 2021 Jan 04]. Available from: http://www.fao.org/policy-support/tools-and-publications/resources-details/en/c/1270461/
19. worldbank.org [Internet]. The World Bank. Food Security and COVID-19. [Cited: 2021 Jan 02]. Available from: https://www.worldbank.org/ en/topic/agriculture/brief/food-security-and-covid-19
20. Rawal V, Verma A. Agricultural supply chains during the COVID-19 lockdown: A study of market arrivals of seven key food commodities in India. SSER Monograph 20/1. New Delhi, India: Society for Social and Economic Research; 2020.
21. Kerr WA. The COVID‐19 pandemic and agriculture: Short-and long-run implications for international trade relations. Can J Agric Econ. 2020;68(2):225-9.
22. Albulescu CT. COVID-19 and the United States financial markets’ volatility. Financ Res Lett. 2021;38:101699.
23. Ashraf BN. Stock markets’ reaction to COVID-19: Cases or fatalities? Res Int Bus Finance. 2020;54:101249.
24. Hassan S. Riveros-Gavilanes JM. First to react is the last to forgive: Evidence from the stock market impact of COVID-19. J Risk Financial Manag. 2021;14(1):26.
25. Onali E. COVID-19 and stock market volatility. SSRN. 2020. doi: 10.2139/ssrn.3571453.
26. Topcu M, Gulal OS. The impact of COVID-19 on emerging stock markets. Financ Res Lett. 2020;36:101691.
27. Xu L. Stock return and the COVID-19 pandemic: evidence from Canada and the US. Financ Res Lett. 2021;38:101872.
28. Ozili PK, Arun T. Spillover of COVID-19: impact on the global economy. SSRN. 2020. doi: 10.2139/ssrn.3562570.
29. Anh DLT, Gan C. The impact of the COVID-19 lockdown on stock market performance: evidence from Vietnam. J Econ Stud. 2020;[Epub ahead of print]. doi: 10.1108/JES-06-2020-0312.
30. Apergis N, Apergis E. The role of COVID-19 for Chinese stock returns: evidence from a GARCHX model. Asia-Pac J Account Econ. 2020;[Epub ahead of print]. doi: 10.1080/16081625.2020.1816185
31. Umar M, Rubbaniy G, Rizvi SKA. COVID-19 and stock market liquidity: An international evidence. SSRN. 2020. doi: 10.2139/ssrn.3758201.
32. Celik I, Yilmaz T, Emir S, Sak AF. The effects of COVID-19 outbreak on financial markets. Financial Studies. 2020;24(4):6-28.
33. Şit A, Telek C. Effects of COVID-19 pandemic on gold ounce price and dollar index. Gaziantep University Journal of Social Sciences. 2020;19(Special Issue):1-13.
34. Unvan YA. Investigation of causality relationships among COVID-19 cases, ISE100 index, dollar, euro, gram gold prices and 2 years bond rates: The case of Turkey. Alphanumeric Journal. 2020;8(1):29-42.
35. Yousef I, Shehadeh E. The impact of the COVID-19 on gold price volatility. Int J Econ Bus Adm. 2020;8(4):353-64.
36. Sarı SS, Kartal T. The relationship of COVID-19 pandemic with Gold Prices, Oil Prices and VIX Index. Erzincan University Journal of Social Sciences Institute. 2020;13(1):93-109.
37. Chowdhury EK, Abedin MZ. COVID-19 Effects on the US stock index returns: An event study approach. SSRN. 2020. doi: 10.2139/ssrn.3611683.
38. Chen C, Liu L, Zhao N. Fear sentiment, uncertainty, and bitcoin price dynamics: The case of COVID-19. Emerg Mark Finance Trade. 2020;56(10):2298-309.
39. Fasanya IO, Oyewole O, Adekoya OB, Odei-Mensah J. Dynamic spillovers and connectedness between COVID-19 pandemic and global foreign exchange markets. Econ Res-Ekon Istraž. 2020;[Epub ahead of print]. doi: 10.1080/1331677X.2020.1860796.
40. Yousef I. Spillover of COVID-19: Impact on stock market volatility. Int J Psychosoc Rehabilitation. 2020;24(6):18069-81.
41. Ibrahim I, Kamaludin K, Sundarasen S. COVID-19, government response, and market volatility: Evidence from the Asia-Pacific developed and developing markets. Economies. 2020;8(4):105.
42. Erdoğan S, Yıldırım DÇ, Gedikli A. Dynamics and determinants of inflation during the COVID-19 pandemic period in European countries: A spatial panel data analysis. Duzce Med J. 2020;22(S1):61-7.
43. Gok R, Kara E. Impacts of the COVID-19 pandemic on the agricultural prices: New insights from CWT Granger causality test. Journal of Research in Economics Politics and Finance. 2020;5(Special Issue):76-96.
44. Elleby C, Domínguez IP, Adenauer M, Genovese G. Impacts of the COVID-19 pandemic on the global agricultural markets. Environ Resource Econ. 2020;76(4):1067-79.
45. Ezeaku HC, Asongu SA, Nnanna, J. Volatility of international commodity prices in times of COVID-19: Effects of oil supply and global demand shocks. Extr Ind Soc. 2021;8(1):257-70.
46. Kotyza P, Czech K, Wielechowski M, Smutka L, Procházka P. Sugar prices vs. financial market uncertainty in the time of crisis: Does COVID-19 induce structural changes in the relationship? Agriculture. 2021;11(2):93.
47. Nazlioglu S, Gormus NA, Soytas U. Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Econ. 2016;60:168-75.
48. Toda HY, Yamamoto T. Statistical inference in vector autoregressions with possibly integrated processes. J Econom. 1995;66(1-2):225-50.
49. Granger CW. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 1969;37(3):424-38.
50. Enders W, Jones P. Grain prices, oil prices, and multiple smooth breaks in a VAR. Stud Nonlinear Dyn E. 2016;20(4):399-419.
51. Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc. 1979;74(366):427-31.
52. Enders W, Lee J. The flexible Fourier form and Dickey–Fuller type unit root tests. Econ Lett. 2012;117(1):196-9.