Will Outbreaks Increase or Reduce Income Inequality? the Case of COVID-19

Yıl 2022, Cilt: 51 Sayı: 2, 583 - 605, 30.12.2022

Esme Işık , Ayfer Özyılmaz , Metin Toprak , Yüksel Bayraktar , Figen Büyükakın , Mehmet Fırat Olgun

https://doi.org/10.26650/ibr.2022.52.895431

Öz

The effects of economic contractions experienced during pandemic periods on different income sectors and country groups in terms of income inequality are not homogeneous. Due to the fact that COVID-19 has deeply affected the lives of the poor, immigrants, refugees, the homeless, seasonal workers and people with no health insurance, the relationship between the pandemic and income inequality is of great significance . This study aims to find an answer to the question of whether the recent pandemic increased or decreased income inequality. In the study, the effect of COVID-19 on income inequality in 38 countries with different income levels is analyzed with the Artificial Neural Networks (ANN) and Linear Regression (LR) method. In this context, Gini index values for 2020 were estimated using unemployment, inflation and growth data, which are determinants of income distribution, for the periods 2000-2019. According to the analysis findings, while COVID-19 reduces income inequality in some countries, it increases it in others. However, in general, the results of our study show that the overall effect of COVID-19 on income levels in both developed and developing countries has been to increase income inequality.

Anahtar Kelimeler

COVID-19, Pandemic, Income inequality, Gini, Artificial neural networks, Linear regression

