Investigating the economic impact of non-pharmaceutical interventions by governments during the outbreak of the Covid-19 virus: Comparison of developed and developing countries

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Economics, University of Mazandaran, Babolsar, Iran.

2 PhD student in economics, University of Mazandaran, Babolsar, Iran.

3 MA of Economics, University of Science and Technology, Tehran, Iran.

Abstract

Extended Abstract
Purpose: Late in 2019, the corona virus outbreak caused complex economic issues and substantially impacted the global economy. Governments resorted to non-pharmaceutical interventions, such as social isolation and mandatory quarantines to combat the ever-increasing spread of this virus. These restrictions, which are referred to as a non-vaccine intervention, have been criticized by some economists, and this led to the formation of the topic of the government's actions against the spread of the virus. What effect has it had on the economy and especially macro–variables? In the economic cycle, the imposition of restrictions and quarantine and measures like these have caused a decrease in the supply of labor, a decrease in the activity of enterprises, their production and the gross domestic production. Considering the possibility of the economic costs of these interventions imposed on the economies of countries, the current research attempts to investigate the economic effects of non-pharmacological measures taken during the period of the COVID-19 spread in a number of developed and developing nations.
Methodology: This study aims to examine the impact of non-pharmaceutical government interventions on the gross domestic production (GDP) of developing and developed nations during the period of 2020 to 2022. This is done with seasonal data, and, for each country, the panel generalized moments model (Panel GMM) is utilized. Therefore, the following model is estimated for each group of countries:
    
where Gdp represents  at constant prices in 2015,  represents the degree of trade openness, represents the number of tourists,  represents the government stringency index, and  represents the number of new COVID cases.
Findings and Discussion: The results of the panel GMM estimation indicate that the previous-period GDP had a positive and significant effect on the current-period GDP in both developed and developing countries. The degree of trade openness has a positive and significant effect on the GDP in both developed and developing countries, such that a one-percent increase in the trade openness raises the GDP by 0.026% in developed countries and by 0.634% in developing countries. The results from both categories of the studied countries indicate that the number of tourists entering the country had a positive and statistically significant effect on the GDP. In developed countries, a one-percent increase in the number of incoming tourists results in a 0.107% increase in the GDP, while, in developing countries, it results in a 0.03% increase in the GDP. The government austerity index, which is used to evaluate the economic costs of non-pharmaceutical interventions during the COVID-19 pandemic, has had a negative and significant impact on the GDP of both developed and developing countries. This indicates that government austerity has a negative and significant effect on economic growth. It is that non-medicinal government interventions to control the epidemic have resulted in a decline in the gross domestic production of countries. The findings indicate that a one-percent increase in the government austerity index decreases the GDP by 0.03 percent in established nations and by 0.001 percent in developing ones. This difference in the estimated coefficient indicates that the austerity index had a greater negative impact on the GDP of developed nations. The variable coefficient for the number of newly infected individuals differs in developed and developing nations. In developed nations, this coefficient is negative and statistically significant. The number obtained for this coefficient in this group of countries indicates that, as predicted, a one-percent increase in the number of new cases of COVID-19 has resulted in a 0.001% decrease in the GDP, whereas, in developing countries, a one-percent increase in the number of new cases of the disease has resulted in a 0.009% increase in the GDP.
Conclusion and Policy Implications: Due to the rapid global spread of COVID-19, the government's role in controlling and overcoming this situation has been undeniable and indispensable. Therefore, it is recommended that governments stimulate aggregate demand and increase their expenditure (G) through various monetary and financial channels, such as lowering interest rates, providing packages and support facilities, and reducing taxes. Since effective vaccines were not yet discovered at the beginning of the spread of this virus and, therefore, it was necessary and natural for governments to adopt preventative austerity measures, it is suggested that, in such critical times, governments could be warned to increase information regarding the economic cost and negative effects of non-pharmaceutical measures on the country. The results of this research provide policymakers with the possibility of future epidemics of comparable or even greater magnitude than COVID-19. This index measures the economic costs incurred by the government during these times. It suggests that, by understanding and analyzing such costs in similar circumstances, organizations can modify their strategies or develop support mechanisms to reduce the cost and external effects of such actions.

