The Impact of COVID-19 on Economic Growth of Countries: What Role Has Income Inequality in It?
Abstract
:1. Introduction
2. Literature Review
3. Conceptual Framework
4. Methodology
- Quality of institutions: Institutions can be important channels through which health conditions can affect economic growth (see, for example, Acemoglu et al. 2003).
- Education: Education is a crucial factor that significantly interacts with health. Health conditions can be explained by the level of education and the interaction between them contributes to the development level of countries (see, for example, Buor 2003; Bloom 2007; Vogl 2012) and explains the economics growth of countries (see Zhang et al. 2003).
- Age composition: Both health outcomes (see, for example, Mehta et al. 2019) and health expenditure growth (see, for example, de Meijer et al. 2013) correlate with age. This correlation explains the impact that age can have on the GDP growth of various economies (see, for example, Kelley and Schmidt 2005; Lee and Mason 2017);
- Access to insurance: Having access to affordable insurance affects health outcomes (see, for example, Currie and Gruber 1996; Reinhold and Jürges 2012). Therefore, any potential impact of health issues on economic growth should take into consideration the extent that health insurance is accessible (see, for example, Levine and Rothman 2006).
- Sector: The COVID-19 pandemic has affected various economic sectors with different degrees of severity. The service sector (except for information technology-based services) has probably been the most affected by this pandemic. For example, the United Nation World Tourism Organization (UNWTO 2020) reported a 22% fall in international tourism receipts of $80 billion in 2020, corresponding to a loss of 67 million international arrivals. Therefore, the GDP of economies with a high reliance on such sectors would be more affected by this pandemic.
5. Results and Discussion
6. Conclusions, Policy Implications, Limitation of Study and Further Research
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Dependent Variable: Real GDP Growth | ||
---|---|---|
Equation (1) | Equation (2) | |
Institutions | 0.347 (0.279) | 0.408 ** (0.245) |
Sector | −0.262 (0.203) | −0.386 (0.183) |
Number of Deaths | −1.039 * (0.693) | |
Number of Infected | −1.184 * (1.004) | |
GINI | −0.235 * (0.154) | −0.285 * (0.182) |
Age | −0.199 (0.293) | −0.132 (0.260) |
Education | −0.307 (0.284) | −0.382 (0.285) |
Insurance | 0.178 (0.260) | 0.205 (0.227) |
Stringency index | −0.388 (0.126) | −0.385 *** (0.119) |
GINI*number of death | 1.135 ** (0.644) | |
GINI*number of infected | 1.602 ** (0.898) | |
Sample size (N) | 109 | 112 |
R2 | 0.49 | 0.54 |
Dependent Variable: Death Cases (eq3) and Number of Cases (eq4) | ||
---|---|---|
Equation (3) | Equation (4) | |
Institutions | −0.294 ** (0.157) | −0.184 (0.149) |
GINI | 0.334 *** (0.107) | 0.222 *** (0.108) |
Age | 0.253 * (0.143) | 0.293 *** (0.141) |
Education | 0.276 ** (0.131) | −0.023 (0.104) |
Insurance | −0.161 (0.102) | −0.162 (0.103) |
Stringency index | 0.112 (0.099) | 0.137 (0.123) |
Service | 0.265 *** (0.093) | 0.289 *** (0.095) |
N (sample size) | 111 | 114 |
R2 | 0.25 | 0.21 |
Variables | Observations | Mean | Standard Deviation |
---|---|---|---|
Real GDP | 140 | −4.75 | 5.19 |
Institutions | 139 | 0 | 0.97 |
Sector | 138 | 55.50 | 11.76 |
Number of Deaths | 137 | 13,093.56 | 39,525.82 |
Number of Infected | 137 | 13,093.56 | 39,525.82 |
GINI | 140 | 37.85 | 7.91 |
Age | 138 | 9.83 | 6.94 |
Education | 124 | 85.33 | 29.65 |
Insurance | 136 | 32.83 | 17.94 |
Stringency index | 133 | 46.09 | 9.51 |
Variables | Real GDP | Institutions | Sector | Number of Death | Number of infected | GINI | Age | Education | Insurance | Stringency Index |
---|---|---|---|---|---|---|---|---|---|---|
Real GDP | 1 | |||||||||
