We assess the Covid-19 pandemic’s implications for state government sales and income tax revenues. We estimate that the economic declines implied by recent forecasts from the Congressional Budget Office will lead to a shortfall of roughly $106 billion in states’ sales and income tax revenues for the 2021 fiscal year. This is equivalent to 0.5 percent of GDP and 11.5 percent of our pre-Covid sales and income tax projection. Additional tax shortfalls from the second quarter of 2020 may amount to roughly $42 billion. We discuss how these revenue declines fit into several pieces of the broader economic context. These include other revenues (e.g., university tuition and fees) that are also at risk, as well as assets (e.g., pension plan holdings) that are at risk. Further dimensions of context include support enacted through several pieces of federal legislation, as well as spending needs necessitated by the public health crisis itself.

As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.

Unlike most countries, Korea did not implement a lockdown in its battle against COVID-19, instead successfully relying on testing and contact tracing. Only one region, Daegu-Gyeongbuk (DG), had a significant number of infections, traced to a religious sect. This allows us to estimate the causal effect of the outbreak on the labor market using difference-in-differences. We find that a one per thousand increase in infections causes a 2 to 3 percent drop in local employment. Non-causal estimates of this coefficient from the US and UK, which implemented large-scale lockdowns, range from 5 to 6 percent, suggesting that at most half of the job losses in the US and UK can be attributed to lockdowns. We also find that employment losses caused by local outbreaks in the absence of lockdowns are (i) mainly due to reduced hiring by small establishments, (ii) concentrated in the accommodation/food, education, real estate, and transportation industries, and (iii) worst for the economically vulnerable workers who are less educated, young, in low-wage occupations, and on temporary contracts, even controlling for industry effects. All these patterns are similar to what we observe in the US and UK: The unequal effects of COVID-19 are the same with or without lockdowns. Our finding suggests that the lifting of lockdowns in the US and UK may lead to only modest recoveries in employment unless COVID-19 infection rates fall.

Social distancing restrictions and demand shifts from COVID-19 are expected to shutter many small businesses, but there is very little early evidence on impacts. This paper provides the first analysis of impacts of the pandemic on the number of active small businesses in the United States using nationally representative data from the April 2020 CPS – the first month fully capturing early effects from the pandemic. The number of active business owners in the United States plummeted by 3.3 million or 22 percent over the crucial two-month window from February to April 2020. The drop in business owners was the largest on record, and losses were felt across nearly all industries and even for incorporated businesses. African-American businesses were hit especially hard experiencing a 41 percent drop. Latinx business owners fell by 32 percent, and Asian business owners dropped by 26 percent. Simulations indicate that industry compositions partly placed these groups at a higher risk of losses. Immigrant business owners experienced substantial losses of 36 percent. Female-owned businesses were also disproportionately hit by 25 percent. These findings of early-stage losses to small businesses have important policy implications and may portend longer-term ramifications for job losses and economic inequality.

Since social distancing is the primary strategy for slowing the spread of many diseases, understanding why U.S. counties respond differently to COVID-19 is critical for designing effective public policies. Using daily data from about 45 million mobile phones to measure social distancing we examine how counties responded to both local COVID-19 cases and statewide shelter-in-place orders. We find that social distancing increases more in response to cases and official orders in counties where individuals historically (1) engaged less in community activities and (2) demonstrated greater willingness to incur individual costs to contribute to social objectives. Our work highlights the importance of these two features of social capital—community engagement and individual commitment to societal institutions—in formulating public health policies.

We quantify the macroeconomic effects of COVID-19 for emerging markets using a SIR-multisector-small open economy model and calibrating it to Turkey. Domestic infection rates feed into both sectoral supply and sectoral demand shocks. Sectoral demand shocks also incorporate lower external demand due to foreign infection rates. Infection rates change endogenously with different lockdown policies. To calibrate the model, we use indicators of physical proximity and tele-workability of jobs to measure supply shocks. We use real-time credit card purchases to pin down demand shocks. Our results show that the optimal policy, which yields the lowest economic cost and saves the maximum number of lives, can be achieved under a full lockdown of 39 days. Partial and/or no lockdowns have higher economic costs as it takes longer to control the disease and hence to normalize the demand. Economic costs are much larger for an open economy because of the amplification role of international input-output linkages. Lower capital flows exacerbate this amplification as capital flows are the key form of financing for the production network. We document that sectors with stronger international input-output linkages and higher external debt suffer worse COVID losses and as a result have larger fiscal needs.

The COVID-19 pandemic continues to have profound personal, public health and economic consequences worldwide. Since its onset, health care providers have worked tirelessly to treat adults and children facing this complex condition, and are consequently at increased risk for acute and long-term mental health conditions. Starting in late March, 2020, the Department of Psychiatry, University of Colorado School of Medicine, has launched COVID-19 related wellness programs for health care workers and staff across disciplines and medical settings. The services are designed to support colleagues in health care, promote their physical, emotional and relational well-being, and reduce their risk for adverse mental health issues. With funding from department, institutional, health system, philanthropic, state and federal sources, our interprofessional faculty and staff offer a range of wellness programs

Researchers share how they have adapted fieldwork and collaborations in the face of travel bans and closed borders.

Clinical prediction models to aid diagnosis, assess disease severity or prognosis have enormous potential to aid clinical decision making during the covid‐19 pandemic. A living systematic review has, so far, identified 145 covid‐19 prediction models published (or preprinted) between 03‐January‐2020 and 05‐May‐2020. Despite the considerable interest in developing covid‐19 prediction models, the review concluded that all models to date, with no exception, are at high risk of bias with concerns related to data quality, flaws in the statistical analysis and poor reporting, and none are recommended for use. Disappointingly, the recent study by Yang and colleagues describing the development of a prediction model to identify covid‐19 patients with severe disease, is no different. The study has failed to report important information needed to judge the study findings, but has numerous methodological concerns in design and analysis that deserve highlighting.

Global lockdowns to halt the spread of the coronavirus will have a negligible impact on rising temperatures due to climate change, researchers have found. Lockdowns to stop the spread of the coronavirus caused huge falls in transport use, as well as reductions in industry and commercial operations, cutting the greenhouse gases and pollutants caused by vehicles and other activities. The impact is only short-lived, however, and analysis shows that even if some lockdown measures last until the end of 2021, global temperatures will only be 0.01°C lower than expected by 2030. Advertisement googletag.cmd.push(function() { googletag.display('mpu-mid-article'); }); But if countries choose a strong green stimulus route out of the pandemic, it could halve the temperature rises expected by 2050, says a team led by Piers Forster at the University of Leeds, UK. That gives the world a good chance of keeping temperature rises to the 1.5°C goal that countries signed up to under the international Paris climate agreement to prevent the most dangerous impacts of global warming.

Deanna Montgomery realizes she doesn’t need to be at a laboratory bench to use her scientific experience — or to make a difference.

The concepts of disease elimination and eradication mostly relate to immunisation programme outcomes. Disease eradication is the global reduction of infection to zero cases, whereas disease elimination is the absence of sustained endemic community transmission in a country or other geographical region.6WHOFramework for verifying elimination of measles and rubella.Wkly Epidemiol Rec. 2013; 88: 89-99PubMed Google Scholar With ongoing global SARS-CoV-2 transmission, reduction to zero cases in a defined region is only possible with stringent travel restrictions. For COVID-19, modelling estimates suggested that sustained restrictions that reduced travel by 90% to and from Wuhan, China, early in the spread of SARS-CoV-2, only modestly affected the epidemic trajectory to other regions of China.7Chinazzi M Davis JT Ajelli M et al.The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak.Science. 2020; 368: 395-400PubMed Google Scholar However, in Australia, travel bans were highly effective in controlling the spread of SARS-CoV-2 into Australia and averted a much larger epidemic.

We know little about the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in African countries, including its infectiousness and the proportion of infected people who develop symptoms. Confined exposure of 2010 people on an aircraft carrier resulted in an infection rate of just 50%, and only 50% of infected people developed symptoms.2Lagneau L Covid-19: la contamination du porte-avions Charles de Gaulle garde ses mystères…pour le moment.http://www.opex360.com/2020/04/19/covid-19-la-contamination-du-porte-avions-charles-de-gaulle-garde-ses-mysteres-pour-le-moment/Date accessed: July 4, 2020Google Scholar Under less confined conditions, and similar to other circulating viruses that cause acute respiratory infections, SARS-CoV-2 might cause infection rates well below 30%, thus unable to provoke herd immunity but most probably causing recurring annual infections.Estimated infection fatality rates of around 0·3%3Streeck H Schulte B Kuemmerer B et al.Infection fatality rate of SARS-CoV-2 infection in a German community with a super-spreading event.medRxiv. 2020; (published online June 2.) (preprint).https://doi.org/10.1101/2020.05.04.20090076Google Scholar draw a much less dramatic picture of COVID-19-related deaths than predicted by Wells and colleagues, who presumed 95% of all Congolese will be infected, with an infection fatality rate of over 4%.1Wells CR Stearns JK Lutumba P Galvani AP COVID-19 on the African continent.Lancet Infect Dis. 2020; (published online May 6.)https://doi.org/10.1016/S1473-3099(20)30374-1Summary Full Text Full Text PDF Scopus (0) Google Scholar In DR Congo, we might thus estimate fewer than 40 000 attributable deaths compared with 800 000 Congolese people dying each year in the country.4countryeconomy.comGoogle ScholarDemocratic Republic of the Congo.https://countryeconomy.com/countries/democratic-republic-congoDate accessed: July 4, 2020Google Scholar Such estimates put the prioritisation of this disease over other health threats on the continent immediately into question.

We frequently find ourselves giving the same advice to different students on how to write rebuttals. So we thought we’d write it up. Our experience is with AI conferences (e.g., CVPR, ECCV, ICCV, NeurIPS, ICLR, EMNLP).The core guiding principle is that the rebuttal should be thorough, direct, and easy for the Reviewers and Area Chair (RACs) to follow.

Social media poses a threat to public health by facilitating the spread of misinformation. At the same time, however, social media offers a promising avenue to stem the distribution of false claims – as evidenced by real-time corrections, crowdsourced fact-checking, and algorithmic tagging. Despite the growing attempts to correct misinformation on social media, there is still considerable ambiguity regarding the ability to effectively ameliorate the negative impact of false messages. To address this gap, the current study uses a meta-analysis to evaluate the relative impact of social media interventions designed to correct health-related misinformation (k = 24; N = 6,086). Additionally, the meta-analysis introduces theory-driven moderators that help delineate the effectiveness of social media interventions. The mean effect size of attempts to correct misinformation on social media was positive and significant (d = 0.40, 95% CI [0.25, 0.55], p =.0005) and a publication bias could not be excluded. Interventions were more effective in cases where participants were involved with the health topic, as well as when misinformation was distributed by news organizations (vs. peers) and debunked by experts (vs. non-experts). The findings of this meta-analysis can be used not only to depict the current state of the literature but also to prescribe specific recommendations to better address the proliferation of health misinformation on social media.

The UK faces a second wave of coronavirus infections this winter if the country’s testing and contact tracing system doesn’t improve by the time schools fully reopen and people return to workplaces, researchers have warned.

Extensive empirical evidence suggests that there is a maximal number of people with whom an individual can maintain stable social relationships (the Dunbar number). We argue that this arises as a consequence of a natural phase transition in the dynamic self-organization among N individuals within a social system. We present the calculated size dependence of the scaling properties of complex social network models to argue that this collective behavior is an enhanced form of collective intelligence. Direct calculation establishes that the complexity of social networks as measured by their scaling behavior is nonmonotonic, peaking around 150, thereby providing a theoretical basis for the value of the Dunbar number. Thus, we establish a theory-based bridge spanning the gap between sociology and psychology.

The COVID-19 pandemic has reshaped the demand for goods and services worldwide. The combination of a public health emergency, economic distress, and disinformation-driven panic have pushed customers and vendors towards the shadow economy. In particular Dark Web Marketplaces (DWMs), commercial websites easily accessible via free software, have gained significant popularity. Here, we analyse 472,372 listings extracted from 23 DWMs between January 1, 2020 and July 7, 2020. We identify 518 listings directly related to COVID-19 products and monitor the temporal evolution of product categories including Personal Protective Equipment (PPE), medicines (e.g., hydroxyclorochine), and medical frauds(e.g., vaccines). Finally, we compare trends in their temporal evolution with variations in public attention, as measured by Twitter posts and Wikipedia page visits. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs, especially when real vaccines or cures become available and are potentially in short supply. We anticipate our analysis will be of interest both to researchers and public agencies focused on the protection of public health.

