In SIR models, homogeneous or with a network structure, infection rates are assumed to be exogenous. However, individuals adjust their behavior. Using daily data for 89 cities worldwide, we document that mobility falls in response to fear, as approximated by Google search terms. Combining these data with experimentally validated measures of social preferences at the regional level, we find that stringency measures matter less if individuals are more patient and altruistic preference traits, and exhibit less negative reciprocity community traits. We modify the homogeneous SIR and the SIR-network model to include agents' optimizing decisions on social interactions. Susceptible individuals internalize infection risk based on their patience, infected ones do so based on their altruism, and reciprocity matters for internalizing risk in SIR networks. A planner further restricts interactions due to a static and a dynamic inefficiency in the homogeneous SIR model, and due to an additional reciprocity inefficiency in the SIR-network model. We show that partial or targeted lockdown policies are efficient only when it is possible to identify infected individuals.

Social Interactions in Pandemics: Fear, Altruism, and Reciprocity

10.3386/w26951

Rationing of medical resources is a critical issue in the COVID-19 pandemic. Most existing triage protocols are based on a priority point system, in which a formula specifies the order in which the supply of a resource, such as a ventilator, is to be rationed for patients. A priority point system generates an identical priority ranking specifying claims on all units. Triage protocols in some states (e.g. Michigan) prioritize frontline health workers giving heavier weight to the ethical principle of instrumental value. Others (e.g. New York) do not, reasoning that if frontline workers obtain high enough priority, there is a risk that they obtain all units and none remain for the general community. This debate is pressing given substantial COVID-19 health risks for frontline workers. In this paper, we analyze the consequences of rationing medical resources through a reserve system. In a reserve system, resources are placed into multiple categories. Priorities guiding allocation of units can reflect different ethical values between these categories. A reserve system provides additional flexibility over a priority point system because it does not dictate a single priority order for the allocation of all units. It offers a middle-ground approach that balances competing objectives, such as in the medical worker debate. This flexibility requires attention to implementation, especially the processing order of reserve categories. We describe our model of a reserve system, characterize its potential outcomes, and examine distributional implications of particular reserve systems. We also discuss several practical considerations with triage protocol design.

Leaving No Ethical Value Behind: Triage Protocol Design for Pandemic Rationing

10.3386/w27021

New York City’s multipronged subway system was a major disseminator – if not the principal transmission vehicle – of coronavirus infection during the initial takeoff of the massive epidemic that became evident throughout the city during March 2020. The near shutoff of subway ridership in Manhattan – down by over 90 percent at the end of March – correlates strongly with the substantial increase in the doubling time of new cases in this borough. Subway lines with the largest drop in ridership during the second and third weeks of March had the lowest subsequent rates of infection in the zip codes traversed by their routes. Maps of subway station turnstile entries, superimposed upon zip code-level maps of reported coronavirus incidence, are strongly consistent with subway-facilitated disease propagation. Reciprocal seeding of infection appears to be the best explanation for the emergence of a single hotspot in Midtown West in Manhattan.

The Subways Seeded the Massive Coronavirus Epidemic in New York City

10.3386/w27224

We study the role of global supply chains in the impact of the Covid-19 pandemic on GDP growth for 64 countries. We discipline the labor supply shock across sectors and countries using the fraction of work in the sector that can be done from home, interacted with the stringency with which countries imposed lockdown measures. Using the quantitative framework and methods developed in Huo, Levchenko, and Pandalai-Nayar (2020), we show that the average real GDP downturn due to the Covid-19 shock is expected to be - 29:6%, with one quarter of the total due to transmission through global supply chains. However, "renationalization" of global supply chains does not in general make countries more resilient to pandemic-induced contractions in labor supply. The average GDP drop would have been - 30:2% in a world without trade in inputs and final goods. This is because eliminating reliance on foreign inputs increases reliance on the domestic inputs, which are also disrupted due to nationwide lockdowns. In fact, trade can insulate a country imposing a stringent lockdown from the pandemic-shock, as its foreign inputs are less disrupted than its domestic ones. Finally, unilateral lifting of the lockdowns in the largest economies can contribute as much as 2.5% to GDP growth in some of their smaller trade partners.

