News and uncertainty about COVID-19: Survey evidence and short-run economic impact.
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ABSTRACT:
SUBMITTER: Dietrich AM
PROVIDER: S-EPMC8827064 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In response to the COVID-19 emergency, many countries have introduced a series of social-distancing measures including lockdowns and businesses' shutdowns, in an attempt to curb the spread of the infection. Accordingly, the pandemic has been generating unprecedented disruption on practically every aspect of society. This paper demonstrates that high-frequency electricity market data can be used to estimate the causal, short-run impacts of COVID-19 on the economy, providing information that is essential for shaping future lockdown policy. Unlike official statistics, which are published with a delay of a few months, our approach permits almost real-time monitoring of the economic impact of the containment policies and the financial stimuli introduced to address the crisis. We illustrate our methodology using daily data for the Italian day-ahead power market. We estimate that the 3 weeks of most severe lockdown reduced the corresponding Italian Gross Domestic Product (GDP) by roughly 30%. Such negative impacts are now progressively declining but, at the end of June 2020, GDP is still about 8.5% lower than it would have been without the outbreak.
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Project description:National differences in uncertainty, inequality, and trust have been accentuated by COVID-19. There are indications that the pandemic has impacted societies characterized by high uncertainty, inequality, and low trust harder than societies characterized by low uncertainty, equality, and high trust. This study investigates differential response strategies to COVID-19 as reflected in news media of two otherwise similar low uncertainty societies: Denmark and Sweden. The comparison is made using a recent approach to information dynamics in unstructured data. The main findings are that the news dynamics generally mirror public-health policies, capture fundamental socio-cultural variables related to uncertainty and trust, and may provide a measure of societal uncertainty. The findings can provide insights into evolutionary trajectories of decision-making under high uncertainty and, from a methodological level, be used to develop a media-based index of uncertainty and trust.
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Project description:Mental health disorders represent an enormous cost to society, are related to economic outcomes, and have increased markedly since the COVID-19 outbreak. Economic activity contracted dramatically on a global scale in 2020, representing the worst crisis since the Great Depression. This study used the COVID Impact Survey to provide insights on the interactions of mental illness and economic uncertainty during COVID-19. We used a probability-based panel survey, COVID Impact Survey, conducted in the U.S. over three waves in the period April-June 2020. The survey covered individual information on employment, economic and financial uncertainty, mental and physical health, as well as other demographic information. The prevalence of moderate mental distress was measured using a Psychological Distress Scale, a 5-item scale that is scored on a 4-point scale (total range: 0-15). The mental distress effect of employment, economic, and financial uncertainty, was assessed in a logit regression analysis conditioning for demographic and health information. It is found that employment, health coverage, social security, and food provision uncertainty are additional stressors for mental health. These economic factors work in addition to demographic effects, where groups who display increased risk for psychological distress include: women, Hispanics, and those in poor physical health. A decrease in employment and increases in economic uncertainty are associated with a doubling of common mental disorders. The population-representative survey evidence presented strongly suggests that economic policies which support employment (e.g., job keeping, job search support, stimulus spending) provide not only economic security but also constitute a major health intervention. Moving forward, the economic uncertainty effect ought to be reflected in community level intervention and prevention efforts, which should include strengthening economic support to reduce financial and economic strain.
| S-EPMC8638947 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:This study investigates the impact of economic uncertainty due to the coronavirus (COVID-19) pandemic on the industrial economy in the US in terms of the interdependence and causality relationship. We apply wavelet coherence analysis to economic policy uncertainty (EPU) data and monthly sector volatility of the S&P 500 index from January 2008 to May 2020. The results reveal that EPU in terms of COVID-19 has influenced the sector volatility more than the global financial crisis (GFC) for all sectors. Furthermore, EPU leads the volatility of all sectors during COVID-19 pandemic, while some sector's volatilities lead EPU during the GFC.