Keywords

Main Subjects

References

Alexakis, C. Eleftheriou, K. & Patsoulis, P. (2021). "COVID-19 Containment Measures and Stock Market Returns: An International Spatial Econometrics Investigation". Journal of Behavioral and Experimental Finance 29: 100428.‏ https://doi.org/10.1016/j.jbef.2020.100428
Amuedo-Dorantes, C. Borra, C. Rivera Garrido, N. & Sevilla, A. (2020). "Timing is Everything when Fighting a Pandemic: Covid-19 Mortality in Spain". https://dx.doi.org/10.2139/ssrn.3620631
Arellano, M. & Bond, S. (1991). "Some Tests of Specification for panel Data: Monte Carlo Evidence and an Application to Employment Equations". The Review of Economic Studies 58(2): 277-297.‏ https://doi.org/10.2307/2297968
Balaguer, J. and Cantavella-Jorda, M. (2002). "Tourism as a Long-Run Economic Growth Factor: The Spanish Case". Applied Economics 34: 877-884. https://doi.org/10.1080/00036840110058923
Banik, R. Rahman, M. Hossain, M. M. Sikder, M. T. & Gozal, D. (2020). "COVID-19 Pandemic and Rohingya Refugees in Bangladesh: What are the Major Concerns?". Global Public Health 15(10): 1578-1581.‏ https://doi.org/10.1080/17441692.2020.1812103
Barro, R. J. Ursúa, J. F. & Weng, J. (2020). "The Coronavirus and the Great Influenza Pandemic: Lessons from the “Spanish Flu” for the Coronavirus’s Potential Effects on Mortality and Economic Activity". Working Paper (No. w26866). National Bureau of Economic Research https://www.jstor.org/stable/resrep24600
Butcher, G. Fairweather, J. R. & Simmons, D. G. (2003). "The Economic Impact of Tourism on Christchurchcity and Akaroa Township". Journal of Economic Dynamics and Control 11: 211-200. https://hdl.handle.net/10182/252
Carillo, M. & Jappelli, T. (2020). "Pandemics and Local Economic Growth: Evidence from the Great Influenza in Italy.‏Centre for Economic Policy Research". https://ssrn.com/abstract=3628169
Castro-Nuño, M. Molina-Toucedo, J. A. & Pablo-Romero, M. P. (2013). "Tourism and GDP: A Meta-analysis of Panel Data Studies". Journal of Travel Research 52(6): 745-758.‏ https://doi.org/10.1177/0047287513478500
Chen, R. E. Zhang, X. Case, J. B. Winkler, E. S. Liu, Y. VanBlargan, L. A. & Diamond, M. S. (2021). "Resistance of SARS-CoV-2 Variants to Neutralization by Monoclonal and Serum-Derived Polyclonal Antibodies". Nature Medicine 27(4): 717-726. https://doi.org/10.1038/s41591-021-01294-w
Cheng, C. Barceló, J. Hartnett, A. S. Kubinec, R. & Messerschmidt, L. (2020). "COVID-19 Government Response Event Dataset (CoronaNet v. 1.0)". Nature Human Behaviour 4(7): 756-768.‏ https://doi.org/10.1038/s41562-020-0909-7
‏Chernozhukov, V. Kasahara, H. & Schrimpf, P. (2021). "Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the US". Journal of Econometrics 220(1): 23-62.‏ https://doi.org/10.1016/j.jeconom.2020.09.003
Cohen, S. Chakravarthy, S. Bharathi, S. Narayanan, B. & Park, C. Y. (2022). "Potential Economic Impact of COVID-19-Related School Closures". Asian Development Bank Economics Working Paper Series (657). https://dx.doi.org/10.2139/ssrn.4108343
Cross, M. Ng, S. K. & Scuffham, P. (2020). "Trading Health for Wealth: The Effect of COVID-19 Response Stringency". International Journal of Environmental Research and Public Health 17(23): 8725.‏ https://doi.org/10.3390/ijerph17238725
Diamantopoulos, A. & Winklhofer, H. M. (2001). "Index Construction with Formative Indicators: An Alternative to Scale Development". Journal of Marketing Research 38(2): 269-277.‏ https://doi.org/10.1509/jmkr.38.2.269.18845