Modern large engineered network systems normally work in cooperation and incorporate dependencies between their components for purposes of efficiency and regulation. Such dependencies may become a major risk since they can cause small-scale failures to propagate throughout the system. Thus, the dependent nodes could be a natural target for malicious attacks that aim to exploit these vulnerabilities. Here we consider a type of targeted attack that is based on the dependencies between the networks. We study strategies of attacks that range from dependency-first to dependency-last, where a fraction 1−p<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>1</mn><mo>−</mo><mi>p</mi></mrow></math> of the nodes with dependency links, or nodes without dependency links, respectively, are initially attacked. We systematically analyze, both analytically and numerically, the percolation transition of partially interdependent networks, where a fraction q<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>q</mi></math> of the nodes in each network are dependent on nodes in the other network. We find that for a broad range of dependency strength q<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>q</mi></math>, the “dependency-first” attack strategy is actually less effective, in terms of lower critical percolation threshold pc<math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>p</mi><mi>c</mi></msub></math>, compared with random attacks of the same size. In contrast, the “dependency-last” attack strategy is more effective, i.e., higher pc<math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>p</mi><mi>c</mi></msub></math>, compared with a random attack. This effect is explained by exploring the dynamics of the cascading failures initiated by dependency-based attacks. We show that while “dependency-first” strategy increases the short-term impact of the initial attack, in the long term the cascade slows down compared with the case of random attacks and vice versa for “dependency-last.” Our results demonstrate that the effectiveness of attack strategies over a system of interdependent networks should be evaluated not only by the immediate impact but mainly by the accumulated damage during the process of cascading failures. This highlights the importance of understanding the dynamics of avalanches that may occur due to different scenarios of failures in order to design resilient critical infrastructures.

Those who sense that this grand experiment in working from home comes with plenty of downsides — longer days, more meetings and more emails to answer — are now backed up by data

There have been several attempts to predict mortality from COVID-19 in the UK, including calculation of age-based case fatality rates1Verity R Okell LC Dorigatti I et al.Estimates of the severity of coronavirus disease 2019: a model-based analysis.Lancet Infect Dis. 2020; 20: 669-677Summary Full Text Full Text PDF PubMed Scopus (191) Google Scholar and relative risk (RR) of mortality.2Banerjee A Pasea L Harris S et al.Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.Lancet. 2020; 395: 1715-1725Summary Full Text Full Text PDF PubMed Scopus (9) Google Scholar David Spiegelhalter said that “roughly speaking, we might say that getting COVID-19 is like packing a year's worth of risk into a week or two”.3Spiegelhalter D How much “normal” risk does COVID represent?.https://medium.com/wintoncentre/how-much-normal-risk-does-covid-represent-4539118e1196Date: March 21, 2020Date accessed: June 29, 2020Google ScholarIn response to these predictions, we decided to calculate the excess mortality in the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) cohort. The RCGP RSC cohort has been recruited to be nationally representative,4de Lusignan S Dorward J Correa A et al.Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study.Lancet Infect Dis. 2020; (published online May 15.)https://doi.org/10.1016/S1473-3099(20)30371-6Summary Full Text Full Text PDF PubMed Google Scholar and the mortality data for the cohort align well with those from the Office of National Statistics (ONS; appendix p 1).

As the initial phase of the COVID-19 pandemic passes its peak in many countries, serological studies are becoming increasingly important in guiding public health responses. Antibody testing is crucial for monitoring the evolution of the pandemic, providing a more complete picture of the total number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than molecular diagnostic testing alone.1Cheng MP Yansouni CP Basta NE et al.Serodiagnostics for severe acute respiratory syndrome-related coronavirus-2.Ann Intern Med. 2020; (published online June 4.)https://doi.org/10.7326/M20-2854Crossref Scopus (44) Google Scholar All individuals with SARS-CoV-2-specific antibodies have been exposed to the virus, so antibody testing can highlight differences in past exposure between regions, demographic groups, and occupations.2Metcalf CJE Farrar J Cutts FT et al.Use of serological surveys to generate key insights into the changing global landscape of infectious disease.Lancet. 2016; 388: 728-730Summary Full Text Full Text PDF PubMed Google Scholar Seroprevalence estimates can also be used to estimate the infection fatality rate.3Erikstrup C Hother CE Pedersen OBV et al.Estimation of SARS-CoV-2 infection fatality rate by real-time antibody screening of blood donors.medRxiv. 2020; (published online April 28.) (preprint).https://doi.org/10.1101/2020.04.24.20075291Google Scholar Dashboards that visualise COVID-19 cases confirmed by diagnostic testing have been pivotal in enabling policy makers and researchers to monitor the pandemic.4Dong E Du H Gardner L An interactive web-based dashboard to track COVID-19 in real time.Lancet Infect Dis. 2020; 20: 533-534Summary Full Text Full Text PDF PubMed Scopus (387) Google Scholar Yet, despite the value of antibody testing, there is no unified resource for seroprevalence estimates.To address this need, we created SeroTracker, a custom-built dashboard that systematically monitors and synthesises findings from hundreds of global SARS-CoV-2 serological studies. The dashboard allows users to visualise seroprevalence estimates on a world map and compare estimates between regions, population groups, and testing modalities (eg, assay type or antibody isotype).

Our understanding of how stress affects primary school children’s attention and learning has developed rapidly. We know that children experience differing levels of stressors (factors that cause stress) at home, and that this can influence how they respond to new stressors when they occur in educational contexts. Here, we review evidence showing that stress can increase children’s attention and learning capacities in some circumstance but hinder them in others. We show how children differ in their attention and learning styles, dependent on stress levels: for example, more highly stressed children may be more distracted by superficial features and may find it harder to engage in planning and voluntary control. We review intervention research on stress management techniques in children, concentrating on psychological techniques (such as mindfulness and stress reappraisal), physiological techniques (such as breathing exercises) and environmental factors (such as noise). At the current time, teachers’ awareness of the differing stress response of their pupils may be the most effective factor in helping them accommodate the needs of the children in their classrooms.

osfr provides a (hopefully) convenient R interface to OSF (Open Science Framework, https://www.osf.io), a free service for managing research developed by the Center for Open Science (COS). osfr completed its rOpenSci peer-review earlier this year and has been available on CRAN since February. Throughout its development and since its release I’ve had numerous conversations with members of the R community about OSF (and osfr), and through these interactions a couple recurring patterns emerged. First, it seems that many R users have heard of OSF but relatively few have first-hand experience with it. Second, I’m often asked how OSF compares to GitHub and whether it would even be useful for someone who already uses GitHub to manage their research. In a future post I’ll highlight some features of osfr and demonstrate how it can help form the basis of efficient and inclusive research workflows. However, before you can extract any value from osfr, you need to be an OSF user first. And so, I wanted to take this opportunity to provide a little background about OSF, what it offers, how it differs from something like GitHub, and where it might fit into your workflow as an R/GitHub user. Before diving in, I want to acknowledge that OSF is a multi-faceted product, and includes a number of services under its umbrella, including things like pre-print servers and a research registration repository, which, while incredibly cool and noteworthy, fall outside the scope of this post, which focuses on project management.

Research on health misinformation has grown rapidly as concerns about the potential harmful effects of health misinformation on individuals and society intensify amid a “post-truth” era. In this chapter, we provide a broad overview of current research and evidence concerning the many facets of health misinformation, including its sources, prevalence, characteristics (both content and diffusion features), impact, and mitigation. We conclude that health misinformation originates from many sources, most notably mass and social media, is fairly prevalent, both in interpersonal and mediated settings, and tends to feature negative sentiments, anecdotal evidence, and anti-science narratives. While there is no conclusive evidence that health misinformation spreads more broadly than scientific information, health misinformation reliably leads to misperceptions on health issues. Efforts to mitigate the impact of health misinformation show early promise in correcting misperceptions. We offer several directions for future research, including a call for more investigations on the impact of health misinformation and correcting messages on actual behaviors.

We provide an interim report on the Indian lockdown provoked by the covid-19 pandemic. The main topics — ranging from the philosophy of lockdown to the provision of relief measures — transcend the Indian case. A recurrent theme is the enormous visibility of covid-19 deaths worldwide, with Governments everywhere propelled to respect this visibility, developing countries perhaps even more so. In advanced economies, the cost of achieving this reduction in visible deaths is “merely” a dramatic reduction in overall economic activity, coupled with far-reaching measures to compensate those who bear such losses. But for India, a developing country with great sectoral and occupational vulnerabilities, this dramatic reduction is more than economic: it means lives lost. These lost lives, through violence, starvation, indebtedness and extreme stress (both psychological and physiological) are invisible. It is this conjunction of visibility and invisibility that drives the Indian response. The lockdown meets all international standards so far; the relief package none.

We show that unexpected changes in the trajectory of COVID-19 infections predict US stock returns, in real time. Parameter estimates indicate that an unanticipated doubling (halving) of projected infections forecasts next-day decreases (increases) in aggregate US market value of 4 to 11 percent, indicating that equity markets may begin to rebound even as infections continue to rise, if the trajectory of the disease becomes less severe than initially anticipated. Using the same variation in unanticipated projected cases, we find that COVID-19-related losses in market value at the firm level rise with capital intensity and leverage, and are deeper in industries more conducive to disease transmission. These relationships provide important insight into current record job losses. Measuring US states' drops in market value as the employment weighted average declines of the industries they produce, we find that states with milder drops in market value exhibit larger initial jobless claims per worker. This initially counter-intuitive result suggests that investors value the relative ease with which labor versus capital costs can be shed as revenues decline.

In the early stages of the COVID-19 pandemic, international testing efforts tended to target individuals whose symptoms and/or jobs placed them at a high presumed risk of infection. Testing regimes of this sort potentially result in a high proportion of cases going undetected. Quantifying this parameter, which we refer to as the undetected rate, is an important contribution to the analysis of the early spread of the SARS-CoV-2 virus. We show that partial identification techniques can credibly deal with the data problems that common COVID-19 testing programs induce (i.e. excluding quarantined individuals from testing and low participation in random screening programs). We use public data from two Icelandic testing regimes during the first month of the outbreak and estimate an identified interval for the undetected rate. Our main approach estimates that the undetected rate was between 89% and 93% before the medical system broadened its eligibility criteria and between 80% and 90% after.

Sparked by the killing of George Floyd in police custody, the 2020 Black Lives Matter protests have brought a new wave of attention to the issue of inequality within criminal justice. However, many public health officials have warned that mass protests could lead to a reduction in social distancing behavior, spurring a resurgence of COVID-19. This study uses newly collected data on protests in 315 of the largest U.S. cities to estimate the impacts of mass protests on social distancing, COVID-19 case growth, and COVID-19-related deaths. Event-study analyses provide strong evidence that net stay-at-home behavior increased following protest onset, consistent with the hypothesis that non-protesters’ behavior was substantially affected by urban protests. This effect was not fully explained by the imposition of city curfews. Estimated effects were generally larger for persistent protests and those accompanied by media reports of violence. Furthermore, we find no evidence that urban protests reignited COVID-19 case or death growth after more than five weeks following the onset of protests. We conclude that predictions of population-level spikes in COVID-19 cases from Black Lives Matter protests were too narrowly conceived because of failure to account for non-participants’ behavioral responses to large gatherings.

We analyze the effectiveness of preventive investments aimed at increasing agents' life expectancy, with a focus on influenza and COVID-19 mitigation. Maximizing overall life expectancy requires allocating resources across hazards so as to equalize investments' marginal effectiveness. Based on estimates for the marginal effectiveness of influenza vaccines, we determine the level of COVID-19 mitigation investments that would imply such equalization. Given current projections for COVID-19 mitigation costs, our results suggest that wide-spread influenza vaccination would be an effective life-preserving investment.

What would a hypothetical one million US deaths in the Covid-19 epidemic mean for mortality of individuals at the population level? To put estimates of Covid-19 mortality into perspective, we estimate age-specific mortality for an epidemic claiming for illustrative purposes one million US lives, with results scalable over a broad range of deaths. We calculate the impact on period life expectancy (down 3 years) and remaining life-years (12.3 years per death), which for one million deaths can be valued at six to 10 trillion dollars. The age-patterns of Covid-19 mortality observed in other countries are remarkably similar and exhibit the typical rate of increase by age of normal mortality. The scenario of one million Covid-19 deaths is similar in scale to the decades-long HIV/AIDS and opioid-overdose epidemics but considerably smaller than the Spanish Flu of 1918. Unlike HIV/AIDS and opioid epidemics, the Covid-19 deaths will be concentrated in months rather than spread out over decades.

