The impact of the COVID-19 pandemic on global GDP growth

Gagnon, Joseph E.; Kamin, Steve; Kearns, John T.

doi:10.1016/j.jjie.2023.101258

Cited by 28 publications

(13 citation statements)

References 41 publications

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“…The COVID-19 pandemic has significantly affected global economic development and may further hinder the process of poverty eradication around the world. 34 , 35 Existing studies discussed the negative impact of the COVID-19 pandemic on the global poverty reduction efforts such as directly reducing the per capita disposable income and consumption expenditure of rural residents, 36 , 37 leading 60 million people back below the poverty line during 2020, 9 and significantly delaying the achievement of the global poverty alleviation target. 38 …”

Section: Methodsmentioning

confidence: 99%

Pandemics erode poverty alleviation process: Impact on productive livelihood and poverty return

Wang,

Shi,

Wang

et al. 2023

iScience

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Section: Methodsmentioning

confidence: 99%

Pandemics erode poverty alleviation process: Impact on productive livelihood and poverty return

Wang,

Shi,

Wang

et al. 2023

iScience

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“…It is documented in Covid-19 studies that investors became more risk averse during Covid-19 and increased uncertainty and risk aversion among investors, causing them to sell off stocks and seek safer assets which caused stock price decline and increased volatility (Mnif, Salhi, Mouakha, & Jarboui, 2022 ; Ortmann et al, 2020 ; Yuan et al, 2022 ). Finally, Covid-19 impacted the global economy as a whole, leading to a decrease in global GDP and an increased risk of a recession (Gagnon et al, 2023 ; Havrlant et al, 2021 ), and impacted financial markets worldwide.…”

Section: Literature Reviewmentioning

confidence: 99%

Exchange Rate and Stock Prices Volatility Connectedness and Spillover during Pandemic Induced-Crises: Evidence from BRICS Countries

Hussain

Bashir

Rehman

2023

Asia-Pac Financ Markets

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This paper investigated exchange rate and stock price volatility connectedness and spillover in Brazil, Russia, India, China, and South Africa (BRICS) during pandemic-induced crises. We first extracted volatility using the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. Then volatility connectedness and spillover were investigated by using (Diebold and Yilmaz, International Journal of Forecasting, 28 (1), 57–66, 2012) method. We find that exchange rate volatility and stock return volatilities are connected during pandemic-induced crises. The study also finds volatilities spillover among countries in the sample. Russia has strong volatility connectedness with India in these financial markets. The direction of volatility spillover is from Russia to India. Similarly, Brazil has strong volatility connectedness with South Africa and the direction volatility spillover is from Brazil to South Africa. Finally, China has a weak volatility connection with the remaining BRICS countries. Thus, the volatility transfer in these financial markets and across BRICS countries has economic implications.

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“…Diseases have always existed in human social life, threatening people's life and property safety, from the early Ebola virus, smallpox virus and SARS virus [1], to the past global public health pandemic COVID-19 [2,3], these major public of health and safety issues are seriously endanger people's physical and mental health, which are gradually hindering the development and progress of the society [4,5]. At present, more and more experts in related professional fields are conducting extensive research on the mechanisms of disease transmission, to find more effective methods to control and prevent the transmission of diseases, thus reducing the harm to society.…”

Section: Introductionmentioning

confidence: 99%

Study on the influence of the information diffusion and vaccination behavior on disease transmission in multiplex networks

Huo,

2023

Phys. Scr.

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To investigate the influence of information diffusion and vaccination behavior on disease transmission. In this paper, a coupling model is proposed to describe the co-evolution process of information diffusion, vaccination behavior and disease transmission in multiplex network. In the information layer, two types of information are diffused, including the positive information and the negative information, respectively. During the process of information diffusion, the influence of mass media on information diffusion is considered, due to the herd effect, which type of information is believed to depend on the information state of the surrounding neighbors based on the Heaviside step function. In the behavioral layer, the individuals with different types of information will choose different immune behavioral responses, which in turn will influence the disease transmission. The coupling model was analyzed by using the Micro Markov Chain Approach (MMCA) to obtain state transition equations and the prevalence thresholds for disease. By simulating the simulation experiments, it demonstrates that the information diffusion has no direct effect on disease prevalence thresholds, but affects the proportion of infected; vaccination behavior has a significant effect on disease prevalence thresholds and also affects the proportion of infected. The results of the study suggest that when the disease transmission is within the controllable range, the stronger the diffusion of positive information, the higher the vaccination rate, which has a more positive influence on inhibiting disease transmission; once out of control, the official will lose trust, which is not conducive to disease control.

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The impact of the COVID-19 pandemic on global GDP growth

Cited by 28 publications

References 41 publications

Pandemics erode poverty alleviation process: Impact on productive livelihood and poverty return

Pandemics erode poverty alleviation process: Impact on productive livelihood and poverty return

Exchange Rate and Stock Prices Volatility Connectedness and Spillover during Pandemic Induced-Crises: Evidence from BRICS Countries

Study on the influence of the information diffusion and vaccination behavior on disease transmission in multiplex networks

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