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We estimate the causal effect of state-mandated business closures on economic and health outcomes in the context of the COVID-19 crisis in the US. We first show that business closures lead to a substantial drop in sales, earnings, and market values for affected firms. We then exploit sectoral variations in exposure to these restrictions across areas within the same state, and show that locking down 10% of the labor force is associated with a significant contraction in employment, but allows to reduce COVID-19 weekly infection and death rates by respectively 0.023 and 0.0015 percentage points. The findings translate into 24,000 saved lives for a cost of $115 billion. Finally, our empirical analysis suggests that the cost per life saved associated to business closures could have been significantly reduced if restrictions had targeted areas with intense workplace interactions

Abstract Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic and to preventative measures such as lockdown. The automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. Methods We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 20,000 manually-coded free-text reports. Results The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain, stress, but also those mentioning dyspnea, ageusia and anosmia peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. Discussion This example of the COVID-19 crisis shows how the availability of reliable and unbiased surveillance platforms can be useful for a timely and relevant monitoring of all events with public health consequences. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. Conclusion The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis.

We examine the influence of economic policy uncertainty on bank stability post-2007-2008 global financial crisis. We rely on the economic policy uncertainty (EPU) index introduced by Baker et al. (2016). We use 176,477 quarterly observations for US commercial banks over the period from 2011Q1 to 2020Q3 and find consistent and robust evidence that bank stability decreases as the level of economic policy uncertainty increases. We specifically control for demand-side effects which indicates that the decrease in bank stability not only originates from borrowers' and customers' conditions but also from a change in bank behavior. A deeper investigation shows that the negative impact of policy uncertainty on bank stability is stronger for larger banks, and weaker for highly capitalized banks as well as for more liquid banks. Our findings have important implications particularly for the COVID-19 policy implementations.

International audience; This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing misinformation spread across different languages. To assess the performance of CMTA and put it in perspective, we performed a comparative analysis of CMTA with eight monolingual models used for detecting misinformation. The comparison shows that CMTA has surpassed various monolingual models and suggests that it can be used as a general method for detecting misinformation in multilingual micro-texts. CMTA experimental results show misinformation trends about COVID-19 in different languages during the first pandemic months.

This paper shows that prospect theory, extended to account for differences across individuals in their patience and their valuation of the vaccination as a common good can explain why more than 40% of the population has intent to reject the Covid-19 vaccination, as well as the differences in vaccination intentions across population subgroups. Indeed, prospect theory by over-weighting the side effect explains the reject of vaccination. This can be partially compensated by a high patience and/or a large valuation of the collective immunity. The calibrated version of our model, based on an original survey carried out on a representative sample of the adult population living in France allowing us to identify curvatures of their value function, their discount rates and their willingness to cooperate, can predict the evolution of the vaccination intentions between November 2020 an March 2021. We also show that the international differences in the vaccination intentions are closely related to the valuation of the vaccination as a common good.

Textual analysis of 14,270 NBER Working Papers published during 1999–2016 is done to assess the effects of the 2008 crisis on the economics literature. The volume of crisis-related WPs is counter-cyclical, lagging the financial-instability-index. WPs by the Monetary-Economics, Asset-Pricing, and Corporate-Finance program members, hardly refer to “crisis/crises” in the pre-crisis period. As the crisis develops, however, their study-efforts of crisis-related issues increase rapidly. In contrast, WPs in macroeconomics-related programs refer quite extensively in the pre-crisis period to “crisis/crises” and to crises-related topics. Overall, our findings are consistent with the claim that economists were not engaged sufficiently in crises studies before the 2008 crisis. However, counter to the popular image, as soon as the crisis began to unravel, the NBER affiliated economists responded dramatically by switching their focus and efforts to studying and understanding the crisis, its causes and its consequences. Thus, although economists were slow to see the crisis coming, they were fast to act. Preliminary observations on current developments suggest that the economists’ initial response to the ongoing Covid-19 crisis is consistent with these conclusions.

COVID-19 mortality increases markedly with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts can have conflicting implications because BIPOC populations are younger than white populations. In analyses of California and Minnesota—demographically divergent states—we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups. Vaccination schemas directly implicate equitability of access, both domestically and globally. Age-based COVID-19 vaccination prioritizes white people above higher-risk others; geographic prioritization improves equity. Description

An aspect of the Covid-19 pandemic that merits attention is its effects on marriage and childbirth. Although the direct fertility effects of people getting the virus may be minor, the impact of delayed marriages due to the first preventive lockdown, such as that imposed in Pakistan from March 14 to May 8 2020, and the closure of marriage halls that lasted till September 14 may be non-negligible. These demographic consequences are of particular import to developing countries such as Pakistan where birth rates remain high, marriage is nearly universal, and almost all child-bearing takes place within marriage. Based on historic marriage patterns, we estimate that the delay in nuptiality during the first wave of coronavirus outbreak may affect about half of the marriages that were to take place during the year. In Pakistan, childbearing begins soon after marriage, and about 37% of Pakistani married women give birth to their first child within twelve months of marriage. A sizeable number out of these around 400,000 annual births that occur within twelve months of the marriage may consequently be delayed. Postponement of marriages due to the accompanying difficult economic situation and employment precariousness should accentuate this fertility effect. The net fertility impact of the Covid-19 outbreak would ultimately depend not only on the delay in marriages but also on the reproductive behavior of existing couples.; Un aspect de la pandémie de Covid-19 qui mérite une attention particulière concerne ses effets sur le mariage et la naissance des enfants. Les conséquences démographiques sont particulièrement importantes pour les pays en développement tels que le Pakistan. Dans ce pays, le taux de natalité est élevé, le mariage est presque universel et la procréation se fait exclusivement dans le cadre dumariage. Bien que les effets directs du virus sur la fertilité des personnes infectées puissent être moins importants, l'impact des mariages retardés en raison des mesures de confinement tecomme celles qui avaient cours au Pakistan du 14 mars au 8 mai 2020, et de la fermeture des salles de mariage qui a duré jusqu'au 14 septembre peut être sérieux. Sur la base des modèles de mariage historiques, nous estimons que le retard de la nuptialité pendant la première vague de la pandémie de coronavirus pourrait affecter environ la moitié des mariages qui devaient avoir lieu pendant l'année. Au Pakistan, la réproduction commence peu après le mariage et environ 37 % des femmes mariées pakistanaises donnent naissance à leur premier enfant dans les douze mois suivant leur mariage. Un nombre non négligeable des 400 000 naissances annuelles qui surviennent dans les douze mois suivant le mariage pourrait donc être retardé. Le report des mariages en raison d'une situation économique difficile et de la précarité de l'emploi devrait accentuer cet effet sur la fécondité. En fin, l'impact net de l'épidémie de Covid-19 sur la fécondité dépendrait en fin de compte non seulement du report des mariages, mais aussi du comportementdes couples existants en matière de reproduction.