Examining the effect of the Covid-19 crisis on income inequality: Evidence from developed and developing countries

Document Type : Original Article

Authors

1 Mazandaran University

2 Associate Professor, Department of Economics, Mazandaran University

3 Professor of Economics Department of Mazandaran University

Abstract

The outbreak of the new coronavirus since December 2019 in Wuhan, China, caused many negative effects on countries around the world. This crisis was initially recognized only as a threat to public health; But with its epidemic in most countries of the world, it became a serious crisis for the global economy.The shock caused by the Covid-19 crisis has had various impacts on the economic situation of countries around the world. Income inequality is one of the most important areas affected by the crisis. Hence, the aim of this study is to investigate the effect of the shock caused by this crisis on income inequality for (22) developed countries and (46) developing countries during the years (2020 to 2022 AD) using a seasonal panel vector autoregression (PVAR) model. The results indicate that the Covid-19 crisis has increased income inequality in both developed and developing countries. What can be inferred from the results of the impact reaction functions is that with the onset of the crisis, the effects of the direct tax shock, the degree of trade openness, the strictness index and the rate of covid-19 infection on the Gini coefficient in both groups of countries have led to an increase in income inequality. Also, in developed countries, the effect of the human development index shock on the Gini coefficient is negative, but in developing countries, the effect is positive and has increased income inequality. The results of variance analysis of prediction error for both groups of countries show that the strictness index is the dominant factor in explaining the variance analysis of Gini coefficient prediction error.