Dinh Su, T. & Phuc Nguyen, C. (2022). "Foreign Financial Flows, Human Capital and Economic Growth in African Developing Countries". International Journal of Finance & Economics 27(3): 3010-3031.‏ https://doi.org/10.1002/ijfe.2310
Edejer, T. T. T. Hanssen, O. Mirelman, A. Verboom, P. Lolong, G. Watson, O. J. & Soucat, A. (2020). "Projected Health-care Resource Needs for an Effective Response to COVID-19 in 73 Low-income and Middle-income Countries: a Modelling Study". The Lancet Global Health 8(11): e1372-e1379.‏ https://doi.org/10.1016/S2214-109X(20)30383-1
Gros, D. Ounnas, A. & Yeung, T. Y. C. (2021). "A New COVID Policy Stringency Index for Europe". Covid Economics 115.‏ https://cepr.org/node/390711
Hale, T. Angrist, N. Kira, B. Petherick, A. Phillips, T. & Webster, S. (2020). "Variation in Government Responses to COVID-19".‏ BSG Working Paper Series
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). (2020). Workshop Report on Biodiversity and Pandemics of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES). IPBES Secretariat.‏
International Monetary Fund (2020). World Economic Outlook, October 2020: A Long and Difficult Ascent, Washington DC.
Jorda, Ò. Singh, S. R. & Taylor, A. M. (2022). "Longer-run Economic Consequences of Pandemics". Review of Economics and Statistics 104(1): 166-175.‏ https://doi.org/10.1162/rest_a_01042
Karaivanov, A. Lu, S. E. Shigeoka, H. Chen, C. & Pamplona, S. (2021). "Face Masks, Public Policies and Slowing the Spread of COVID-19: Evidence from Canada". Journal of Health Economics 78: 102475.‏ https://doi.org/10.1016/j.jhealeco.2021.102475
Kazerooni, A. Salahesh, T. & Asgharpur, H. (2018). "Banks’ Role in Monetary Policy Transmission Mechanism (Emphasis on Balance-Sheet and Financial Health Characteristics of Banks)". Journal of Economic Research (Tahghighat- E- Eghtesadi) 53(1): 69-92. https://doi.org/10.22059/jte.2017.232209.1007571. (In Persian)
Koh, D. & Goh, H. P. (2020). "Occupational Health Responses to COVID-19: What Lessons Can We Learn from SARS?". Journal Of Occupational Health 62(1): e12128.‏ https://doi.org/10.1002/1348-9585.12128
Konig, M. & Winkler, A. (2020). "COVID-19 and Economic Growth: Does Good Government Performance Pay Off?". Intereconomics 55(4): 224-231.‏ https://doi.org/10.1007/s10272-020-0906-0
Konig, M. & Winkler, A. (2020). "Monitoring in Real Time: Cross-Country Evidence on the COVID-19 Impact on GDP Growth in the First Half of 2020". Covid Economics 57: 132-153.‏ https://cepr.org/node/390668
Konig, M. & Winkler, A. (2021). "COVID-19: Lockdowns, Fatality Rates and GDP Growth". Intereconomics 56(1): 32-39.‏ https://doi.org/10.1007/s10272-021-0948-y
Konig, M. & Winkler, A. (2021). "The Impact of Government Responses to the COVID-19 Pandemic on GDP Growth: Does Strategy Matter?". PloS One 16(11): e0259362.‏ https://doi.org/10.1007/s10272-021-0948-y
Lim, A. B. Sazuki, F. Weerasena, B. & Ferlito, C. (2021). "The Economic Impact of School Closures in Malaysia". Policy Brief (1).‏ https://dx.doi.org/10.22617/WPS220197-2
Lopez Bóo, F. (2010). "Returns to Education and Macroeconomic Shocks: Evidence from Argentina". Available at SSRN 1552687. https://dx.doi.org/10.2139/ssrn.1552687
Marozzi, M. (2016). "Construction, Robustness Assessment and Application of an Index of Perceived Level of Socio-economic Threat from Immigrants: A Study of 47 European Countries and Regions". Social Indicators Research 128(1): 413-437.‏ https://doi.org/10.1007/s11205-015-1037-z