Both the White House and state governors have explicitly linked thresholds of reduced COVID-19 case growth to the lifting of statewide shelter-in-place orders (SIPOs). This “hardwired” policy endogeneity creates empirical challenges in credibly isolating the causal effect of lifting a statewide SIPO on COVID-19-related health. To break this simultaneity problem, the current study exploits a unique natural experiment generated by a Wisconsin Supreme Court decision. On May 13, 2020, the Wisconsin Supreme Court abolished the state’s “Safer at Home” order, ruling that the Wisconsin Department of Health Services unconstitutionally usurped legislative authority to review COVID-19 regulations. We capitalize on this sudden, dramatic, and largely unanticipated termination of a statewide SIPO to estimate its effect on social distancing and COVID-19 case growth. Using a synthetic control design, we find no evidence that the repeal of the state SIPO impacted social distancing, COVID-19 cases, or COVID-19-related mortality during the fortnight following enactment. Estimated effects were economically small and nowhere near statistically different from zero. We conclude that the impact of shelter-in-place orders is likely not symmetric across enactment and lifting of the orders.

Covid-19 is the single largest threat to global public health since the Spanish Influenza pandemic of 1918-20. Was the world better prepared in 2020 than it was in 1918? After a century of public health and basic science research, pandemic response and mortality outcomes should be better than in 1918-20. We ask whether mortality from historical pandemics has any predictive content for mortality in the ongoing Covid-19 pandemic. We find a strong persistence in public health performance in the early days of the Covid-19 pandemic. Places that performed poorly in terms of mortality in 1918 were more likely to have higher mortality today. This is true across countries and across a sample of US cities. Experience with SARS is associated with lower mortality today. Distrust of expert advice, lack of cooperation at many levels, over-confidence, and health care supply shortages have likely promoted higher mortality today as in the past.

This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed.

Hydroxychloroquine, used to treat malaria and some autoimmune disorders, potently inhibits viral infection of SARS coronavirus (SARS-CoV-1) and SARS-CoV-2 in cell-culture studies. However, human clinical trials of hydroxychloroquine failed to establish its usefulness as treatment for COVID-19. This compound is known to interfere with endosomal acidification necessary to the proteolytic activity of cathepsins. Following receptor binding and endocytosis, cathepsin L can cleave the SARS-CoV-1 and SARS-CoV-2 spike (S) proteins, thereby activating membrane fusion for cell entry. The plasma membrane-associated protease TMPRSS2 can similarly cleave these S proteins and activate viral entry at the cell surface. Here we show that the SARS-CoV-2 entry process is more dependent than that of SARS-CoV-1 on TMPRSS2 expression. This difference can be reversed when the furin-cleavage site of the SARS-CoV-2 S protein is ablated. We also show that hydroxychloroquine efficiently blocks viral entry mediated by cathepsin L, but not by TMPRSS2, and that a combination of hydroxychloroquine and a clinically-tested TMPRSS2 inhibitor prevents SARS-CoV-2 infection more potently than either drug alone. These studies identify functional differences between SARS-CoV-1 and -2 entry processes, and provide a mechanistic explanation for the limited in vivo utility of hydroxychloroquine as a treatment for COVID-19.

The COVID-19 pandemic has led to accelerated efforts to develop therapeutics and vaccines. A key target of these efforts is the spike (S) protein, which is metastable and difficult to produce recombinantly. Here, we characterized 100 structure-guided spike designs and identified 26 individual substitutions that increased protein yields and stability. Testing combinations of beneficial substitutions resulted in the identification of HexaPro, a variant with six beneficial proline substitutions exhibiting ~10-fold higher expression than its parental construct and the ability to withstand heat stress, storage at room temperature, and three freeze-thaw cycles. A 3.2 Å-resolution cryo-EM structure of HexaPro confirmed that it retains the prefusion spike conformation. High-yield production of a stabilized prefusion spike protein will accelerate the development of vaccines and serological diagnostics for SARS-CoV-2.

What is already known about this topic? Older adults and those with chronic obstructive pulmonary disease, heart disease, diabetes, chronic kidney disease, and obesity are at higher risk for severe COVID-19–associated illness. What is added by this report? The median model-based estimate of the prevalence of any of five underlying medical conditions associated with increased risk for severe COVID-19–associated illness among U.S. adults was 47.2% among 3,142 U.S. counties. The estimated number of persons with these conditions followed population distributions, but prevalence was higher in more rural counties. What are the implications for public health practice? The findings can help local decision-makers identify areas at higher risk for severe COVID-19 illness in their jurisdictions and guide resource allocation and implementation of community mitigation strategies.

BackgroundThis is the first randomised controlled trial for assessment of the immunogenicity and safety of a candidate non-replicating adenovirus type-5 (Ad5)-vectored COVID-19 vaccine, aiming to determine an appropriate dose of the candidate vaccine for an efficacy study.MethodsThis randomised, double-blind, placebo-controlled, phase 2 trial of the Ad5-vectored COVID-19 vaccine was done in a single centre in Wuhan, China. Healthy adults aged 18 years or older, who were HIV-negative and previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-free, were eligible to participate and were randomly assigned to receive the vaccine at a dose of 1 × 1011 viral particles per mL or 5 × 1010 viral particles per mL, or placebo. Investigators allocated participants at a ratio of 2:1:1 to receive a single injection intramuscularly in the arm. The randomisation list (block size 4) was generated by an independent statistician. Participants, investigators, and staff undertaking laboratory analyses were masked to group allocation. The primary endpoints for immunogenicity were the geometric mean titres (GMTs) of specific ELISA antibody responses to the receptor binding domain (RBD) and neutralising antibody responses at day 28. The primary endpoint for safety evaluation was the incidence of adverse reactions within 14 days. All recruited participants who received at least one dose were included in the primary and safety analyses. This study is registered with ClinicalTrials.gov, NCT04341389.Findings603 volunteers were recruited and screened for eligibility between April 11 and 16, 2020. 508 eligible participants (50% male; mean age 39·7 years, SD 12·5) consented to participate in the trial and were randomly assigned to receive the vaccine (1 × 1011 viral particles n=253; 5 × 1010 viral particles n=129) or placebo (n=126). In the 1 × 1011 and 5 × 1010 viral particles dose groups, the RBD-specific ELISA antibodies peaked at 656·5 (95% CI 575·2–749·2) and 571·0 (467·6–697·3), with seroconversion rates at 96% (95% CI 93–98) and 97% (92–99), respectively, at day 28. Both doses of the vaccine induced significant neutralising antibody responses to live SARS-CoV-2, with GMTs of 19·5 (95% CI 16·8–22·7) and 18·3 (14·4–23·3) in participants receiving 1 × 1011 and 5 × 1010 viral particles, respectively. Specific interferon γ enzyme-linked immunospot assay responses post vaccination were observed in 227 (90%, 95% CI 85–93) of 253 and 113 (88%, 81–92) of 129 participants in the 1 × 1011 and 5 × 1010 viral particles dose groups, respectively. Solicited adverse reactions were reported by 183 (72%) of 253 and 96 (74%) of 129 participants in the 1 × 1011 and 5 × 1010 viral particles dose groups, respectively. Severe adverse reactions were reported by 24 (9%) participants in the 1 × 1011 viral particles dose group and one (1%) participant in the 5 × 1010 viral particles dose group. No serious adverse reactions were documented.InterpretationThe Ad5-vectored COVID-19 vaccine at 5 × 1010 viral particles is safe, and induced significant immune responses in the majority of recipients after a single immunisation.

The COVID-19 pandemic, which is caused by the novel coronavirus SARS-CoV-2, has been associated with more than 470,000 fatal cases worldwide. In order to develop antiviral interventions quickly, drugs used for treatment of unrelated diseases are currently being repurposed to combat COVID-19. Chloroquine is a anti-malaria drug that is frequently employed for COVID-19 treatment since it inhibits SARS-CoV-2 spread in the kidney-derived cell line Vero1–3. Here, we show that engineered expression of TMPRSS2, a cellular protease that activates SARS-CoV-2 for entry into lung cells4, renders SARS-CoV-2 infection of Vero cells insensitive to chloroquine. Moreover, we report that chloroquine does not block SARS-CoV-2 infection of the TMPRSS2-positive lung cell line Calu-3. These results indicate that chloroquine targets a pathway for viral activation that is not operative in lung cells and is unlikely to protect against SARS-CoV-2 spread in and between patients.

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads, the susceptible subpopulation declines causing the rate at which new infections occur to slow down. Variation in individual susceptibility or exposure to infection exacerbates this effect. Individuals that are more susceptible or more exposed tend to be infected and removed from the susceptible subpopulation earlier. This selective depletion of susceptibles intensifies the deceleration in incidence. Eventually, susceptible numbers become low enough to prevent epidemic growth or, in other words, the herd immunity threshold is reached. Here we fit epidemiological models with inbuilt distributions of susceptibility or exposure to SARS-CoV-2 outbreaks to estimate basic reproduction numbers (R_0) alongside coefficients of individual variation (CV) and the effects of containment strategies. Herd immunity thresholds are then calculated as 1-(1⁄R_0 )^(1⁄((1+〖CV〗^2 ) )) or 1-(1⁄R_0 )^(1⁄((1+〖2CV〗^2 ) )), depending on whether variation is on susceptibility or exposure. Our inferences result in herd immunity thresholds around 10-20%, considerably lower than the minimum coverage needed to interrupt transmission by random vaccination, which for R_0 higher than 2.5 is estimated above 60%. We emphasize that the classical formula, 1-1⁄R_0 , remains applicable to describe herd immunity thresholds for random vaccination, but not for immunity induced by infection which is naturally selective. These findings have profound consequences for the governance of the current pandemic given that some populations may be close to achieving herd immunity despite being under more or less strict social distancing measures.

Importance  Reported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection likely underestimate the prevalence of infection in affected communities. Large-scale seroprevalence studies provide better estimates of the proportion of the population previously infected.Objective  To estimate prevalence of SARS-CoV-2 antibodies in convenience samples from several geographic sites in the US.Design, Setting, and Participants  This cross-sectional study performed serologic testing on a convenience sample of residual sera obtained from persons of all ages. The serum was collected from March 23 through May 12, 2020, for routine clinical testing by 2 commercial laboratory companies. Sites of collection were San Francisco Bay area, California; Connecticut; south Florida; Louisiana; Minneapolis-St Paul-St Cloud metro area, Minnesota; Missouri; New York City metro area, New York; Philadelphia metro area, Pennsylvania; Utah; and western Washington State.Exposures  Infection with SARS-CoV-2.Main Outcomes and Measures  The presence of antibodies to SARS-CoV-2 spike protein was estimated using an enzyme-linked immunosorbent assay, and estimates were standardized to the site populations by age and sex. Estimates were adjusted for test performance characteristics (96.0% sensitivity and 99.3% specificity). The number of infections in each site was estimated by extrapolating seroprevalence to site populations; estimated infections were compared with the number of reported coronavirus disease 2019 (COVID-19) cases as of last specimen collection date.Results  Serum samples were tested from 16 025 persons, 8853 (55.2%) of whom were women; 1205 (7.5%) were 18 years or younger and 5845 (36.2%) were 65 years or older. Most specimens from each site had no evidence of antibodies to SARS-CoV-2. Adjusted estimates of the proportion of persons seroreactive to the SARS-CoV-2 spike protein antibodies ranged from 1.0% in the San Francisco Bay area (collected April 23-27) to 6.9% of persons in New York City (collected March 23-April 1). The estimated number of infections ranged from 6 to 24 times the number of reported cases; for 7 sites (Connecticut, Florida, Louisiana, Missouri, New York City metro area, Utah, and western Washington State), an estimated greater than 10 times more SARS-CoV-2 infections occurred than the number of reported cases.Conclusions and Relevance  During March to early May 2020, most persons in 10 diverse geographic sites in the US had not been infected with SARS-CoV-2 virus. The estimated number of infections, however, was much greater than the number of reported cases in all sites. The findings may reflect the number of persons who had mild or no illness or who did not seek medical care or undergo testing but who still may have contributed to ongoing virus transmission in the population.