Global Supply Chains in the Pandemic

10.3386/w27123

We examine household wealth across birth cohorts and over time using data from the Survey of Consumer Finances. We show that although the Great Recession reduced wealth in every age group, longer-term trends indicate that the wealth of older age groups has increased while the wealth of younger age groups has declined. A substantial share of these changes, in both directions, can be explained by changes in household demographic and economic characteristics. As for the millennial generation, their median wealth in 2016 was lower than the wealth of any similarly aged cohort between 1989 and 2007. Millennials will have several advantages in wealth accumulation relative to previous generations, such as more education and longer working lives, but also several disadvantages, including weak prospects for economic growth and delays in home purchase and marriage. The millennial generation contains a significantly higher percentage of minorities than previous generations. We estimate that minority households have tended to accumulate less wealth than whites in the past, controlling for household characteristics, and the difference appears to be growing over time for Blacks relative to whites. These results apply to the period before the COVID-19 pandemic and are best interpreted as addressing generational wealth patterns through 2016 and providing a pre-COVID benchmark against which future studies can be compared.

The Wealth of Generations, With Special Attention to the Millennials

10.3386/w26934

What are the medium- to long-term effects of pandemics? How do they differ from other economic disasters? We study major pandemics using the rates of return on assets stretching back to the 14th century. Significant macroeconomic after-effects of pandemics persist for about decades, with real rates of return substantially depressed, in stark contrast to what happens after wars. Our findings are consistent with the neoclassical growth model: capital is destroyed in wars, but not in pandemics; pandemics instead may induce relative labor scarcity and/or a shift to greater precautionary savings.

Longer-run Economic Consequences of Pandemics

10.3386/w27047

In this paper we argue that endogenous shifts in private consumption behavior across sectors of the economy can act as a potent mitigation mechanism during an epidemic or when the economy is re-opened after a temporary lockdown. Extending the theoretical framework proposed by Eichenbaum-Rebelo-Trabandt (2020), we distinguish goods by their degree to which they can be consumed at home rather than in a social (and thus possibly contagious) context. We demonstrate that, within the model the "Swedish solution" of letting the epidemic play out without government intervention and allowing agents to shift their sectoral behavior on their own can lead to a substantial mitigation of the economic and human costs of the COVID-19 crisis, avoiding more than 80 of the decline in output and of number of deaths within one year, compared to a model in which sectors are assumed to be homogeneous. For different parameter configurations that capture the additional social distancing and hygiene activities individuals might engage in voluntarily, we show that infections may decline entirely on their own, simply due to the individually rational re-allocation of economic activity: the curve not only just flattens, it gets reversed.

Macroeconomic Dynamics and Reallocation in an Epidemic

10.3386/w27457

Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test. This increases testing efficiency, such that increasing frequency by x times only increases expected tests by around √x rather than x. However, this calculation omits a further benefit of frequent testing: infected people are quickly removed from the population, which lowers prevalence and generates further efficiency. Accounting for this decline in intra-group spread, we show that increasing frequency can paradoxically reduce the total testing cost. Second, we show that group size and efficiency increases with intra-group risk correlation, which is expected in natural test groupings based on proximity. Third, because optimal groupings depend on uncertain risk and correlation, we show how better estimates from machine learning can drive large efficiency gains. We conclude that frequent group testing, aided by machine learning, is a promising and inexpensive surveillance strategy.

Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning

10.3386/w26901

We extend the baseline Susceptible-Exposed-Infectious-Recovered (SEIR) infectious disease epidemiology model to understand the role of testing and case-dependent quarantine. Our model nests the SEIR model. During a period of asymptomatic infection, testing can reveal infection that otherwise would only be revealed later when symptoms develop. Along with those displaying symptoms, such individuals are deemed known positive cases. Quarantine policy is case-dependent in that it can depend on whether a case is unknown, known positive, known negative, or recovered. Testing therefore makes possible the identification and quarantine of infected individuals and release of non-infected individuals. We fix a quarantine technology—a parameter determining the differential rate of transmission in quarantine—and compare simple testing and quarantine policies. We start with a baseline quarantine-only policy that replicates the rate at which individuals are entering quarantine in the US in March, 2020. We show that the total deaths that occur under this policy can occur under looser quarantine measures and a substantial increase in random testing of asymptomatic individuals. Testing at a higher rate in conjunction with targeted quarantine policies can (i) dampen the economic impact of the coronavirus and (ii) reduce peak symptomatic infections—relevant for hospital capacity constraints. Our model can be plugged into richer quantitative extensions of the SEIR model of the kind currently being used to forecast the effects of public health and economic policies.