Institutions | −0.22 | 1 | ||||||||
Sector | −0.36 | 0.69 | 1 | |||||||
Number of Deaths | −0.12 | 0.08 | 0.26 | 1 | ||||||
Number of Infected | −0.08 | 0.10 | 0.23 | 0.95 | 1 | |||||
GINI | −0.06 | −0.14 | 0.14 | 0.09 | 1 | |||||
Age | −0.25 | 0.70 | 0.62 | 0.15 | 0.14 | −0.51 | 1 | |||
Education | −0.31 | 0.75 | 0.62 | 0.14 | 0.11 | −0.38 | 0.76 | 1 | ||
Insurance | 0.22 | −0.59 | −0.46 | −0.12 | −0.12 | 0.08 | −0.46 | −0.47 | 1 | |
Stringency index | −0.38 | 0.14 | 0.29 | 0.25 | 0.23 | 0.09 | 0.13 | 0.25 | −0.03 | 1 |
Albania | Algeria | Angola | Argentina | Armenia | Australia | Austria | Bangladesh | Belarus | Belgium |
---|---|---|---|---|---|---|---|---|---|
Benin | Bhutan | Bolivia | Botswana | Brazil | Bulgaria | Burkina Faso | Burundi | Cabo Verde | Cameroon |
Canada | Chad | Chile | China | Colombia | Comoros | Congo, Dem. Rep. | Congo, Rep. | Costa Rica | Cote d’Ivoire |
Croatia | Cyprus | Czech Republic | Denmark | Djibouti | Dominican Republic | Ecuador | Egypt, Arab Rep. | El Salvador | Estonia |
Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia, The | Georgia | Germany | Ghana |
Greece | Guatemala | Guinea | Haiti | Honduras | Hungary | Iceland | India | Indonesia | Iran, Islamic Rep. |
Iraq | Ireland | Israel | Italy | Japan | Kazakhstan | Kenya | Korea, Rep. | Kosovo | Kyrgyz Republic |
Latvia | Lesotho | Liberia | Lithuania | Luxembourg | Madagascar | Malawi | Malaysia | Maldives | Malta |
Mauritania | Mauritius | Mexico | Moldova | Mongolia | Montenegro | Morocco | Mozambique | Myanmar | Namibia |
Netherlands | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Pakistan | Panama | Paraguay | Peru |
Philippines | Poland | Portugal | Romania | Russian Federation | Rwanda | Sao Tome and Principe | Senegal | Serbia | Seychelles |
Sierra Leone | Slovak Republic | Slovenia | Somalia | South Africa | South Sudan | Spain | Sri Lanka | St. Lucia | Sudan |
Sweden | Switzerland | Tajikistan | Tanzania | Thailand | Timor-Leste | Togo | Tunisia | Turkey | Uganda |
Ukraine | United Arab Emirates | United Kingdom | United States | Uruguay | Vietnam | West Bank and Gaza | Yemen, Rep. | Zambia | Zimbabwe |
1 | Note that the meaning of rich/poor countries and high/low-income countries is used interchangeably. The study uses, in this context, the definition of the World Bank. Therefore, low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1045 or less in 2020; lower middle-income economies are those with a GNI per capita between $1046 and $4095; upper middle-income economies are those with a GNI per capita between $4096 and $12,695; high-income economies are those with a GNI per capita of $12,696 or more (see https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (accessed on 18 June 2022)). |
2 | The sample of this research includes 35 high-income countries and 105 low- and middle-income countries (categorized according to the definition of the World Bank). See Appendix A Table A5. |
3 | More definitions, sources, and descriptive statistics of the variables are found in Appendix A Table A3 and Table A4. |
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Ghecham, M.A. The Impact of COVID-19 on Economic Growth of Countries: What Role Has Income Inequality in It? Economies 2022, 10, 158. https://doi.org/10.3390/economies10070158
Ghecham MA. The Impact of COVID-19 on Economic Growth of Countries: What Role Has Income Inequality in It? Economies. 2022; 10(7):158. https://doi.org/10.3390/economies10070158
Chicago/Turabian StyleGhecham, Mahieddine Adnan. 2022. "The Impact of COVID-19 on Economic Growth of Countries: What Role Has Income Inequality in It?" Economies 10, no. 7: 158. https://doi.org/10.3390/economies10070158
APA StyleGhecham, M. A. (2022). The Impact of COVID-19 on Economic Growth of Countries: What Role Has Income Inequality in It? Economies, 10(7), 158. https://doi.org/10.3390/economies10070158