BackgroundThe risk of vertical and perinatal transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, which causes COVID-19), the most appropriate management, and the neonate's risk of developing COVID-19 during the perinatal period are unknown. Therefore, we aimed to elucidate best practices regarding infection control in mother–newborn dyads, and identify potential risk factors associated with transmission.MethodsIn this observational cohort study, we identified all neonates born between March 22 and May 17, 2020, at three New York Presbyterian Hospitals in New York City (NY, USA) to mothers positive for SARS-CoV-2 at delivery. Mothers could practice skin-to-skin care and breastfeed in the delivery room, but had to wear a surgical mask when near their neonate and practice proper hand hygiene before skin-to-skin contact, breastfeeding, and routine care. Unless medically required, neonates were kept in a closed Giraffe isolette in the same room as their mothers, and were held by mothers for feeding after appropriate hand hygiene, breast cleansing, and placement of a surgical mask. Neonates were tested for SARS-CoV-2 by use of real-time PCR on nasopharyngeal swabs taken at 24 h, 5–7 days, and 14 days of life, and were clinically evaluated by telemedicine at 1 month of age. We recorded demographics, neonatal, and maternal clinical presentation, as well as infection control practices in the hospital and at home.FindingsOf 1481 deliveries, 116 (8%) mothers tested positive for SARS-CoV-2; 120 neonates were identified. All neonates were tested at 24 h of life and none were positive for SARS-CoV-2. 82 (68%) neonates completed follow-up at day 5–7 of life. Of the 82 neonates, 68 (83%) roomed in with the mothers. All mothers were allowed to breastfeed; at 5–7 days of life, 64 (78%) were still breastfeeding. 79 (96%) of 82 neonates had a repeat PCR at 5–7 days of life, which was negative in all; 72 (88%) neonates were also tested at 14 days of life and none were positive. None of the neonates had symptoms of COVID-19.InterpretationOur data suggest that perinatal transmission of COVID-19 is unlikely to occur if correct hygiene precautions are undertaken, and that allowing neonates to room in with their mothers and direct breastfeeding are safe procedures when paired with effective parental education of infant protective strategies.

Health officials are concerned about why some people who test positive for the coronavirus never feel sick. Could it be the luck of genetics? The blessings of youth? Or something else?

DeVos has falsely claimed the children are "stoppers" of COVID-19.

Reports of reinfection instead may be cases of drawn-out illness. A decline in antibodies is normal after a few weeks, and people are protected from the coronavirus in other ways.

Biologically, a vaccine against the COVID-19 virus is unlikely to offer complete protection. Logistically, manufacturers will have to make hundreds of millions of doses while relying, perhaps, on technology never before used in vaccines and competing for basic supplies such as glass vials. Then the federal government will have to allocate doses, perhaps through a patchwork of state and local health departments with no existing infrastructure for vaccinating adults at scale. The Centers for Disease Control and Prevention, which has led vaccine distribution efforts in the past, has been strikingly absent in discussions so far—a worrying sign that the leadership failures that have characterized the American pandemic could also hamper this process. To complicate it all, 20 percent of Americans already say they will refuse to get a COVID-19 vaccine, and with another 31 percent unsure, reaching herd immunity could be that much more difficult.

Radical public health responses to the pandemic around the world have asked us to make unprecedented changes to our daily lives. Social distancing measures require compliance with recommendations, instructions, and legal orders that come with undeniable sacrifices for almost all of us (though these sacrifices are far from equally distributed). These extreme public measures depend for their success on public trust.

The UK is ‘locked down’ because of coro­n­avirus (COVID-19). No clear exit strategy currently exists. This paper suggests a possible way forward that combines elements from economics and epi­demi­ol­ogy. The paper proposes as a policy a ‘release’ from lockdown of the young cohort of UK citizens aged between age 20 and 30 who do not live with parents. The paper cal­cu­lates that there are ap­prox­i­mately 4.2 million UK in­di­vid­u­als who fall into this 20-30 ageband and who live outside the original parental home. Of those, 2.6 million work in the private sector, so unless some cor­rec­tive action is taken they are likely to be extremely harshly affected, fi­nan­cially, when compared to employees in the public sector. The paper argues that a young-​workforce release of this kind would lead to sub­stan­tial economic and societal benefits without enormous health costs to the country. In this way, the nation might begin to move forward in the footsteps of the young. The paper’s key concept could in principle be im­ple­mented in other countries.

Preprint servers have existed for decades, but the fight against the coronavirus has seen their use soar. They’re changing how science is done—but need important guardrails.

COVID-19 is spreading and has reached the state of a worldwide pandemic and health systems are or will be tested in how they can deal with it. So far, during this early phase of the pandemic, outcomes in terms of case-​fatality rates (CFR) differ widely across countries. We explore how dif­fer­ences in living arrange­ments of gen­er­a­tions within families con­tribute to the cross country dif­fer­ences. We document a strong positive cor­re­la­tion between countries’ CFRs and the share of working-​age families living with their parents. This suggest that policy needs to focus on inter-​generational social distance when combating this pandemic.

New York City is the hot spot of the COVID-19 pandemic in the United States. This paper merges in­for­ma­tion on the number of tests and the number of in­fec­tions at the New York City zip code level with de­mo­graphic and so­cioe­co­nomic in­for­ma­tion from the decennial census and the American Community Surveys. People residing in poor or immigrant neigh­bor­hoods were less likely to be tested; but the like­li­hood that a test was positive was larger in those neigh­bor­hoods, as well as in neigh­bor­hoods with larger house­holds or pre­dom­i­nantly black pop­u­la­tions. The rate of infection in the pop­u­la­tion depends on both the frequency of tests and on the fraction of positive tests among those tested. The non-​randomness in testing across New York City neigh­bor­hoods indicates that the observed cor­re­la­tion between the rate of infection and the so­cioe­co­nomic char­ac­ter­is­tics of a community tells an in­com­plete story of how the pandemic evolved in a congested urban setting.

It is almost certain that the world economy is entering a recession of historic pro­por­tions; how bad things get will depend on how gov­ern­ments manage the Covid-19 pandemic. At the core of the problem lies a very difficult choice: whether to “flatten the curve” of the epidemic or whether to flatten the curve of the recession. It is unlikely that both can be achieved and, in this case, it is better to address the tradeoff heads-on rather than try to ignore it or assume it doesn’t exist. Because de­vel­op­ing countries are less prepared to deal with the con­se­quences of an economic downturn, they might not be able to afford “social dis­tanc­ing” policies for extended periods.

What is the response of US governors to the COVID-19 pandemic? In this research note, we explore the de­ter­mi­nants of im­ple­ment­ing stay-​at-home orders, focusing on governors’ char­ac­ter­is­tics. In our most con­ser­v­a­tive estimate, being a De­mo­c­ra­tic governor increases the prob­a­bil­ity of im­ple­ment­ing a stay-​at-home order by more than 50 percent. Moreover, we find that the prob­a­bil­ity of im­ple­ment­ing a statewide stay-​at-home order is about 40 percent more likely for governors without a term limit than governors with a term limit. We also find that De­mo­c­ra­tic governors and governors without a term limit are sig­nif­i­cantly faster to adopt statewide orders than Re­pub­li­can governors and governors with a term limit. There is evidence of politics as usual in these unusual times.

Which jobs are more likely to be affected by mobility re­stric­tions due to the Covid-19 pandemic? This paper uses American Time Use Survey data to measure the share of the work hours that are spent at home for different job cat­e­gories. We compute and provide home-​working shares by oc­cu­pa­tion (US census clas­si­fi­ca­tion, SOC and in­ter­na­tional ISCO clas­si­fi­ca­tion), and by industry (US census clas­si­fi­ca­tion, NAICS and in­ter­na­tional ISIC clas­si­fi­ca­tion).

Using the 2012-13 American Time Use Survey, I find that both who people spend time with and how they spend it affect their happiness, adjusted for numerous de­mo­graphic and economic variables. Sat­is­fac­tion among married in­di­vid­u­als increases most with ad­di­tional time spent with spouse. Among singles, sat­is­fac­tion decreases most as more time is spent alone. Assuming that lockdowns constrain married people to spend time solely with their spouses, sim­u­la­tions show that their happiness may have been increased compared to before the lockdowns; but suf­fi­ciently large losses of work time and income reverse this inference. Sim­u­la­tions demon­strate clearly that, assuming lockdowns impose solitude on singles, their happiness was reduced, re­duc­tions that are made more severe by income and work losses.

Many countries consider the lifting of re­stric­tions of social contacts (RSC). We quantify the effects of RSC for Germany. We initially employ a purely sta­tis­ti­cal approach to pre­dict­ing preva­lence of COVID19 if RSC were upheld after April 20. We employ these findings and feed them into our the­o­ret­i­cal model. We find that the peak of the number of sick in­di­vid­u­als would be reached already in April. The number of sick in­di­vid­u­als would fall below 1,000 at the beginning of July. When re­stric­tions are lifted com­pletely on April 20, the number of sick should rise quickly again from around April 27. A balance between economic and in­di­vid­ual costs of RSC and public health ob­jec­tives consists in lifting RSC for ac­tiv­i­ties that have high economic benefits but low health costs. In the absence of large-​scale rep­re­sen­ta­tive testing of CoV-2 in­fec­tions, these ac­tiv­i­ties can most easily be iden­ti­fied if federal states of Germany adopted exit strate­gies that differ across states.

Due to the COVID-19 crisis and the related “social dis­tanc­ing” measures, working from home (WfH) has suddenly become a crucial lever of economic activity. This paper combines survey and ad­min­is­tra­tive data to compute measures for the fea­si­bil­ity of working from home among German employees. Breaking down the data by oc­cu­pa­tion, region, industry, and employee char­ac­ter­is­tics, we document con­sid­er­able variation in the potential to WfH across all di­men­sions. We find that WfH is feasible for roughly 56 percent of the overall German workforce, while less than half of this potential was exploited in the pre-​pandemic economy.

In this ongoing project, we examine the short-​term con­se­quences of COVID-19 on em­ploy­ment and wages in the United States. Guided by a pre-​analysis plan, we document the impact of COVID-19 at the national-​level using a simple dif­fer­ence and test whether states with rel­a­tively more confirmed cases/deaths were more affected. Our findings suggest that COVID-19 increased the un­em­ploy­ment rate, decreased hours of work and labor force par­tic­i­pa­tion and had no sig­nif­i­cant impacts on wages. The negative impacts on labor market outcomes are larger for men, younger workers, Hispanics and less-​educated workers. This suggest that COVID-19 increases labor market in­equal­i­ties. We also in­ves­ti­gate whether the economic con­se­quences of this pandemic were larger for certain oc­cu­pa­tions. We built three indexes using ACS and O*NET data: workers rel­a­tively more exposed to disease, workers that work with proximity to coworkers and workers who can easily work remotely. Our estimates suggest that in­di­vid­u­als in oc­cu­pa­tions working in proximity to others are more affected while oc­cu­pa­tions able to work remotely are less affected. We also find that oc­cu­pa­tions classifed as more exposed to disease are less affected, possibly due to the large number of essential workers in these oc­cu­pa­tions.

On March 19, 2020, Cal­i­for­nia Governor Gavin Newsom issued Executive Order N-33-20 2020, which required all residents of the state of Cal­i­for­nia to shelter in place for all but essential ac­tiv­i­ties such as grocery shopping, re­triev­ing pre­scrip­tions from a pharmacy, or caring for relatives. This shelter-​in-place order (SIPO), the first such statewide order issued in the United States, was designed to reduce COVID-19 cases and mortality. While the White House Task Force on the Coro­n­avirus has credited the State of Cal­i­for­nia for taking early action to prevent a statewide COVID-19 outbreak, no study has examined the impact of Cal­i­for­nia’s SIPO. Using daily state-​level coro­n­avirus data and a synthetic control research design, we find that Cal­i­for­nia’s statewide SIPO reduced COVID-19 cases by 125.5 to 219.7 per 100,000 pop­u­la­tion by April 20, one month following the order. We further find that Cal­i­for­nia’s SIPO led to as many as 1,661 fewer COVID-19 deaths during the first four weeks following its enactment. Back-​of-the-envelope cal­cu­la­tions suggest that there were about 400 job losses per life saved during this short-run post-​treatment period.

