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COVID-19 outbreaks at nursing homes during the recent pandemic, which received ample media coverage, may have lasting negative impacts on individuals’ perceptions regarding ursing homes. We argue that this could have sizable and persistent implications for savings and long-term care policies. We first develop a theoretical model predicting that higher nurs- ing home aversion should induce higher savings and stronger support for policies subsidizing home care. We further document, based on a survey on Canadians in their 50s and 60s, that higher nursing home aversion is widespread: 72% of respondents are less inclined to enter a nursing home because of the pandemic. Consistent with our model, we find that the latter are much more likely to have higher intended savings for older age because of the pandemic. We also find that they are more likely to strongly support home care subsidies.

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.

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.

The objective of this paper is to assess the mitigating role of remittances during the adverse COVID-19 employment shock on Nigeriai¯s food insecurity. Based on pre-COVID-19 and postCOVID-19 surveys, we use a difference-in-difference approach while controlling for the time and household fixed effects. Results indicate that remittances are mitigating the negative consequences of COVID-19 employment shocks, especially in the short run. We find that 100% of the deterioration in food insecurity, owing to the shock, is offset by the remittances received. While the adverse effects of the shock persist over time, the mitigation effect of remittances appears to be effective only at the early stages of the pandemic, however. Furthermore, the mitigation effect of remittances seems heterogeneous regarding the origin of remittances, residence area, and poverty status. The mitigation effect of remittances is higher for remittances from abroad than for Domestic ones. We also find a higher mitigating effect of remittances in the rural area and for non-poor households. Finally, our results shed light on the capital channel as a crucial mechanism explaining the mitigation effect of remittances. Notably, findings suggest that formal financial inclusion, capital ownership like livestock or rental earnings, amplifies the attenuating effect of remittances.

We develop a matching model that predicts the impact of the COVID-19 lockdown on US unemployment, while accounting for the contrasted impacts across various job types. The model is calibrated on the subprime experience and is then used to identify the job-specific lockdown shocks, using observed worker flows by diploma. The model persistence-which is significantly larger than in the Diamond-Mortensen-Pissarides model-is dampened by CARES act that facilitates the use of temporary separations. Counterfactual experiments show that time-varying risk, hiring cost externalities, and wage rigidity are needed to account for these crises.

We show that the acceleration index, a novel indicator that measures acceleration and deceleration of viral spread (Baunez et al. 2020a,b), is essentially a test-controlled version of the reproduction number. As such it is a more accurate indicator to track the dynamics of an infectious disease outbreak in real time. We indicate a discrepancy between the acceleration index and the reproduction number, based on the infectivity and test rates and we provide a formal decomposition of this difference. When applied to French data for the ongoing COVID-19 pandemic, our decomposition shows that the reproduction number consistently underestimates the resurgence of the pandemic since the summer of 2020, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all the relevant information and captures in real time the sizeable time variation featured by viral circulation, it is a sufficient statistic to track the pandemic’s propagation.

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 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

We investigate, for the first time, the empirical drivers of the COVID-19 cross-country mortality rates at a macroeconomic level. The intensity of the pandemic (number of infected people), the demographic structure (proportion of people age 65 or above) and the openness degree (number of tourists arrivals) seem to be significant predictors in addition to health infrastructures (number of hospital beds, physicians). We also find that the subprime crisis and the austerity policies conducted in certain countries, by reducing the public health expenditures in the last ten years and altering the adaptation capacity of the health system, have probably intensified the tragic consequences of the COVID-19 pandemic. Pollution seems to have only played a marginal role as well as control strategies (travel restrictions, testing policy). We do not find consistent effects against the COVID-19 virus due to past exposal to other types of epidemics like Malaria or Tuberculosis.