Pandemics, Income Inequality, and Refugees: The Case of COVID-19.

Refugees are more vulnerable to COVID-19 due to factors such as low standard of living, accommodation in crowded households, difficulty in receiving health care due to high treatment costs in some countries, and inability to access public health and social services. The increasing income inequalities, anxiety about providing minimum living conditions, and fear of being unemployed compel refugees to continue their jobs, and this affects the number of cases and case-related deaths. The aim of the study is to analyze the impact of refugees and income inequality on COVID-19 cases and deaths in 95 countries for the year 2021 using Poisson regression, Negative Binomial Regression, and Machine Learning methods. According to the estimation results, refugees and income inequalities increase both COVID-19 cases and deaths. On the other hand, the impact of income inequality on COVID-19 cases and deaths is stronger than on refugees.

Effects of public expenditures on environmental pollution: evidence from G-7 countries.

In this study, the effect of public expenditures and, their sub-components on environmental pollution is discussed in G-7 countries. Two different periods were used in the study. These are the period 1997-2020 for general public expenditure, and the period 2008-2020 for public expenditure sub-components. For cointegration, Westerlund cointegration test was used, and according to the analysis result there is a cointegration relationship between general government expenditure and environmental pollution. Panel Fourier Toda-Yamamoto causality test was used to determine the causality relationship between public expenditures and environmental pollution and the result indicates that there is bidirectional causality between public expenditures and CO2 on a panel basis. For models estimation, System the Generalized Method of Moments (GMM) method was used. The findings of the study indicate that general public expenditures decrease environmental pollution. Considering at the results of the sub-components of public expenditures, housing and community amenities, social protection, health expenditure, economic affairs, recreation, culture & religion expenditures have a negative effect on environmental pollution. Other control variables generally  have a statistically significant effect on environmental pollution. Energy consumption and population density increase environmental pollution but environmental policy stringency index, renewable energy and GDP per capita reduce environmental pollution.

Convergence of per capita ecological footprint among BRICS-T countries: evidence from Fourier unit root test.

In recent years there has been a great deal of research into environmental pollution using a variety of techniques in response to growing environmental concerns. Convergence analysis, one of these techniques, helps determine whether the developing countries will catch up with the rich countries in pollution using unit root tests. However, the vast majority of the research in the field has generally used conventional unit root tests. Since many economic series contain structural breaks, using unit root tests that account for structural breaks is essential for accurate prediction. More specifically, if the series has a fractional process, conventional unit root tests may erroneously conclude that the departure from linearity is permanent. Moreover, the existing literature mainly uses gas emissions, such as carbon dioxide, which represent pollution weakly. Therefore, we use per capita ecological footprint (EF hereafter) as a more comprehensive pollution indicator of environmental degradation. In this direction, the study aims to determine whether BRICS-T countries' EF converges to the average of the BRICS-T for the 1992-2017 period. Besides the ADF unit root test, we employed the Fourier ADF unit root test, which considers the structural breaks, and the Fractional Frequency Fourier ADF unit root test, which accounts for structural breaks by considering fractional values. Our results showed that EF converges in Russia and Turkey according to the conventional ADF test, in China and Russia according to the Fourier ADF test, and in Brazil and China according to the Fractional Fourier Frequency test.

The Relationship between Health Expenditures and Economic Growth in EU Countries: Empirical Evidence Using Panel Fourier Toda-Yamamoto Causality Test and Regression Models.

The aim of this study is to investigate the effect of health expenditures on economic growth in the period 2000-2019 in 27 European Union (EU) countries. First, the causality relationship between the variables was analyzed using the panel Fourier Toda-Yamamoto Causality test. The findings demonstrate a bidirectional causality relationship between health expenditures and economic growth on a panel basis. Secondly, the effects of health expenditures on economic growth were examined using the Random Forest Method for the panel and then for each country. According to the Random Forest Method, health expenditures positively affected economic growth, but on the country basis, the effect was different. Then, government health expenditures, private health expenditures, and out-of-pocket expenditures were used, and these three variables were ranked in order of importance in terms of their effects on growth using the Random Forest Method. Accordingly, government health expenditures were the most important variable for economic growth. Finally, Support Vector Regression, Gaussian Process Regression, and Decision Tree Regression models were designed for the simulation of the data used in this study, and the performances of the designed models were analyzed.

Socio-Economic, Demographic and Health Determinants of the COVID-19 Outbreak.

OBJECTIVE: In this study, the effects of social and health indicators affecting the number of cases and deaths of the COVID-19 pandemic were examined. For the determinants of the number of cases and deaths, four models consisting of social and health indicators were created. METHODS: In this quantitative research, 93 countries in the model were used to obtain determinants of the confirmed cases and determinants of the COVID-19 fatalities. RESULTS: The results obtained from Model I, in which the number of cases was examined with social indicators, showed that the number of tourists, the population between the ages of 15 and 64, and institutionalization had a positive effect on the number of cases. The results obtained from the health indicators of the number of cases show that cigarette consumption affects the number of cases positively in the 50th quantile, the death rate under the age of five affects the number of cases negatively in all quantiles, and vaccination positively affects the number of cases in 25th and 75th quantile values. Findings from social indicators of the number of COVID-19 deaths show that life expectancy negatively affects the number of deaths in the 25th and 50th quantiles. The population over the age of 65 and CO2 positively affect the number of deaths at the 25th, 50th, and 75th quantiles. There is a non-linear relationship between the number of cases and the number of deaths at the 50th and 75th quantile values. An increase in the number of cases increases the number of deaths to the turning point; after the turning point, an increase in the number of cases decreases the death rate. Herd immunity has an important role in obtaining this finding. As a health indicator, it was seen that the number of cases positively affected the number of deaths in the 50th and 75th quantile values and the vaccination rate in the 25th and 75th quantile values. Diabetes affects the number of deaths positively in the 75th quantile. CONCLUSION: The population aged 15-64 has a strong impact on COVID-19 cases, but in COVID-19 deaths, life expectancy is a strong variable. On the other hand, it has been found that vaccination and the number of cases interaction term has an effect on the mortality rate. The number of cases has a non-linear effect on the number of deaths.

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases.

In the fight against Covid-19, developed countries and developing countries diverge in success. This drew attention to the discussion of how different health systems and different levels of health spending are effective in combating Covid-19. In this study, the role of the health system in the fight against Covid-19 is discussed. In this context, the number of hospital beds, the number of doctors, life expectancy at 60, universal health service and the share of health expenditures in GDP were used as health indicators. In the study, firstly 2020 data was estimated by using the Artificial Neural Networks simulation method and this year was used in the analysis. The model, with the data of 124 countries, was estimated using the cross-sectional OLS regression method. The estimation results show that the number of hospital beds, number of doctors and life expectancy at the age of 60 have statistically significant and positive effects on the ratio of Covid-19 recovered/cases. Universal health service and share of health expenditures in GDP are not significant statistically on the cases and recovered. Hospital bed capacity is the most effective variable on the recovered/case ratio.