This study is the first in the world to in­ves­ti­gate the expected impact of the COVID-19 crisis on career outcomes and career as­pi­ra­tions. To this end, high-​quality survey research with a relevant panel of Belgian employees was conducted. About 21% of them fear losing their jobs due to the crisis—14% are concerned that they will even lose their jobs in the near future. In addition, 26% expect to miss out on pro­mo­tions that they would have received had the COVID-19 crisis not occurred. This fear of a negative impact is higher in vul­ner­a­ble groups, such migrants. In addition, we observe that many panel members believe they will look at the labour market dif­fer­ently and will have different work-​related pri­or­i­ties in the future. In this respect, more than half of the panel members indicate that they have attached more im­por­tance to working con­di­tions and work-life balance since the COVID-19 crisis.

Ar­ti­fi­cial In­tel­li­gence (AI) is a po­ten­tially powerful tool in the fight against the COVID- 19 pandemic. Since the outbreak of the pandemic, there has been a scramble to use AI. This article provides an early, and nec­es­sar­ily selective review, dis­cussing the con­tri­bu­tion of AI to the fight against COVID-19, as well as the current con­straints on these con­tri­bu­tions. Six areas where AI can con­tribute to the fight against COVID-19 are discussed, namely i) early warnings and alerts, ii) tracking and pre­dic­tion, iii) data dash­boards, iv) diagnosis and prognosis, v) treat­ments and cures, and vi) social control. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Over­com­ing these con­straints will require a careful balance between data privacy and public health, and rigorous human-AI in­ter­ac­tion. It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of di­ag­nos­tic data on who is in­fec­tious will be essential to save lives, train AI, and limit economic damages.

Food pro­duc­tion and dis­tri­b­u­tion is essential for human well-​being, but the food sector has ex­pe­ri­enced a number of dif­fi­cul­ties main­tain­ing worker health and pro­duc­tiv­ity during the COVID-19 pandemic. We examine em­ploy­ment status changes of persons recently employed in the U.S. food sector with a focus on food man­u­fac­tur­ing and grocery stores. We find that the pandemic sig­nif­i­cantly reduced the prob­a­bil­ity of continued active em­ploy­ment for previous workers in both food man­u­fac­tur­ing and grocery stores. Individual-​level analysis confirms that the COVID-19 infection rate in an in­di­vid­ual’s local labor market is a strong and sig­nif­i­cant factor. The em­ploy­ment changes are not just due to un­em­ploy­ment during facility closures. Previous workers in­creas­ingly exit the labor force as the severity of the COVID-19 infection rate in their local area worsens. The con­sid­er­able risk of infection drives many previous food sector workers to stop working al­to­gether. Main­tain­ing worker health and safety is essential for a stable food supply.

In response to the Covid-19 pandemic, gov­ern­ments around the world have provided a massive fiscal and monetary stimulus. While this policy is welcome in the short run, it does not address the un­der­ly­ing problem in the medium and long run. The reason is that the pandemic has not given rise to a gen­er­al­ized shortfall in aggregate demand. Rather, it has generated a Great Economic Mismatch, char­ac­ter­ized by deficient demand for things requiring close physical in­ter­ac­tions among people and deficient supply of things com­pat­i­ble with social dis­tanc­ing, where ap­pro­pri­ate. Expansive macro­eco­nomic policy can stimulate aggregate demand, but when social dis­tanc­ing is enforced, it will not stimulate pro­duc­tion and con­sump­tion whenever this demand is satisfied through phys­i­cally in­ter­ac­tive ac­tiv­i­ties. To overcome the Great Economic Mismatch, “readap­ta­tion policies” are called for. In the medium run, these policies promote a redi­rec­tion of resources to ac­tiv­i­ties com­pat­i­ble with social dis­tanc­ing; the long run, these policies make economies more resilient to un­fore­seen shocks that generate a Great Economic Mismatch. Once the pandemic is over, a more profound re­think­ing of decision making – in public policy, business and civil society – is called for. First, decision makers will need to sup­ple­ment the current focus on economic ef­fi­ciency by greater emphasis on economic re­silience. Second, economic policies and business strate­gies will need to focus less on in­cen­tives for selfish in­di­vid­u­als and more on the mo­bi­liza­tion of people’s prosocial motives. Finally, to encourage people around the world to cooperate globally in tackling global problems, policy makers at local, national and global levels will need to encourage people around the world to cooperate globally in tackling global problems, with the aid of two powerful tools that humans through­out history have used to co­or­di­nate their efforts: identity-​shaping nar­ra­tives and in­sti­tu­tions of multi-​level gov­er­nance.

Disease spread is in part a function of in­di­vid­ual behavior. We examine the factors pre­dict­ing in­di­vid­ual behavior during the Covid-19 pandemic in the United States using novel data collected by Belot et al. (2020). Among other factors, we show that people with lower income, less flexible work arrange­ments (e.g., an inability to tele-work) and lack of outside space at home are less likely to engage in behaviors, such as social dis­tanc­ing, that limit the spread of disease. We also find evidence that region, gender and beliefs predict behavior. Broadly, our findings align with typical re­la­tion­ships between health and socio-​economic status. Moreover, they suggest that the burden of measures designed to stem the pandemic are unevenly dis­trib­uted across socio-​demographic groups in ways that affect behavior and thus po­ten­tially the spread of illness. Policies that assume otherwise are unlikely to be effective or sus­tain­able.

We in­ves­ti­gate the impacts of COVID-19 on domestic violence and family stress. Our empirical analysis relies on a unique online survey, Canadian Per­spec­tive Survey Series, that allow us to dis­en­tan­gle the mech­a­nisms through which COVID-19 may affect family stress and domestic violence. We find no evidence that em­ploy­ment status and work arrange­ments are related to higher self-​reported levels of family stress and violence in the home due to con­fine­ment, sug­gest­ing that remote working on a large scale does not lead to family violence. In contrast, we find that the inability to meet financial oblig­a­tions and main­tain­ing social ties sig­nif­i­cantly increase reported family stress and domestic violence. These findings are con­sis­tent with two al­ter­na­tive mech­a­nisms: social isolation and decreased bar­gain­ing power for women. Last, we provide sug­ges­tive evidence that receiving financial relief does not mitigate the effect of financial worries on domestic violence and family stress. We conclude that targeted programs sup­port­ing victims of domestic violence may be more effective.

Worries about the impact of COVID-19 on pregnant mothers and their offspring are wide-​spread. As a com­par­i­son, the Spanish Flu pandemic had dev­as­tat­ing health impacts on pregnant mothers and in-utero exposure to influenza is known to have negative short- and long-term con­se­quences for children. The existing evidence from the COVID-19 pandemic, however, allows for cautious optimism about the impacts of COVID-19 on pregnant women and their children.

This review of UK economic policy responses to the Covid-19 crisis iden­ti­fies serious problems with existing measures. We describe al­ter­na­tive policies which could alleviate hardship, protect business from de­struc­tion in the growing de­pres­sion, fa­cil­i­tate recovery with full em­ploy­ment in a Green New Deal, and re­dis­trib­ute income and power with economic democracy in the workplace. Only such policies can ensure high quality work for all, the natural rights of self-​determination at work, and equitable sharing of the surplus that is produced by all employees as in­ten­tional agents. The proposed reforms are opposed by the strong vested interests which currently hold most power, so mo­bil­is­ing popular support and achieving real change will require a long struggle, just as attaining political democracy a century ago did.

In an effort to contain the spread of the COVID-19 pandemic, many countries around the globe adopted social dis­tanc­ing measures. Previous studies have relied on the ge­o­graph­i­cal and temporal variation in the adoption of non-​pharmaceutical in­ter­ven­tions (NPIs) to show that early adoption of NPIs is cor­re­lated to lower infection and mortality rates. However, due to the non-​random adoption of NPIs, the findings may not be in­ter­preted as causal. We address this lim­i­ta­tion using a different source of iden­ti­fi­ca­tion –namely, the regional variation in the placement on the pandemic curve at the time of a na­tion­wide lockdown. Our results reveal how, relative to regions for which the lockdown arrived 10+ days after the pandemic’s outbreak, regions where the outbreak had just started were able to lower their daily fatality rate by 2.5 deaths per 100,000 in­hab­i­tants. We also provide sug­ges­tive evidence of contagion de­cel­er­a­tion as the main mechanism behind the ef­fec­tive­ness of the early adoption of NPIs in lowering the death rate, rather than increased health­care capacity.

Both the White House and state governors have ex­plic­itly linked thresh­olds of reduced COVID-19 case growth to the lifting of statewide shelter-​in-place orders (SIPOs). This “hardwired” policy en­do­gene­ity creates empirical chal­lenges in credibly isolating the causal effect of lifting a statewide SIPO on COVID-19-related health. To break this si­mul­tane­ity problem, the current study exploits a unique natural ex­per­i­ment generated by a Wisconsin Supreme Court decision. On May 13, 2020, the Wisconsin Supreme Court abolished the state’s “Safer at Home” order, ruling that the Wisconsin De­part­ment of Health Services un­con­sti­tu­tion­ally usurped leg­isla­tive authority to review COVID-19 reg­u­la­tions. We cap­i­tal­ize on this sudden, dramatic, and largely unan­tic­i­pated ter­mi­na­tion of a statewide SIPO to estimate its effect on social dis­tanc­ing and COVID-19 case growth. Using a synthetic control design, we find no evidence that the repeal of the state SIPO impacted social dis­tanc­ing, COVID-19 cases, or COVID-19-related mortality during the fortnight following enactment. Estimated effects were eco­nom­i­cally small and nowhere near sta­tis­ti­cally different from zero. We conclude that the impact of shelter-​in-place orders is likely not symmetric across enactment and lifting of the orders.

The COVID-19 pandemic has resulted in income and em­ploy­ment loss in many countries around the world. Yet, hardly any formal study exists on household finance and future economic ex­pec­ta­tions in poorer countries. To fill in this gap, we im­ple­mented and analyzed a web-based rapid as­sess­ment survey im­me­di­ately after the removal of lockdown measures in Vietnam, a lower-​middle-income country that has received wide­spread recog­ni­tion for its suc­cess­ful fight against the pandemic. We find that having a job is strongly and pos­i­tively as­so­ci­ated with better finance and more income and savings, as well as more optimism about the re­silience of the economy. Further dis­ag­gre­gat­ing em­ploy­ment into different types of jobs such as self-​employment and jobs with permanent and short-​term contracts, we find those with permanent job contracts to be more strongly as­so­ci­ated with better as­sess­ments and fewer job worries. In­di­vid­u­als with good health and higher ed­u­ca­tional levels also have more positive eval­u­a­tions for their current and future finance. These findings are relevant for post-​outbreak economic policies, es­pe­cially regarding the labor market in a de­vel­op­ing country context

This paper models the local and cross-​city trans­mis­sions of the novel coro­n­avirus in China between January 19 and February 29 in 2020. We examine the role of various so­cioe­co­nomic mediating factors, including public health measures that encourage social dis­tanc­ing in local com­mu­ni­ties. Weather char­ac­ter­is­tics two weeks ago are used as in­stru­men­tal variables for causal inference. Stringent quar­an­tine, city lockdown, and local public health measures imposed since late January sig­nif­i­cantly decreased the virus trans­mis­sion rate. The virus spread was contained by the middle of February. Pop­u­la­tion outflow from the outbreak source region posed a higher risk to the des­ti­na­tion regions than other factors including ge­o­graphic proximity and sim­i­lar­ity in economic con­di­tions. We quantify the effects of different public health measures in reducing the number of in­fec­tions through coun­ter­fac­tual analyses. Over 1.4 million in­fec­tions and 56,000 deaths could have been avoided as a result of the national and provin­cial public health measures imposed in late January in China.

This note describes the con­tri­bu­tion of migrant workers to the ongoing effort to keep basic services running in the Union during the COVID-19 epidemic. We quantify the preva­lence of migrant workers in the so called “key pro­fes­sions” that the Com­mis­sion and Member States have iden­ti­fied using the most recent wave of the EU Labour Force Survey. Our results show that migrant “key workers” are essential for critical functions in European societies.

We present real time survey evidence from the UK, US and Germany showing that the labor market impacts of COVID-19 differ con­sid­er­ably across countries. Employees in Germany, which has a well-​established short-​time work scheme, are sub­stan­tially less likely to be affected by the crisis. Within countries, the impacts are highly unequal and ex­ac­er­bate existing in­equal­i­ties. Workers in al­ter­na­tive work arrange­ments and in oc­cu­pa­tions in which only a small share of tasks can be done from home are more likely to have reduced their hours, lost their jobs and suffered falls in earnings. Less educated workers and women are more affected by the crisis.