An SEIR Infectious Disease Model with Testing and Conditional Quarantine

10.3386/w27051

Hoarding of staples has long worried policymakers due to concerns about shortages. We quantify how sticky store prices---delayed price adjustment to shocks by reputable retailers---exacerbate hoarding. When prices are sticky, households hoard not only for precautionary motives but also non-precautionary motives: they stockpile as they would during a standard retail promotion or for the purpose of retail arbitrage. Using US supermarket scanner data covering the 2008 Global Rice Crisis, an episode driven by an observable cost shock due an Indian ban on raw rice exports, we find that sticky prices account for a sizeable fraction of hoarding. Hoarding is mostly for own use and more prevalent among richer households. Our findings are consistent with media reports of distributional concerns associated with hoarding during the Covid-19 Pandemic.

A Sticky-Price View of Hoarding

10.3386/w27104

Epidemiology models used in macroeconomics generally assume that people know their current health status. In this paper, we consider a more realistic environment in which people are uncertain about their health status. We use our model to study the impact of testing with and without quarantining infected people. We find that testing without quarantines can worsen the economic and health repercussions of an epidemic. In contrast, a policy that uses tests to quarantine infected people has very large social benefits. Critically, this policy ameliorates the sharp tradeoff between declines in economic activity and health outcomes that is associated with broad-based containment policies like lockdowns. This amelioration is particularly dramatic when people who recover from an infection acquire only temporary immunity to the virus.

The Macroeconomics of Testing and Quarantining

10.3386/w27218

The reproduction number R0 plays an outsized role in COVID-19 risk management. But it is an insufficient statistic, particularly for financial risks, because transmissions are stochastic due to environmental factors. We introduce aggregate transmission shocks into a widely-used epidemic model and link firm valuation to epidemic data by using an asset-pricing framework that accounts for potential vaccines. Pooling early data, we estimate a large R0 and transmission volatility. R0 mismeasures social-distancing benefits because it gives a poor approximation of conditional infection forecasts. R0 mismeasures financial risks since transmission volatility and vaccine news also determine firm-value damage.

Implications of Stochastic Transmission Rates for Managing Pandemic Risks

10.3386/w27612

We explore the impact of COVID-19 on employee's digital communication patterns through an event study of lockdowns in 16 large metropolitan areas in North America, Europe and the Middle East. Using de- identified, aggregated meeting and email meta-data from 3,143,270 users, we find, compared to pre- pandemic levels, increases in the number of meetings per person (+12.9 percent) and the number of attendees per meeting (+13.5 percent), but decreases in the average length of meetings (-20.1 percent). Collectively, the net effect is that people spent less time in meetings per day (-11.5 percent) in the post- lockdown period. We also find significant and durable increases in length of the average workday (+8.2 percent, or +48.5 minutes), along with short-term increases in email activity. These findings provide insight from a novel dataset into how the nature of work has changed for a large sample of knowledge workers. We discuss these changes in light of the ongoing challenges faced by organizations and workers struggling to adapt and perform in the face of a global pandemic.

Collaborating During Coronavirus: The Impact of COVID-19 on the Nature of Work

10.3386/w27532

Managing the outbreak of COVID-19 in India constitutes an unprecedented health emergency in one of the largest and most diverse nations in the world. On May 4, 2020, India started the process of releasing its population from a national lockdown during which extreme social distancing was implemented. We describe and simulate an adaptive control approach to exit this situation, while maintaining the epidemic under control. Adaptive control is a flexible counter-cyclical policy approach, whereby different areas release from lockdown in potentially different gradual ways, dependent on the local progression of the dis- ease. Because of these features, adaptive control requires the ability to decrease or increase social distancing in response to observed and projected dynamics of the disease outbreak. We show via simulation of a stochastic Susceptible-Infected-Recovered (SIR) model and of a synthetic intervention (SI) model that adaptive control performs at least as well as immediate and full release from lockdown starting May 4 and as full release from lockdown after a month (i.e., after May 31). The key insight is that adaptive response provides the option to increase or decrease socioeconomic activity depending on how it affects disease progression and this freedom allows it to do at least as well as most other policy alternatives. We also discuss the central challenge to any nuanced release policy, including adaptive control, specifically learning how specific policies translate into changes in contact rates and thus COVID-19's reproductive rate in real time.

Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown

10.3386/w27235

This study quantifies the effect of state reopening policies on daily mobility, travel, and mixing behavior during the COVID-19 pandemic. We harness cell device signal data to examine the effects of the timing and pace of reopening plans in different states. We quantify the increase in mobility patterns during the reopening phase by a broad range of cell-device-based metrics. Soon (four days) after reopening, we observe a 6% to 8% mobility increase. In addition, we find that temperature and precipitation are strongly associated with increased mobility across counties. The mobility measures that reflect visits to a greater variety of locations responds the most to reopening policies, while total time in vs. outside the house remains unchanged. The largest increases in mobility occur in states that were late adopters of closure measures, suggesting that closure policies may have represented more of a binding constraint in those states. Together, these four observations provide an assessment of the extent to which people in the U.S. are resuming movement and physical proximity as the COVID-19 pandemic continues.

Impacts of State Reopening Policy on Human Mobility

10.3386/w26949

We explore how household consumption responds to epidemics, utilizing transaction-level household financial data to investigate the impact of the COVID-19 virus. As the number of cases grew, households began to radically alter their typical spending across a number of major categories. Initially spending increased sharply, particularly in retail, credit card spending and food items. This was followed by a sharp decrease in overall spending. Households responded most strongly in states with shelter-in-place orders in place by March 29th. We explore heterogeneity across partisan affiliation, demographics and income. Greater levels of social distancing are associated with drops in spending, particularly in restaurants and retail.

How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic

10.3386/w27277

Voluntary social distancing plays a vital role in containing the spread of the disease during a pandemic. As a public good, it should be more commonplace in more homogeneous and altruistic societies. However, for healthy people, observing social distancing has private benefits, too. If sick individuals are more likely to stay home, healthy ones have fewer incentives to do so, especially if the asymptomatic transmission is perceived to be unlikely. Theoretically, we show that this interplay may lead to a stricter observance of social distancing in more diverse and less altruistic societies. Empirically, we find that, consistent with the model, the reduction in mobility following the first local case of COVID-19 was stronger in Russian cities with higher ethnic fractionalization and cities with higher levels of xenophobia. For identification, we predict the timing of the first case using pre-existing patterns of internal migration to Moscow. Using SafeGraph data on mobility patterns, we confirm that mobility reduction in the United States was also higher in counties with higher ethnic fractionalization. Our findings highlight the importance of strategic incentives of different population groups for the effectiveness of public policy.

Divided We Stay Home: Social Distancing and Ethnic Diversity

10.3386/w27009

We analyze the externalities that arise when social and economic interactions transmit infectious diseases such as COVID-19. Individually rational agents do not internalize that they impose infection externalities upon others when the disease is transmitted. In an SIR model calibrated to capture the main features of COVID-19 in the US economy, we show that private agents perceive the cost an additional infection to be around $80k whereas the social cost including infection externalities is more than three times higher, around $286k. This misvaluation has stark implications for how society ultimately overcomes the disease: for a population of individually rational agents, the precautionary behavior by the susceptible flattens the curve of infections, but the disease is not overcome until herd immunity is acquired. The resulting economic cost is high; an initial sharp decline in aggregate output followed by a slow recovery over several years. By contrast, the socially optimal approach in our model focuses public policy measures on the infected in order to contain the disease and quickly eradicate it, which produces a much milder recession. If targeting the infected is impossible, the optimal policy in our model is still to aggressively contain and eliminate the disease, and the social cost of an extra infection rises to $586k.

Covid-19 Infection Externalities: Trading Off Lives vs. Livelihoods

10.3386/w26917

New York City has been rightly characterized as the epicenter of the coronavirus pandemic in the United States. Just one month after the first cases of coronavirus infection were reported in the city, the burden of infected individuals with serious complications of COVID-19 has already outstripped the capacity of many of the city’s hospitals. As in the case of most pandemics, scientists and public officials don’t have complete, accurate, real-time data on the path of new infections. Despite these data inadequacies, there already appears to be sufficient evidence to conclude that the curve in New York City is indeed flattening. The purpose of this report is to set forth the evidence for – and against – this preliminary but potentially important conclusion. Having examined the evidence, we then inquire: if the curve is indeed flattening, do we know what caused to it to level off?