Kaynakça

• Aggarwal, C. C. (2018). Neural Networks and Deep Learning: A textbook. Cham, Switzerland: Springer International Publishing AG.
• Basheer, I. A., & Hajmeer, M. (2020). Artificial neural networks : Fundamentals, computing, design and application. Journal of Microbiological Methods, 43(1), 3-31.
• Bayar, A. A., Gunçavdı Ö., & Levent H. (2020). COVID-19 salgınının Türkiye’de gelir dağılımına etkisi ve mevcut politika seçenekleri [Covid-19 outbreak of the impact of the income distribution in Turkey and the available policy options]. Istanpol Politika Raporu, 7, 1-23.
• Bonacini, L., Gallo, G., & Scicchitano, S. (2020). All that glitters is not gold. Effects of working from home on income inequality at the time of COVID-19. GLO Discussion Paper Series No. 541, 1-32. https://www.econstor.eu/bitstream/10419/216901/1/GLO-DP-0541.pdf.
• Clarke, H., & Whiteley, P. (2020, May 6). Economic inequality can help predict Covid-19 deaths in the US [USApp–American Politics and Policy Blog]. Retrieved from https://blogs.lse.ac.uk/usappblog/2020/05/06/economic-inequality-can-help-predict-covid-19-deaths-in-the-us/.
• Collins, C., Ocampo, O., & Paslaski, S. (2020). Billionaire Bonanza 2020: Wealth, windfalls, tumbling taxes, and pandemic profiteers. Washington, DC: Institute for Policy Studies. https://ips-dc.org/wp-content/uploads/2020/04/Billionaire-Bonanza-2020.pdf.
• Delaporte, I., Escobar, J., & Peña, W. (2020). The Distributional consequences of social distancing on poverty and labour income inequality in Latin America and the Caribbean. GLO Discussion Paper No: 682, 1-42. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3710062.
• Duman, A. (2020). Wage losses and inequality in developing countries: Labor market and distributional consequences of Covid-19 lockdowns in Turkey. Retrieved from http://dx.doi.org/10.2139/ssrn.3645468.
• FAO., & UN (2020). Addressing inequality in times of COVID-19. FAO Policy Brief. Retrieved from http://www.fao.org/documents/card/en/c/ca8843en/.
• FAO (2020). COVID-19 and rural poverty: supporting and protecting the rural poor in times of pandemic. FAO Policy Brief. Retrieved from http://www.fao.org/publications/card/en/c/CA8824EN.
• Fisher, M., & Bubola, E. (2020, March 15). As Coronavirus deepens inequality, inequality worsens its spread. New York Times. Retrieved from https://www.nytimes.com/2020/03/15/world/europe/coronavirus-inequality.html.
• Hecht-Nielsen, R. (1989). Theory of the Backpropagation Neural Network. Academic Press, 593–605. Retrieved from http://www.andrew.cmu.edu/user/nwolfe/esr/pdf/backprop.pdf.
• Komatsu B.K., & Menezes-Filho N. (2020). Simulações de impactos da COVID-19 e da Renda Básica emergencial sobre o Desemprego, Renda, Pobreza e Desigualdade. São Paulo: Policy Paper No.43, 1-31. Retrieved from https://www.insper.edu.br/wp-content/uploads/2020/04/Policy-Paper-v14.pdf.
• Kubat, M. (2017). An Introduction to Machine Learning. Cham: Springer International Publishing.
• Lee, Tsong L. (2004). Back-Propagation neural network for long-term tidal predictions. Ocean Engineering, 31(2), 225–238.
• Lustig, N., Martinez-Pabon, V., Sanz, F., & Younger, S. D. (2020). The impact of COVID-19 lockdowns and expanded social assistance on inequality, poverty and mobility in Argentina, Brazil, Colombia and Mexico. CGD Working Paper No. 556, 1-36. Retrieved from https://www.cgdev.org/sites/default/files/impact-covid-19-lockdowns-and-expanded-social-assistance.pdf.
• Maffioli, E. M. (2020). Consider inequality: Another consequence of the coronavirus epidemic. Journal of Global Health. 10(1), 1-3. doi: 10.7189/jogh.10.010359.
• Martinez-Juarez, L. A., Sedas, A. C., Orcutt, M., & Bhopal, R. (2020). Governments and international institutions should urgently attend to the unjust disparities that COVID-19 is exposing and causing. E-Clinical Medicine. 23, 1-2. doi: 10.1016/j.eclinm.2020.100376.
• Neidhöfer, G. (2020, June 9). Long run consequences of the COVID-19 pandemic on social inequality [UNDP IN LATIN AMERICA AND THE CARIBBEAN]. Retrieved from https://www.latinamerica.undp.org/content/rblac/en/home/blog/2020/consecuencias-de-la-pandemia-del-covid-19-en-las-desigualdades-s.html.
• O'Donoghue, C., Sologon, D. M., Kyzyma, I., & McHale, J. (2020). Modelling the distributional impact of the COVID‐19 crisis. Fiscal Studies, 41(2), 321-336.
• ILO (2020). COVID-19 and the world of work country policy responses. Retrieved from https://www.ilo.org/global/topics/coronavirus/regional-country/country-responses/lang--en/index.htm#DE.
• Perugini, C., & Vladisavljevic, M. (2020). Social stability challenged: pandemics, ınequality and policy responses. IZA Discussion Paper No. 13249. Retrieved from https://www.econstor.eu/bitstream/10419/223691/1/dp13249.pdf.
• Stiglitz, J. (2020). Conquering the great divide: The pandemic has laid bare deep divisions, but it’s not too late to change course. Finance and Development, 57(3), 17-19.
• UNDP (2020). COVID-19 and human development: Assessing the crisis, envisioning the recovery. 2020 Human Development Perspectives. Retrieved from http://hdr.undp.org/sites/default/files/covid-19_and_human_development_0.pdf.
• Van Barneveld, K., Quinlan, M., Kriesler, P., Junor, A., Baum, F., Chowdhury, A., Junankar P., Clibborn S., Flanagan, F., Wright C. F., Friel, S., Halevi, J., & Rainnie, A. (2020). The COVID-19 pandemic: Lessons on building more equal and sustainable societies. The Economic and Labour Relations Review, 31(2), 133-157.
• Warren S. S. (1995). Artificial neural networks and statistical model. Japanese Journal of Applied Statistics, 24(2), 77–88.
• Willis, M. J., Montague, G. A., Di Massimo, C., Tham, M. T., & Morris, A. J. (1992). Artificial neural networks in process estimation and control. Automatica, 28(6), 1181–1187.
• World Bank (2020). Poverty and distributional impacts of COVID-19: Potential channels of impact and mitigating policies. Retrieved from https://www.worldbank.org/en/topic/poverty/brief/poverty-and-distributional-impacts-of-covid-19-potential-channels-of-impact-and-mitigating-policies.