It is politi­cians who have to decide when to release the lockdown, and in what way. In doing so, they have to balance many con­sid­er­a­tions (as with any decision). Often the different con­sid­er­a­tions appear in­com­men­su­rable so that only the roughest of judge­ments can be made. For example, in the case of COVID-19, one has to compare the economic benefits of releasing the lockdown with the social and psy­cho­log­i­cal benefits, and then compare the total of these with the increase in deaths that would result from an early exit. We here propose a way of doing this more sys­tem­at­i­cally.

Shelter in place orders (SIPOs) require residents to remain home for all but essential ac­tiv­i­ties such as pur­chas­ing food or medicine, caring for others, exercise, or traveling for em­ploy­ment deemed essential. Between March 19 and April 20, 2020, 40 states and the District of Columbia adopted SIPOs. This study explores the impact of SIPOs on health, with par­tic­u­lar attention to het­ero­gene­ity in their impacts. First, using daily state-​level social dis­tanc­ing data from SafeGraph and a difference-​in-differences approach, we document that adoption of a SIPO was as­so­ci­ated with a 5 to 10 percent increase in the rate at which state residents remained in their homes full-time. Then, using daily state-​level coro­n­avirus case data collected by the Centers for Disease Control and Pre­ven­tion, we find that ap­prox­i­mately three weeks following the adoption of a SIPO, cu­mu­la­tive COVID-19 cases fell by 44 percent. Event-​study analyses confirm common COVID-19 case trends in the week prior to SIPO adoption and show that SIPO-​induced case re­duc­tions grew larger over time. However, this average effect masks important het­ero­gene­ity across states — early adopters and high pop­u­la­tion density states appear to reap larger benefits from their SIPOs. Finally, we find that statewide SIPOs were as­so­ci­ated with a reduction in coronavirus-​related deaths, but estimated mortality effects were im­pre­cisely estimated.

This note describes some of the early policy de­vel­op­ments in the UK and the way in which the framing and un­der­stand­ing of a novel economic problem evolved to include a focus on liveli­hoods combining social pro­tec­tion and business support ori­en­ta­tions. It high­lights various points including the rapid iterative nature of policy de­vel­op­ment based on con­sul­ta­tion in the face of un­cer­tainty, as well as a trade-off between risk-​sharing with com­mer­cial banks to limit adverse selection and the rapid pre­ven­tion of in­sol­ven­cies. We consider some of the policy-​making lessons to date and conclude with sug­ges­tions for issues that policy makers might consider to support workers in the near and medium term.

In response to the COVID-19 pandemic, many states have adopted stay-​at-home orders, rendering a large segment of the workforce unable to continue doing their jobs. These policies have dis­tri­b­u­tional con­se­quences, as workers in some oc­cu­pa­tions may be better able to continue their work from home. I identify the segments of the U.S. workforce that can plausibly work from home by linking oc­cu­pa­tion data from O*NET to the American Community Survey. I find that lower-​wage workers are up to three times less likely to be able to work from home than higher-​wage workers. Those with lower levels of education, younger adults, ethnic mi­nori­ties, and im­mi­grants are also con­cen­trated in oc­cu­pa­tions that are less likely to be performed from home.

We study how the dif­fer­en­tial timing of local lockdowns due to COVID-19 causally affects house­holds’ spending and macro­eco­nomic ex­pec­ta­tions at the local level using several waves of a cus­tomized survey with more than 10,000 re­spon­dents. About 50% of survey par­tic­i­pants report income and wealth losses due to the corona virus, with the average losses being $5,293 and $33,482 re­spec­tively. Aggregate consumer spending dropped by 31 log per­cent­age points with the largest drops in travel and clothing. We find that house­holds living in counties that went into lockdown earlier expect the un­em­ploy­ment rate over the next twelve months to be 13 per­cent­age points higher and continue to expect higher un­em­ploy­ment at horizons of three to five years. They also expect lower future inflation, report higher un­cer­tainty, expect lower mortgage rates for up to 10 years, and have moved out of foreign stocks into liquid forms of savings. The im­po­si­tion of lockdowns can account for much of the decline in em­ploy­ment in recent months as well as declines in consumer spending. While lockdowns have pro­nounced effects on local economic con­di­tions and house­holds’ ex­pec­ta­tions, they have little impact on approval ratings of Congress, the Fed, or the Treasury but lead to declines in the approval of the President.

Using a large-​scale survey of U.S. house­holds during the Covid-19 pandemic, we study how new in­for­ma­tion about fiscal and monetary policy responses to the crisis affects house­holds’ ex­pec­ta­tions. We provide random subsets of par­tic­i­pants in the Nielsen Homescan panel with different com­bi­na­tions of in­for­ma­tion about the severity of the pandemic, recent actions by the Federal Reserve, stimulus measures, as well as rec­om­men­da­tions from health officials. This ex­per­i­ment allows us to assess to what extent these policy an­nounce­ments alter the beliefs and spending plans of house­holds. In short, they do not, contrary to the powerful effects they have in standard macro­eco­nomic models.

Social contacts are a key trans­mis­sion channel of in­fec­tious diseases spread by the res­pi­ra­tory or close-​contact route, such as COVID-19. There is no evidence, however, on the question of whether the nature and the or­gan­i­sa­tion of work affect the spread of COVID-19 in different countries. I have developed a method­ol­ogy to measure country-​specific levels of oc­cu­pa­tional exposure to contagion driven by social contacts. I combined six in­di­ca­tors based on Oc­cu­pa­tion In­for­ma­tion Network (O*NET) and the European Working Condition Survey (EWCS) data. I then applied them to 26 European countries, and found sub­stan­tial cross-​country dif­fer­ences in levels of exposure to contagion in com­pa­ra­ble oc­cu­pa­tions. The resulting country-​level measures of levels of exposure to contagion (excluding health pro­fes­sions) predict the growth in COVID-19 cases, and the number of deaths from COVID-19 in the early stage of pandemic (up to four weeks after the 100th case). The re­la­tion­ship between levels of oc­cu­pa­tional exposure to contagion and the spread of COVID-19 is par­tic­u­larly strong for workers aged 45-64. I found that 20-25% of the cross-​country variance in numbers of COVID-19 cases and deaths can be at­trib­uted to cross-​country dif­fer­ences in levels of oc­cu­pa­tional exposure to contagion in European countries. My findings are robust to con­trol­ling for the strin­gency of con­tain­ment policies, such as lockdowns and school closures. They are also driven by country-​specific patterns of social contacts at work, rather than by oc­cu­pa­tional struc­tures. Thus, I conclude that measuring workplace in­ter­ac­tions may help to predict the next waves of the COVID-19 pandemic.

While a con­sid­er­able number of employees across the globe are being forced to work from home due to the COVID-19 crisis, it is a guessing game as to how they are ex­pe­ri­enc­ing this current surge in telework. Therefore, we examined employee per­cep­tions of telework on various life and career aspects, dis­tin­guish­ing between typical and extended telework during the COVID-19 crisis. To this end, we conducted a state-​of-the-art web survey among Flemish employees. Notwith­stand­ing this ex­cep­tional time of sudden, oblig­a­tory and high-​intensity telework, our re­spon­dents mainly attribute positive char­ac­ter­is­tics to tele­work­ing, such as increased ef­fi­ciency and a lower risk of burnout. The results also suggest that the over­whelm­ing majority of the surveyed employees believe that tele­work­ing (85%) and digital con­fer­enc­ing (81%) are here to stay. In contrast, some fear that telework di­min­ishes their promotion op­por­tu­ni­ties and weakens ties with their col­leagues and employer.

In light of the existing pre­lim­i­nary evidence of a link between Covid-19 and poor air quality, which is largely based upon cor­re­la­tions, we estimate the re­la­tion­ship between long term air pollution exposure and Covid-19 in 355 mu­nic­i­pal­i­ties in the Nether­lands. Using detailed secondary and ad­min­is­tra­tive data we find com­pelling evidence of a positive re­la­tion­ship between air pollution, and par­tic­u­larly PM2.5 con­cen­tra­tions, and Covid-19 cases, hospital ad­mis­sions and deaths. This re­la­tion­ship persists after con­trol­ling for a wide range of ex­plana­tory variables. Our results indicate that a 1 μ/m3 increase in PM2.5 con­cen­tra­tions is as­so­ci­ated with 9.4 more Covid-19 cases, 3.0 more hospital ad­mis­sions, and 2.3 more deaths. The re­la­tion­ship between Covid-19 and air pollution with­stands a number of sen­si­tiv­ity and ro­bust­ness exercises including in­stru­ment­ing pollution to mitigate potential en­do­gene­ity and modelling spatial spillovers using spatial econo­met­ric tech­niques.

The evolutionary biologist on data manipulation, fake news, and the importance of using science as a lie detector

We explore the role of social capital in the spread of the recent Covid-19 pandemic in independent analyses for Austria, Germany, Italy, the Netherlands, Sweden, Switzerland and the UK. Exploiting within-country variation, we show that a one standard deviation increase in social capital leads to 12% and 32% fewer Covid-19 cases per capita accumulated from mid-March until mid-May. Using Italy as a case study, we find that high-social-capital areas exhibit lower excess mortality and a decline in mobility. Our results have important implications for the design of local containment policies in future waves of the pandemic.

We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based economic policy uncertainty, twitter chatter about economic uncertainty, subjective uncertainty about future business growth, and disagreement among professional forecasters about future GDP growth. Three results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly – from a rise of around 100% (relative to January 2020) in two-year implied volatility on the S&P 500 and subjective uncertainty around year-ahead sales for UK firms to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting the difference in uncertainty measures between Wall Street and Main Street.

Nursing homes and other long term-care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes---and the crucial role these connections serve in spreading a highly contagious respiratory infection---is currently unknown given the lack of centralized data on cross-facility nursing home employment. In this paper, we perform the first large-scale analysis of nursing home connections via shared staff using device-level geolocation data from 30 million smartphones, and find that 7 percent of smartphones appearing in a nursing home also appeared in at least one other facility---even after visitor restrictions were imposed. We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections with 15 other facilities. Controlling for demographic and other factors, a home's staff-network connections and its centrality within the greater network strongly predict COVID-19 cases. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Results suggest that eliminating staff linkages between nursing homes could reduce COVID-19 infections in nursing homes by 44 percent.

Even when, objectively speaking, death is on the line, partisan bias still colors beliefs about facts. We show that a higher share of Trump voters in a county is associated with lower perceptions of risk during the COVID-19 pandemic. As Trump voter share rises, individuals search less for information on the virus, and engage in less social distancing behavior, as measured by smartphone location patterns. These patterns persist in the face of state-level mandates to close schools and businesses or to “stay home,” and reverse only when conservative politicians are exposed and the White House releases federal social distancing guidelines.

Assessing the economic impact of the COVID-19 pandemic is essential for policymakers, but challenging because the crisis has unfolded with extreme speed. We identify three indicators – stock market volatility, newspaper-based economic uncertainty, and subjective uncertainty in business expectation surveys – that provide real-time forward-looking uncertainty measures. We use these indicators to document and quantify the enormous increase in economic uncertainty in the past several weeks. We also illustrate how these forward-looking measures can be used to assess the macroeconomic impact of the COVID-19 crisis. Specifically, we feed COVID-induced first-moment and uncertainty shocks into an estimated model of disaster effects developed by Baker, Bloom and Terry (2020). Our illustrative exercise implies a year-on-year contraction in U.S. real GDP of nearly 11 percent as of 2020 Q4, with a 90 percent confidence interval extending to a nearly 20 percent contraction. The exercise says that about half of the forecasted output contraction reflects a negative effect of COVID-induced uncertainty.

We use high frequency internet search data to study in real time how US households sought out online learning resources as schools closed due to the Covid-19 pandemic. By April 2020, nationwide search intensity for both school- and parent-centered online learning resources had roughly doubled relative to baseline. Areas of the country with higher income, better internet access and fewer rural schools saw substantially larger increases in search intensity. The pandemic will likely widen achievement gaps along these dimensions given schools' and parents' differing engagement with online resources to compensate for lost school-based learning time. Accounting for such differences and promoting more equitable access to online learning could improve the effectiveness of education policy responses to the pandemic. The public availability of internet search data allows our analyses to be updated when schools reopen and to be replicated in other countries.