The Coronavirus Epidemic Curve is Already Flattening in New York City

10.3386/w27261

In the wake of the global pandemic known as COVID-19, retirees, along with those hoping to retire someday, have been shocked into a new awareness of the need for better risk management tools to handle longevity and aging. This paper offers an assessment of the status quo prior to the spread of the coronavirus, evaluates how retirement systems are faring in the wake of the shock. Next we examine insurance and financial market products that may render retirement systems more resilient for the world’s aging population. Finally, potential roles for policymakers are evaluated.

Building Better Retirement Systems in the Wake of the Global Pandemic

10.3386/w27243

Employment rates in the United States fell dramatically between February 2020 and April 2020 as the repercussions of the COVID-19 pandemic reverberated through the labor market. This paper uses data from the CPS Basic Monthly Files to document that the employment decline was particularly severe for immigrants. Historically, immigrant men were more likely to be employed than native men. The COVID-related labor market disruptions eliminated the immigrant employment advantage. By April 2020, immigrant men had lower employment rates than native men. Part of the relative increase in the immigrant rate of job loss arises because immigrants were less likely to work in jobs that could be performed remotely and suffered disparate employment consequences as the lockdown permitted workers with more “remotable” skills to continue their work from home. Undocumented men were particularly hard hit by the pandemic, with their rate of job loss far exceeding the rate of job loss of legal immigrants.

The Adverse Effect of the COVID-19 Labor Market Shock on Immigrant Employment

10.3386/w27124

Social distancing via shelter-in-place strategies has emerged as the most effective way to combat Covid-19. In the United States, choices about such policies are made by individual states. Here we show that the policy choice made by one state influences the incentives that other states face to adopt similar policies: they can be viewed as strategic complements in a supermodular game. If they satisfy the condition of uniform strict increasing differences then following Heal and Kunreuther ([6]) we show that if enough states engage in social distancing, they will tip others to do the same and thus shift the Nash equilibrium with respect to the number of states engaging in social distancing.

Covid-19, Shelter-In Place Strategies and Tipping

10.3386/w27293

As the COVID-19 pandemic progresses, researchers are reporting findings of randomized trials comparing standard care with care augmented by experimental drugs. The trials have small sample sizes, so estimates of treatment effects are imprecise. Seeing imprecision, clinicians reading research articles may find it difficult to decide when to treat patients with experimental drugs. Whatever decision criterion one uses, there is always some probability that random variation in trial outcomes will lead to prescribing sub-optimal treatments. A conventional practice when comparing standard care and an innovation is to choose the innovation only if the estimated treatment effect is positive and statistically significant. This practice defers to standard care as the status quo. To evaluate decision criteria, we use the concept of near optimality, which jointly considers the probability and magnitude of decision errors. An appealing decision criterion from this perspective is the empirical success rule, which chooses the treatment with the highest observed average patient outcome in the trial. Considering the design of recent and ongoing COVID-19 trials, we show that the empirical success rule yields treatment results that are much closer to optimal than those generated by prevailing decision criteria based on hypothesis tests.

Statistical Decision Properties of Imprecise Trials Assessing COVID-19 Drugs

10.3386/w27061

We use job vacancy data collected in real time by Burning Glass Technologies, as well as initial unemployment insurance (UI) claims data to study the impact of COVID-19 on the labor market. Our data allow us to track postings at disaggregated geography and by detailed occupation and industry. We find that job vacancies collapsed in the second half of March and are now 30% lower than their level at the beginning of the year. To a first approximation, this collapse was broad based, hitting all U.S. states, regardless of the intensity of the initial virus spread or timing of stay-at-home policies. UI claims also largely match these patterns. Nearly all industries and occupations saw contraction in postings and spikes in UI claims, regardless of whether they are deemed essential and whether they have work-from-home capability. The only major exceptions are in essential retail and nursing, the “front line” jobs most in-demand during the current crisis.