| S-EPMC7524523 | biostudies-literature
Project description:Kenneth Gillingham is an Associate Professor of Economics at Yale University, with a primary appointment in the School of Forestry & Environmental Studies. In 2015 to 2016, he served as the Senior Economist for Energy and the Environment at the White House Council of Economic Advisers. His research interests cover energy and environmental economics, industrial organization, technological change, and energy modeling. He held a Fulbright to New Zealand and has worked for Resources for the Future and Pacific Northwest National Laboratory. He received a PhD and two MS degrees from Stanford University and an AB from Dartmouth College. Christopher Knittel is the George P. Shultz Professor of Applied Economics in the Sloan School of Management at the Massachusetts Institute of Technology (MIT). He is also the Director of MIT's Center for Energy and Environmental Policy Research, which serves as the hub for social science research on energy and the environmental since the late 1970s. Professor Knittel is also the Co-Director of the MIT Energy Initiative's Electric Power System Low Carbon Energy Center and a co-director of The E2e Project, a research initiative between MIT, UC Berkeley, and the University of Chicago to undertake rigorous evaluation of energy efficiency investments. Jing Li holds the inaugural William Barton Rogers Career Development Chair of Energy Economics at the MIT Sloan School of Management. From 2017-2018, Jing Li was a Postdoctoral Associate of the MIT Energy Initiative. Jing's research interests lie in energy economics and industrial organization, focusing on development and adoption of new technologies. Her most recent work examines compatibility and investment in electric vehicle recharging networks in the United States and cost pass-through in the E85 retail market. Jing received double BSc degrees in Mathematics and Economics from MIT in 2011 and her PhD in Economics from Harvard in 2017. Marten Ovaere is a Postdoctoral Associate in the School of Forestry & Environmental Studies of Yale University. His research interests lie in energy and environmental economics, with a focus on electricity markets, carbon pricing, and renewable energy. Marten holds a MSc in Economics, a MSc in Energy Engineering, and a PhD in Economics from KU Leuven. Mar Reguant is an Associate Professor in Economics at Northwestern University. She received her PhD from MIT in 2011. Her research uses high-frequency data to study the impact of auction design and environmental regulation on electricity markets and energy-intensive industries. She has numerous awards, including a Sloan Research Fellowship in 2016, the Sabadell Prize for Economic Research in 2017, a Presidential Early Career Award for Scientists and Engineers award in 2019, and the European Association of Environmental and Resource Economists Award for Researchers in Environmental Economics under the Age of Forty in 2019.
| S-EPMC7305502 | biostudies-literature
Project description:The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
| S-EPMC7523140 | biostudies-literature
Project description:BackgroundSocial media is widely used as a source of news and information regarding COVID-19. However, the abundance of misinformation on social media platforms has raised concerns regarding the spreading infodemic. Accordingly, many have questioned the utility and impact of social media news use on users' engagement with (mis)information.ObjectiveThis study offers a conceptual framework for how social media news use influences COVID-19 misinformation engagement. More specifically, we examined how news consumption on social media leads to COVID-19 misinformation sharing by inducing belief in such misinformation. We further explored if the effects of social media news use on COVID-19 misinformation engagement depend on individual differences in cognition and personality traits.MethodsWe used data from an online survey panel administered by a survey agency (Qualtrics) in Singapore. The survey was conducted in March 2022, and 500 respondents answered the survey. All participants were older than 21 years and provided consent before taking part in the study. We used linear regression, mediation, and moderated mediation analyses to explore the proposed relationships between social media news use, cognitive ability, personality traits, and COVID-19 misinformation belief and sharing intentions.ResultsThe results suggested that those who frequently used social media for news consumption were more likely to believe COVID-19 misinformation and share it on social media. Further probing the mechanism suggested that social media news use translated into sharing intent via the perceived accuracy of misinformation. Simply put, social media news users shared COVID-19 misinformation because they believed it to be accurate. We also found that those with high levels of extraversion than those with low levels were more likely to perceive the misinformation to be accurate and share it. Those with high levels of neuroticism and openness than those with low levels were also likely to perceive the misinformation to be accurate. Finally, it was observed that personality traits did not significantly influence misinformation sharing at higher levels of cognitive ability, but low cognitive users largely drove misinformation sharing across personality traits.ConclusionsThe reliance on social media platforms for news consumption during the COVID-19 pandemic has amplified, with dire consequences for misinformation sharing. This study shows that increased social media news consumption is associated with believing and sharing COVID-19 misinformation, with low cognitive users being the most vulnerable. We offer recommendations to newsmakers, social media moderators, and policymakers toward efforts in limiting COVID-19 misinformation propagation and safeguarding citizens.
| S-EPMC9533200 | biostudies-literature
Project description:The personal experience of events such as financial crises and natural disasters can alter economic preferences. We administered a repeated cross-sectional preference survey during the early stages of the COVID-19 outbreak, collecting three bi-weekly samples from participants recruited through Amazon Mechanical Turk. The survey elicits economic preferences, self-reported fear of the pandemic, and beliefs about economic and health consequences. Preferences varied over time and across regions, and self-reported fear of the pandemic explains this variation. These findings suggest caution about the generalizability of some types of experimental work during times of heightened fear. Supplementary Information The online version contains supplementary material available at 10.1007/s40881-021-00111-x.
| S-EPMC8642764 | biostudies-literature