The outbreak of COVID-19 has called for swift action by governments, often involving the adoption of restrictive measures such as lockdowns. In this context, leaders have faced a trade-off between imposing stringent measures to limit the contagion, and minimizing the costs on their national economy, which could impact their electoral prospects. Leveraging on both the timing of elections and the constitutional term limits faced by leaders, we document how incumbents who can run for re-election implement less stringent restrictions when the election is closer in time. The effect is driven by measures more likely to have a negative economic impact. This shows how electoral concerns help explain the observed differences in the response to COVID-19 across different countries.

The covid-19 crisis has led to a sharp deterioration in firm and bank balance sheets. The government has responded with a massive intervention in corporate credit markets. We study equilibrium dynamics of macroeconomic quantities and prices, and how they are affected by government policy. The interventions prevent a much deeper crisis by reducing corporate bankruptcies by about half and short-circuiting the doom loop between corporate and financial sector fragility. The additional fiscal cost is zero since program spending replaces what would otherwise have been spent on intermediary bailouts. The model predicts rising interest rates on government debt and slow debt pay-down. We analyze an alternative intervention that targets aid to firms at risk of bankruptcy. While this policy prevents more bankruptcies and has lower fiscal cost, it only enjoys marginally higher welfare. Finally, we study longer-run consequences for firm leverage and intermediary health when pandemics become the new normal.

Using original data from two waves of a survey conducted in March and April 2020 in eight OECD countries (N = 21,649), we show that women are more likely to see COVID-19 as a very serious health problem, to agree with restraining public policy measures adopted in response to it, and to comply with them. Gender differences in attitudes and behavior are substantial in all countries, robust to controlling for a large set of sociodemographic, employment, psychological, and behavioral factors, and only partially mitigated for individuals who cohabit or have direct exposure to COVID-19. The results are not driven by differential social desirability bias. They carry important implications for the spread of the pandemic and may contribute to explain gender differences in vulnerability to it.

Social distancing restrictions and demand shifts from COVID-19 shuttered many small businesses and entrepreneurs in the first month of widespread shelter-in-place restrictions. Fairlie (2020) finds that 22 percent of small business owners were inactive in April 2020 with disproportionate impacts on African-American, Latinx, immigrant, and female business owners. What happened in the second month of social distancing restrictions? Were there further closures or a rebound? This paper provides the first analysis of impacts of the pandemic on the number of active small businesses in the United States using nationally representative data from the May 2020 CPS – the second month capturing effects from mandated restrictions. The number of active business owners in the United States is down by 2.2 million or 15 percent from February 2020, but up 7 percent since the low in April. The continued losses in May and partial rebound from April were felt across nearly all industries and were not sensitive to using alternative restrictions on hours worked and measures. African-American business owners continue to be the hardest hit by COVID-19 experiencing a drop of 26 percent in business activity from pre-COVID-19 levels. Latinx business owners fell by 19 percent, and Asian business owners dropped by 21 percent. Immigrant business owners experienced substantial losses of 25 percent. Simulations indicate that industry compositions partly placed black, Latinx and immigrant businesses at a higher risk of losses. All of these demographic groups, however, experienced partial rebounds in business activity from April lows. These findings of the continued early-stage losses to small businesses have important policy implications and may portend longer-term ramifications for job losses and economic inequality.

New York City is the hot spot of the COVID-19 pandemic in the United States. This paper merges information on the number of tests and the number of infections at the New York City zip code level with demographic and socioeconomic information from the decennial census and the American Community Surveys. People residing in poor or immigrant neighborhoods were less likely to be tested; but the likelihood that a test was positive was larger in those neighborhoods, as well as in neighborhoods with larger households or predominantly black populations. The rate of infection in the population depends on both the frequency of tests and on the fraction of positive tests among those tested. The non-randomness in testing across New York City neighborhoods indicates that the observed correlation between the rate of infection and the socioeconomic characteristics of a community tells an incomplete story of how the pandemic evolved in a congested urban setting.

The success of non-pharmaceutical interventions to contain pandemics often depends greatly upon voluntary compliance with government guidelines. What explains variation in voluntary compliance? Using mobile phone and survey data, we show that during the early phases of COVID-19, voluntary social distancing was higher when individuals exhibit a higher sense of civic duty. This is true for U.S. individuals, U.S. counties, and European regions. We also show that after U.S. states began re-opening, social distancing remained more prevalent in high civic capital counties. Our evidence points to the importance of civic capital in designing public policy responses to pandemics.

We conducted a large-scale survey covering 58 countries and over 100,000 respondents between late March and early April 2020 to study beliefs and attitudes towards citizens’ and governments’ responses to the COVID-19 pandemic. Most respondents reacted strongly to the crisis: they report engaging in social distancing and hygiene behaviors, and believe that strong policy measures, such as shop closures and curfews, are necessary. They also believe that their government and their country’s citizens are not doing enough and underestimate the degree to which others in their country support strong behavioral and policy responses to the pandemic. The perception of a weak government and public response is associated with higher levels of worries and depression. Using both cross-country panel data and an event-study, we additionally show that strong government reactions correct misperceptions, and reduce worries and depression. Our findings highlight that policy-makers not only need to consider how their decisions affect the spread of COVID-19, but also how such choices influence the mental health of their population.

This note seeks the socioeconomic roots of racial disparities in COVID-19 mortality, using county-level mortality, economic, and demographic data from 3,140 counties. For all minorities, the minority's population share is strongly correlated with total COVID-19 deaths. For Hispanic/Latino and Asian minorities those correlations are fragile, and largely disappear when we control for education, occupation, and commuting patterns. For African Americans and First Nations populations, the correlations are very robust. Surprisingly, for these two groups the racial disparity does not seem to be due to differences in income, poverty rates, education, occupational mix, or even access to healthcare insurance. A significant portion of the disparity can, however, be sourced to the use of public transit.

Reductions in ambient pollution have been taken as an indisputable "silver lining" to the COVID-19 Pandemic. Indeed, worldwide economic contraction induced by COVID-19 lockdowns should generate global air quality improvements ceteris paribus, including to China's notoriously-poor air quality. We analyze China's official pollution monitor data and account for the large, recurrent improvement in air quality following Lunar New Year (LNY), which essentially coincided with lock-downs in 2020. With the important exception of NO2, China's air quality improvements in 2020 are smaller than we should expect near the pandemic's epicenter: Hubei province. Compared with LNY improvements experienced in 2018 and 2019 in Hubei, we see smaller improvements in SO2 while ozone concentrations increased in both relative and absolute terms (roughly doubling). Similar patterns are found for the six provinces neighboring Hubei. We conclude that COVID-19 had ambiguous impacts on China's pollution, with evidence of relative deterioration in air quality near the Pandemic's epicenter.

We analyze the correlates of COVID-19 cases and deaths across US counties. We consider a wide range of correlates - population density, public transportation, age structure, nursing home residents, connectedness to source countries, etc. - finding that these variables are important predictors of variation in disease severity. Many of the effects are persistent - even increasing - through time. We also show that there are fewer deaths and cases in counties where Donald Trump received a high share of the vote in 2016, partly explaining the emerging political divide over lockdown and reopening policies, but that this correlation is reversed when controlling for shares of minority groups. The patterns we identify are meant to improve our understanding of the drivers of the spread of COVID-19, with an eye toward helping policymakers design responses that are sensitive to the specificities of different locations.

Drawing on firm-level expectations at a one-year forecast horizon in the Survey of Business Uncertainty (SBU), we construct novel, forward-looking reallocation measures for jobs and sales. These measures rise sharply after February 2020, reaching rates in April that are 2.4 (3.9) times the pre-COVID average for jobs (sales). We also draw on special questions in the April SBU to quantify the near-term impact of the COVID-19 shock on business staffing. We find 3 new hires for every 10 layoffs caused by the shock and estimate that 42 percent of recent layoffs will result in permanent job loss. Our survey evidence aligns well with anecdotal evidence of large pandemic-induced demand increases at some firms, with contemporaneous evidence on gross business formation, and with a sharp pandemic-induced rise in equity return dispersion across firms. After developing the evidence, we consider implications of our evidence for the economic outlook and for policy responses to the pandemic. Unemployment benefit levels that exceed worker earnings, policies that subsidize employee retention, occupational licensing restrictions, and regulatory barriers to business formation will impede reallocation responses to the COVID-19 shock.

How quickly will American businesses reopen after COVID-19 lockdowns end? We use a nationwide survey of small businesses to measure firms’ expectations about their re-opening and future demand. A plurality of firms in our sample expect to reopen within days of the end of legal restrictions, but a sizable minority expect to delay their reopening. While health-related variables, such as COVID-19 case rates and physical proximity of workers, do explain the prevalence and expected duration of regulated lockdown, these variables have little or no correlation with post-lockdown reopening intentions. Instead, almost one half of closed or partially open businesses said that their reopening would depend on the reopening of related businesses, including customers and suppliers. Owners expect demand to be one-third lower than before the crisis through autumn. Firms with more pessimistic expectations about demand predict a later reopening. Using an instrumental variables strategy, we estimate the relationship between demand expectations and reopening. These estimates suggest that post-lockdown delays in reopening can be explained by low levels of expected demand.

In the decade following the financial crisis of 2008, investment funds in corporate bond markets became prominent market players and generated concerns of financial fragility. The COVID-19 crisis provides an opportunity to inspect their resilience in a major stress event. Using daily microdata, we document major outflows in these funds during this period, far greater than anything they experienced in past events. Large outflows were sustained over several weeks and were widespread across funds. Inspecting the role of sources of fragility, we show that both the illiquidity of fund assets and the vulnerability to fire sales were important factors in explaining outflows in this episode. The exposure to sectors most hurt by the COVID-19 crisis was also important. Two policy announcements by the Federal Reserve about extraordinary direct interventions in corporate-bond markets seem to have played an important role in calming down the panic and reversing the outflows.

This paper provides the first nationally representative estimates of vulnerability to severe com-plications from COVID-19 overall and across race-ethnicity and socioeconomic status. We use the Panel Study of Income Dynamics (PSID) to examine the prevalence of specific health condi-tions associated with complications from COVID-19 and to calculate, for each individual, an index of the risk of severe complications from respiratory infections developed by DeCaprio et al. (2020). We show large disparities across race-ethnicity and socioeconomic status in the prev-alence of conditions which are associated with the risk of severe complications from COVID-19. Moreover, we show that these disparities emerge early in life, prior to age 65, leading to higher vulnerability to such complications. While vulnerability is highest among older adults regardless of their race-ethnicity or socioeconomic status, our results suggest particular attention should also be given to the risk of adverse outcomes in midlife for non-Hispanic Blacks, adults with a high school degree or less, and low-income Americans.

Big data on job-vacancy postings reveal several dimensions of the impact of COVID-19 on the U.S. job market. Firms have cut back on postings for high-skill jobs more than for low-skill jobs, with small firms nearly halting their new hiring altogether. New-hiring cuts and downskilling are most pronounced in local labor markets lacking depth (where employment is concentrated within a few firms), in low-income areas, and in areas with greater income inequality. Cuts are deeper in industries where workers are more unionized and in the non-tradable sector. Access to finance modulates corporate hiring, with credit-constrained firms curtailing their job postings the most. Our study shows how the early-2020 global pandemic is shaping the dynamics of hiring, identifying the firms, jobs, places, industries, and labor markets most affected by it. Our results point to important challenges to the scale and speed of a recovery.

Key factors in modeling a pandemic and guiding policy-making include mortality rates associated with infections; the ability of government policies, medical systems, and society to adapt to the changing dynamics of a pandemic; and institutional and demographic characteristics affecting citizens’ perceptions and behavioral responses to stringent policies. This paper traces the cross-country associations between COVID-19 mortality, policy interventions aimed at limiting social contact, and their interactions with institutional and demographic characteristics. We document that, with a lag, more stringent pandemic policies were associated with lower mortality growth rates. The association between stricter pandemic policies and lower future mortality growth is more pronounced in countries with a greater proportion of the elderly population and urban population, greater democratic freedoms, and larger international travel flows. Countries with greater policy stringency in place prior to the first death realized lower peak mortality rates and exhibited lower durations to the first mortality peak. In contrast, countries with higher initial mobility saw higher peak mortality rates in the first phase of the pandemic, and countries with a larger elderly population, a greater share of employees in vulnerable occupations, and a higher level of democracy took longer to reach their peak mortalities. Our results suggest that policy interventions are effective at slowing the geometric pattern of mortality growth, reducing the peak mortality, and shortening the duration to the first peak. We also shed light on the importance of institutional and demographic characteristics in guiding policy-making for future waves of the pandemic.