Labor Demand in the Time of COVID-19: Evidence from Vacancy Postings and UI Claims

10.3386/w27432

The collapse of economic activity in 2020 from COVID-19 has been immense. An important question is how much of that resulted from government restrictions on activity versus people voluntarily choosing to stay home to avoid infection. This paper examines the drivers of the collapse using cellular phone records data on customer visits to more than 2.25 million individual businesses across 110 different industries. Comparing consumer behavior within the same commuting zones but across boundaries with different policy regimes suggests that legal shutdown orders account for only a modest share of the decline of economic activity (and that having county-level policy data is significantly more accurate than state-level data). While overall consumer traffic fell by 60 percentage points, legal restrictions explain only 7 of that. Individual choices were far more important and seem tied to fears of infection. Traffic started dropping before the legal orders were in place; was highly tied to the number of COVID deaths in the county; and showed a clear shift by consumers away from larger/busier stores toward smaller/less busy ones in the same industry. States repealing their shutdown orders saw identically modest recoveries--symmetric going down and coming back. The shutdown orders did, however, have significantly reallocate consumer activity away from “nonessential” to “essential” businesses and from restaurants and bars toward groceries and other food sellers.

Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline 2020

10.3386/w27562

Violence against women is a problem worldwide, with economic costs ranging from 1-4% of global GDP. Using variation in the intensity of government-mandated lockdowns in India, we show that domestic violence complaints increase by 0.47 SD in districts with the strictest lockdown rules. We find similarly large increases in cybercrime complaints. Interestingly, rape and sexual assault complaints decrease 0.4 SD during the same period in districts with the strictest lockdowns, consistent with decreased female mobility in public spaces, public transport, and workplaces. Attitudes toward domestic violence play an important role in the reporting and incidence of domestic violence during the lockdown.

Unintended Consequences of Lockdowns: COVID-19 and the Shadow Pandemic

10.3386/w26906

We quantify the causal impact of human mobility restrictions, particularly the lockdown of the city of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ a set of difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. We find that the lockdown of Wuhan reduced inflow into Wuhan by 76.64%, outflows from Wuhan by 56.35%, and within-Wuhan movements by 54.15%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities, the epicenter of the 2019-nCoV outbreak, on the destination cities' new infection cases. We find, using simulations with these estimates, that the lockdown of the city of Wuhan on January 23, 2020 contributed significantly to reducing the total infection cases outside of Wuhan, even with the social distancing measures later imposed by other cities. We find that the COVID-19 cases would be 64.81% higher in the 347 Chinese cities outside Hubei province, and 52.64% higher in the 16 non-Wuhan cities inside Hubei, in the counterfactual world in which the city of Wuhan were not locked down from January 23, 2020. We also find that there were substantial undocumented infection cases in the early days of the 2019-nCoV outbreak in Wuhan and other cities of Hubei province, but over time, the gap between the officially reported cases and our estimated “actual” cases narrows significantly. We also find evidence that enhanced social distancing policies in the 63 Chinese cities outside Hubei province are effective in reducing the impact of population inflows from the epicenter cities in Hubei province on the spread of 2019-nCoV virus in the destination cities elsewhere.

Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China

10.3386/w27023

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.

Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

10.3386/w27418

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.

Economic Uncertainty Before and During the COVID-19 Pandemic

10.3386/w27608

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.

Nursing Home Staff Networks and COVID-19

10.3386/w27008

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.

Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic

10.3386/w26983

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.

COVID-Induced Economic Uncertainty

10.3386/w27555

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.

Inequality in Household Adaptation to Schooling Shocks: Covid-Induced Online Learning Engagement in Real Time

10.3386/w27498

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.

Electoral Concerns Reduce Restrictive Measures During the COVID-19 Pandemic

10.3386/w27207

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.

Can the Covid Bailouts Save the Economy?

10.3386/w27359

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.

Gender Differences in COVID-19 Related Attitudes and Behavior: Evidence from a Panel Survey in Eight OECD Countries

10.3386/w27462

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.

The Impact of COVID-19 on Small Business Owners: Continued Losses and the Partial Rebound in May 2020

10.3386/w26952

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.

Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods

10.3386/w27320

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.

Civic Capital and Social Distancing during the Covid-19 Pandemic

10.3386/w27082

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.

Global Behaviors and Perceptions at the Onset of the COVID-19 Pandemic

10.3386/w27407