Keywords

Main Subjects

References

Abdel-Latif, M. M. (2020). The enigma of health literacy and COVID-19 pandemic. Public health, 185, 95.‏https://doi.org/10.1016/2.puhe.2020.06.030
Almeida, V., Barrios Cobos, S., Christl, M., De Poli, S., Tumino, A., & Van Der Wielen, W. (2020). Households' income and the cushioning effect of fiscal policy measures during the Great Lockdown (No. 06/2020). JRC Working Papers on Taxation and Structural Reforms.‏ https://hdl.handle.net/10419/248819
Arndt, C., Davies, R., Gabriel, S., Harris, L., Makrelov, K., Robinson, S., ... & Anderson, L. (2020). Covid-19 lockdowns, income distribution, and food security: An analysis for South Africa
. Global food security, 26, 100410 https://doi.org/10.1016/j.gfs.2020.100410
Barro, R. J. (2013). Health and economic growth. Annals of economics and finance, 14(2), 329-366.http://down.aefweb.net/AefArticles/aef140202Barro.pdf.‏
Bell, C., & Lewis, M. (2005). Economic implications of epidemics old and new. Available at SSRN 997387.‏ https://dx.doi.org/10.2139/ssrn.997387
Blaskó, Z., & Schnepf, S. V. (2020). Educational inequalities in Europe and physical school closures during Covid-19. Fairness Policy Brief Series, 4, 2020.‏ https://ec.europa.eu/jrc/en/research/crosscutting-activities/fairness
Blaskó, Z., Costa, P. D., & Schnepf, S. V. (2022). Learning losses and educational inequalities in Europe: Mapping the potential consequences of the COVID-19 crisis. Journal of European Social Policy, 32(4), 361-375.‏ https://doi.org/10.1177/09589287221091687
Blundell, R., Costa Dias, M., Cribb, J., Joyce, R., Waters, T., Wernham, T., & Xu, X. (2022). Inequality and the COVID-19 Crisis in the United Kingdom. Annual review of Economics, 14, 607-636.‏ https://doi.org/10.1146/annurev-economics-051520-030252
Blundell, R., Costa Dias, M., Joyce, R., & Xu, X. (2020). COVID‐19 and Inequalities. Fiscal studies, 41(2), 291-319.‏ https://doi.org/10.1111/1475-5890.12232
Bottan, N., Hoffmann, B., & Vera-Cossio, D. (2020). The unequal impact of the coronavirus pandemic: Evidence from seventeen developing countries. PloS one, 15(10), e0239797.‏ https://doi.org/ 10.1371/journal.pone.0239797
Cattan, S., Farquharson, C., Krutikova, S., Phimister, A., Salisbury, A., & Sevilla, A. (2021). Home learning experiences through the COVID-19 pandemic (No. R195). IFS Report.‏ https://doi.org/10.1920/re.ifs.2021.0195
Chetty, R., Friedman, J. N., Hendren, N., Stepner, M., & The Opportunity Insights Team. (2020). How did COVID-19 and stabilization policies affect spending and employment? A new real-time economic tracker based on private sector data (Vol. 91, pp. 1689-1699). Cambridge, MA: National Bureau of Economic Research.‏ http://www.nber.org/papers/w27431
Crossley, T. F., Fisher, P., & Low, H. (2021). The heterogeneous and regressive consequences of COVID-19: Evidence from high quality panel data. Journal of public economics, 193, 104334.‏ https://doi.org/10.1016/j.jpubeco.2020.104334
Dang, H. A. H., & Nguyen, C. V. (2021). Gender inequality during the COVID-19 pandemic: Income, expenditure, savings, and job loss. World Development, 140, 105296.‏ https://doi.org/10.1016/j.worlddev.2020.105296
Dauderstädt, M. (2022). International inequality and the COVID-19 pandemic. Intereconomics, 57(1), 40-46.‏ https://doi.org/10.1007/s10272-022-1026-9
Delaporte, I., & Pena, W. (2020). Working from home under Covid-19: Who is affected? Evidence from Latin American and Caribbean countries. Evidence From Latin American and Caribbean Countries (April 1, 2020). CEPR COVID Economics, 14.‏ https://ssrn.com/abstract=3610885
Fang, H., Ge, C., Huang, H., & Li, H. (2020). Pandemics, global supply chains, and local labor demand: evidence from 100 million posted jobs in China (No. w28072). National Bureau of Economic Research.‏ https://doi.org/10.1371/journal.pone.0239797
Huber, S. G., & Helm, C. (2020). COVID-19 and schooling: evaluation, assessment and accountability in times of crises—reacting quickly to explore key issues for policy, practice and research with the school barometer. Educational assessment, evaluation and accountability, 32(2), 237-270.‏ https://doi.org/10.1007/s11092-020-09322-y
Keogh-Brown, M. R., & Smith, R. D. (2008). The economic impact of SARS: how does the reality match the predictions?. Health policy, 88(1), 110-120.‏ https://doi.org/10.1016/j.healthpol.2008.03.003
Lee, G., & Warner, M. (2007). The political economy of the SARS epidemic: the impact on human resources in East Asia. Routledge.‏ https://doi.org/10.4324/9780203934814
Lu, L., Peng, J., Wu, J., & Lu, Y. (2021). Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: Evidence from Sichuan, China. International Journal of Disaster Risk Reduction, 55, 102085.‏ https://doi.org/10.1016/j.ijdrr.2021.102085
Noy, I., Doan, N., Ferrarini, B., & Park, D. (2020). Measuring the economic risk of COVID‐19. Global Policy, 11(4), 413-423.‏ https://doi.org/10.1111/1758-5899.12851
Preston, S. H. (1975). The changing relation between mortality and level of economic development. Population studies, 29(2), 231-248.‏ https://doi.org/10.1093/ije/dym075
Ray, D., & Subramanian, S. (2022). India’s lockdown: An interim report. In The impact of COVID-19 on India and the global order: A multidisciplinary approach (pp. 11-61). Singapore: Springer Nature Singapore.‏ https://doi.org/10.1007%2Fs41775-020-00094-2
Rezagholizadeh, M., Jafari, H., & Abdolhosseiny, M. (2023). Investigating the economic impact of non-pharmaceutical interventions by governments during the outbreak of the Covid-19 virus: Comparison of developed and developing countries. The Journal of Economic Policy, 15(29), 40-79. (In Persian). https://doi.org/10.22034/epj.2023.20074.2435.
Romer, P. M. (1989). Human capital and growth: Theory and evidence.‏ https://doi.org/10.1016/0167-2231(90)90028-J
Sakhaei, E., Khorsandi, M., Mohammadi, T., & Arbab, H. (2020). Investigating the Effects of Shock Caused by Covid-19 Virus on the Iran's Economy: A GVAR Approach. Journal of Economics and Modelling, 11(2), 125-153. (In Persian). https://dorl.net/dor/20.1001.1.24765775.1399.11.2.5.6
Schult, J., Mahler, N., Fauth, B., & Lindner, M. A. (2022, April). Long-term consequences of repeated school closures during the COVID-19 pandemic for reading and mathematics competencies. In Frontiers in Education (Vol. 13). Frontiers Media SA.‏ https://doi.org/10.3389/feduc.2022.867316
Schultz, T. W. (1964). Changing relevance of agricultural economics. Journal of Farm Econom ics, 46(5), 1004-1014.‏ http://dx.doi.org/10.3991/ijim.v15i08.21565
Wahyuningsih, S., Qohar, A., Satyananda, D., & Atan, N. A. (2021). The Effect of Online Project-Based Learning Application on Mathematics Students' Visual Thinking Continuum in Covid-19 Pandemic. International Journal of Interactive Mobile Technologies, 15(8).‏ http://dx.doi.org/10.3991/ijim.v15i08.21565
Wilkinson, R. G. (1997). Socioeconomic determinants of health: Health inequalities: relative or absolute material standards?. Bmj, 314(7080), 591.‏ https://doi.org/10.1136/bmj.314.7080.591
Zhang, D. (2021). The impact of lockdown policies on labor market outcomes of the Chinese labor force in 2020: Evidence based on an employee tracking survey. China Economic Quarterly International, 1(4), 344-360.‏ https://doi.org/10.1016/j.ceqi.2021.11.002
Public Sector Economics Studies
Volume 3, Issue 2
July 2024
Pages 247-270

Files

History

  • Receive Date: 22 April 2024
  • Revise Date: 27 May 2024
  • Accept Date: 28 May 2024

Share

How to cite

Statistics