Matysiak, K. & Perzyński, T. (2019). "The Use of Selected Water Resources of Radom Regions for Tourism and Recreation". AUTOBUSY–Technika, Eksploatacja, Systemy Transportowe 24(6): 332-336.‏ https://doi.org/10.24136/atest.2019.172
McKibbin, W. & Fernando, R. (2021). "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios". Asian Economic Papers 20(2): 1-30. https://doi.org/10.1162/asep_a_00796
Nelson, M. A. (2021). "COVID-19 Closure and Containment Policies: A First Look at the Labour Market Effects in Emerging Nations". Covid Economics 66: 89-114.‏ https://cepr.org/node/390710
Nicola, M. Alsafi, Z. Sohrabi, C. Kerwan, A. Al-Jabir, A. Iosifidis, C. & Agha, R. (2020). "The Socio-economic Implications of the Coronavirus Pandemic (COVID-19): A Review". International Journal of Surgery 78: 185-193.‏ https://doi.org/10.1016/j.ijsu.2020.04.018
Pesaran, H. M. (2004). "General Diagnostic Tests for Cross Section Dependence in Panels". (Vol. 435). Working Paper. https://dx.doi.org/10.2139/ssrn.572504
Pesaran, M. H. (2007). "A Simple Panel Unit Root Test in the Presence of Cross‐Section Dependence". Journal of Applied Econometrics 22(2): 265-312.‏ https://doi.org/10.1002/jae.951
Pesaran, M. H. Schuermann, T. & Weiner, S. M. (2004). "Modeling Regional Interdependencies using a Global Error-correcting Macroeconometric Model". Journal of Business & Economic Statistics 22(2): 129-162. https://doi.org/10.1198/073500104000000019
Raghutla, C. (2020). "The Effect of Trade Openness on Economic Growth: Some Empirical Evidence from Emerging Market Economies". Journal of Public Affairs 20(3): e2081.‏ https://doi.org/10.1002/pa.2081
Sequeira, T. N. & Maçãs Nunes, P. (2008). "Does Tourism Influence Economic Growth? A Dynamic Panel Data Approach". Applied Economics 40(18): 2431-2441.‏ https://doi.org/10.1080/00036840600949520
Shoss, M. (2021). "Occupational Health Psychology Research and the COVID-19 Pandemic". Journal of Occupational Health Psychology 26(4): 259.‏ https://doi.org/10.1037/ocp0000292
Soomro, A. N. Kumar, J. & Kumari, J. (2022). "The Dynamic Relationship between FDI, ICT, Trade Openness, and Economic Growth: Evidence from BRICS Countries". The Journal of Asian Finance, Economics and Business 9(2): 295-303.‏ https://doi:10.13106/jafeb.2022.vol9.no2.0295
Su, T. D. & Nguyen, C. P. (2022). "Productive Contribution of Public Spending and Human Capital in Developing Countries Revisited: The Role of Trade Openness". Foreign Trade Review 57(1): 66-84. https://doi.org/10.1177/00157325211045471
Sun, C. & Zhai, Z. (2020). "The Efficacy of Social Distance and Ventilation Effectiveness in Preventing COVID-19 Transmission". Sustainable Cities and Society 62, 102390.‏ https://doi.org/10.1016/j.scs.2020.102390
Takian, A. Kiani, M. M. & Khanjankhani, K. (2020). "COVID-19 and the Need to Prioritize Health Equity and Social Determinants of Health". International Journal of Public Health 65(5): 521-523.‏ https://doi.org/10.1007/s00038-020-01398-z
UNESCO (2021). Adverse Consequences of School Closures, https://en.unesco.org/covid19/ educationresponse/consequences.
Yamin, M. (2020). "Counting the Cost of COVID-19". International Journal of Information Technology 12(2): 311-317.‏ https://doi.org/10.1007/s41870-020-00466-0
Yan, B. Zhang, X. Wu, L. Zhu, H. & Chen, B. (2020). "Why do Countries Respond Differently to COVID-19? A Comparative Study of Sweden, China, France, and Japan". The American Review of Public Administration 50(6-7): 762-769.‏ https://doi.org/10.1177/0275074020942445
The Journal of Economic Policy
Volume 15, Issue 29
September 2023
Pages 40-79

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