COVID-19 may accelerate the automation of jobs, as employers invest in technology to adapt the production process to safeguard against current and potential future pandemics. We identify occupations that have high automation potential and also exhibit a high degree of risk of viral infection. We then examine regional variation in terms of which local labor markets are most at risk. Next we outline the differential impact that COVID-19 may have on automatable jobs for different demographic groups. We find that occupations held by mid-educated females are at highest risk, notably including some healthcare, office and administrative support, and protective service occupations.

We build a publicly available platform that tracks economic activity at a granular level in real time using anonymized data from private companies. We report daily statistics on consumer spending, business revenues, employment rates, and other key indicators disaggregated by county, industry, and income group. Using these data, we study the mechanisms through which COVID-19 affected the economy by analyzing heterogeneity in its impacts across geographic areas and income groups. We first show that high-income individuals reduced spending sharply in mid-March 2020, particularly in areas with high rates of COVID-19 infection and in sectors that require physical interaction. This reduction in spending greatly reduced the revenues of businesses that cater to high-income households in person, notably small businesses in affluent ZIP codes. These businesses laid off most of their low-income employees, leading to a surge in unemployment claims in affluent areas. Building on this diagnostic analysis, we use event study designs to estimate the causal effects of policies aimed at mitigating the adverse impacts of COVID. State-ordered reopenings of economies have little impact on local employment. Stimulus payments to low-income households increased consumer spending sharply, but had modest impacts on employment in the short run, perhaps because very little of the increased spending flowed to businesses most affected by the COVID-19 shock. Paycheck Protection Program loans have also had little impact on employment at small businesses. These results suggest that traditional macroeconomic tools – stimulating aggregate demand or providing liquidity to businesses – may have diminished capacity to restore employment when consumer spending is constrained by health concerns. During a pandemic, it may be more fruitful to mitigate economic hardship through social insurance. More broadly, this analysis illustrates how real-time economic tracking using private sector data can help rapidly identify the origins of economic crises and facilitate ongoing evaluation of policy impacts.

We report the results of a nationally-representative sample of the US population during the COVID-19 pandemic. The survey ran in two waves from April 1-5, 2020 and May 2-8, 2020. Of those employed pre-COVID-19, we find that about half are now working from home, including 35.2% who report they were commuting and recently switched to working from home. In addition, 10.1% report being laid-off or furloughed since the start of COVID-19. There is a strong negative relationship between the fraction in a state still commuting to work and the fraction working from home. We find that the share of people switching to remote work can be predicted by the incidence of COVID-19 and that younger people were more likely to switch to remote work. Furthermore, states with a higher share of employment in information work including management, professional and related occupations were more likely to shift toward working from home and had fewer people laid off or furloughed. We find no substantial change in results between the two waves, suggesting that most changes to remote work manifested by early April.

One of the most common policy prescriptions to reduce the spread of COVID-19 has been to legally enforce social distancing through state or local shelter-in-place orders (SIPOs). This paper is the first to explore the comparative effectiveness of early county-level SIPOs versus later statewide mandates in curbing COVID-19 growth. We exploit the unique laboratory of Texas, a state in which the early adoption of local SIPOs by densely populated counties covered almost two-thirds of the state’s population prior to Texas’s adoption of a statewide SIPO on April 2, 2020. Using an event study framework, we document that countywide SIPO adoption is associated with a 14 percent increase in the percent of residents who remain at home full-time, a social distancing effect that is largest in urbanized and densely populated counties. Then, we find that in early adopting counties, COVID-19 case growth fell by 19 to 26 percentage points two-and-a-half weeks following adoption of a SIPO, a result robust to controls for county-level heterogeneity in outbreak timing, coronavirus testing, and border SIPO policies. This effect is driven nearly entirely by highly urbanized and densely populated counties. We find that approximately 90 percent of the curbed growth in COVID-19 cases in Texas came from the early adoption of SIPOs by urbanized counties, suggesting that the later statewide shelter-in-place mandate yielded relatively few health benefits.

Using tools described in our earlier work (Hassan et al., 2019, 2020), we develop text-based measures of the costs, benefits, and risks listed firms in the US and over 80 other countries associate with the spread of Covid-19 and other epidemic diseases. We identify which firms expect to gain or lose from an epidemic disease and which are most affected by the associated uncertainty as a disease spreads in a region or around the world. As Covid-19 spread globally in the first quarter of 2020, firms' primary concerns relate to the collapse of demand, increased uncertainty, and disruption in supply chains. Other important concerns relate to capacity reductions, closures, and employee welfare. Financing concerns were mentioned relatively rarely in the first quarter but appear to become a more important concern in the second quarter. We also identify some firms that foresee opportunities in new or disrupted markets due to the spread of the disease. Finally, we find some evidence that firms that have experience with SARS or H1N1 have more positive expectations about their ability to deal with the coronavirus outbreak.

We study the impact of non-pharmaceutical policy interventions (NPIs) like “stay-at-home” orders on the spread of infectious disease. Local policies have little impact on the economy nor on local public health. Stay-at-home is only weakly associated with slower growth of Covid-19 cases. Reductions in observed “mobility” are not associated with slower growth of Covid-19 cases. Stay-at-home is associated with lower workplace and more residential activity, but common shocks matter much more. Moreover, job losses have been no higher in US states that implemented stay-at-home during the Covid-19 pandemic than in states that did not have stay-at-home. All of these results demonstrate that the Covid-19 pandemic is a common economic and public health shock. They also show that policy spillovers and behavioral responses are important. The tradeoff between the economy and public health in a pandemic depends strongly on what is happening elsewhere. This underscores the importance of coordinated economic and public health responses.

There is growing concern that the COVID-19 pandemic may have severe, adverse effects on the health care sector, a sector of the economy that historically has been somewhat shielded from the business cycle. In this paper, we study one aspect of this issue by estimating the magnitude of the COVID-19 pandemic on use of outpatient health services. We use 2010-2020 data from the Outpatient Influenza-like Illness Surveillance Network (ILINet). Our findings indicate that the COVID-19 pandemic is associated with about a 67 percent decline in the total number of outpatient visits per provider by the week of April 12-18th, 2020 relative to the same week in prior years. Effects become apparent earlier in the pandemic for outpatient visits for non-flu symptoms, but we find negative effects on outpatient visits for flu symptoms as well.

Little is known about individual beliefs concerning the Coronavirus Disease 2019 (COVID-19). Still less is known about how these beliefs influence the spread of the virus by determining social distancing behaviors. To shed light on these questions, we conduct an online experiment (n = 3,610) with participants in the US and UK. Participants are randomly allocated to a control group, or one of two treatment groups. The treatment groups are shown upper- or lower-bound expert estimates of the infectiousness of the virus. We present three main empirical findings. First, individuals dramatically overestimate the infectiousness of COVID-19 relative to expert opinion. Second, providing people with expert information partially corrects their beliefs about the virus. Third, the more infectious people believe that COVID-19 is, the less willing they are to take social distancing measures, a finding we dub the “fatalism effect”. We estimate that small changes in people's beliefs can generate billions of dollars in mortality benefits. Finally, we develop a theoretical model that can explain the fatalism effect.

The threat of COVID-19 has increased the health risks of going to an office or factory, leading more workers to do their jobs remotely. In this paper, we provide results from firm surveys on both small and large businesses on the prevalence and productivity of remote work, and expectations about the persistence of remote work once the COVID-19 crisis ends. We present four main findings. First, while overall levels of remote work are high, there is considerable variation across industries. The Dingel and Neiman (2020) measure of suitability for remote work does a remarkably good job of predicting the industry level patterns of remote work - highlighting the challenge of moving many industries to remote work. Second, remote work is much more common in industries with better educated and better paid workers. Third, in our larger survey, employers think that there has been less productivity loss from remote working in better educated and higher paid industries. Fourth, more than one-third of firms that had employees switch to remote work believe that remote work will remain more common at their company even after the COVID-19 crisis ends.

We summarize some of the early effects and discuss possible future effects of the COVID-19 pandemic and recession on the employment outcomes of older workers in the United States. We start by discussing what we know about how older workers faired in prior recessions in the United States and how COVID-19 and this recession may differ. We then estimate some early effects of the COVID-19 pandemic and recession on employment and unemployment rates by age group and sex using Current Population Survey data. We calculate employment and unemployment rates multiple ways to account for the complicated employment situation and possible errors in survey enumeration. We find that while previous recessions, in some ways, did not affect employment outcomes for older workers as much, this recession disproportionately affected older workers of ages 65 and older. For example, we find that unemployment rates in April 2020 increased to 15.43% for those ages 65 and older, compared to 12.99% for those ages 25-44. We also find that COVID-19 and the recession disproportionately affected women, where women have reached higher unemployment rates than men, which was consistent for all age groups and unemployment rate measures we used.

We make several contributions to understanding how the COVID-19 epidemic and policy responses have affected U.S. labor markets, benchmarked against two previous recessions. First, monthly Current Population Survey (CPS) data show greater declines in employment in April 2020 (relative to February) for Hispanics, workers aged 20 to 24, and those with high school degrees and some college. Second, we show that job loss was larger in occupations that require more interpersonal contact and that cannot be performed remotely. Pre-epidemic sorting into occupations with more potential for remote work and industries that are currently essential explain a large share of gaps in recent unemployment for key racial, ethnic, age, and education sub-populations. However, there is a larger unexplained component to the gender employment gaps. We also address measurement issues known to have affected the March and April 2020 CPS. In particular, non-response increased dramatically, especially among the incoming rotation groups. Some of the increase appears non-random, but is not likely to be driving our conclusions. We also demonstrate the importance of tracking workers who report having a job but being absent, in addition to tracking employed and unemployed workers. We conclude with a discussion of policy priorities implied by the disparities in labor market losses from the COVID-19 crisis that we identify.

During health crises, like COVID-19, individuals are inundated with messages promoting health-preserving behavior. Does additional light-touch messaging by a credible individual change behavior? Do the features of the message matter? To answer this, we conducted a large-scale messaging campaign in West Bengal, India. Twenty-five million individuals were sent an SMS containing a 2.5-minute clip, delivered by West Bengal native and 2019 Nobel laureate Abhijit Banerjee. All messages encouraged reporting symptoms to the local public health worker. In addition, each message emphasizes one health-preserving behavior (distancing or hygiene) and one motivation for action (effects on everyone or just on self). Further, some messages addressed concerns about ostracism of the infected. Messages were randomized at the PIN code level. As control, three million individuals received a message pointing them to government information. The campaign (i) doubled the reporting of health symptoms to the community health workers (p = 0.001 for fever, p = 0.024 for respiratory symptoms); (ii) decreased travel beyond one’s village in the last two days by 20% (p = 0.026) (on a basis of 37% in control) and increased estimated hand-washing when returning home by 7% (p = 0.044) (67.5% in control); (iii) spilled over to behaviors not mentioned in the message – mask-wearing was never mentioned but increased 2% (p = 0.042), while distancing and hygiene both increased in the sample where they were not mentioned by similar amounts as where they were mentioned; (iv) spilled over onto nonrecipients within the same community, with effects similar to those for individuals who received the messages.

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We use the synthetic control method to analyze the effect of face masks on the spread of Covid-19 in Germany. Our identification approach exploits regional variation in the point in time when face masks became compulsory. Depending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory. Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%.

We use a repeated large-scale survey of households in the Nielsen Homescan panel to characterize how labor markets are being affected by the covid-19 pandemic. We document several facts. First, job loss has been significantly larger than implied by new unemployment claims: we estimate 20 million lost jobs by April 8th, far more than jobs lost over the entire Great Recession. Second, many of those losing jobs are not actively looking to find new ones. As a result, we estimate the rise in the unemployment rate over the corresponding period to be surprisingly small, only about 2 percentage points. Third, participation in the labor force has declined by 7 percentage points, an unparalleled fall that dwarfs the three percentage point cumulative decline that occurred from 2008 to 2016. Early retirement almost fully explains the drop in labor force participation both for those survey participants previously employed and those previously looking for work.

Look beyond gender — if research thrives on collaboration, a book asks, why do we reward individualism?