This paper starts with a quick overview of results on the classic SIR model and variants allowing for heterogeneity in contact rates. It then notes several implications relevant to model calibrations and policy predictions. Calibrating the classic SIR model to data generated by a heterogeneous model can lead to forecasts that are biased in several ways and to understatement of the forecast uncertainty. Among the biases are that we may underestimate how quickly herd immunity might be reached, underestimate differences across regions, and have biased estimates of the impact of endogenous and policy-driven social distancing.
About Covid Economics
Covid Economics, Vetted and Real-Time Papers, launched at the end of March 2020, is a free online CEPR publication. It has been created to quickly disseminate fast-rising scholarly work on the Covid-19 epidemic. Alongside VoxEU, which presents short analyses on the epidemic and other economic issues, Covid Economics features more formal investigations, based on explicit theory and/or empirical evidence.
The Covid-19 breakout challenges all areas of economics including, but not only, health, industrial organization, macroeconomics, finance, history, development, inequality, political economy and public finance, and concerns theory as well as empirical evidence. We are welcoming submissions in all these areas and we aim to have a wide geographical coverage.
Covid Economics is special in three respects:
- It presents research in real time. The submissions are evaluated very fast, in less than 48 hours and appear online a few days later.
- The papers are vetted by Editors for quality and relevance Vetting is different from refereeing in the sense that the decision is up or down, with no possibility of revising and resubmitting.
- The articles are pre-prints, meaning that authors, who retain copyright, may later submit to established reviews. The list of reviews that have announced that they will accept revised versions of papers featured in Covid Economics appears in each issue.
The vetting process aims at making Covid Economics a reliable source of on-going academic research.
The accepted papers are collated in ‘issues’. There is no preset periodicity of the issues. They are posted whenever a sufficient number of papers are accepted.
CEPR-affiliated researchers may also publish them in the CEPR Discussion Paper series.
|How to Submit to Covid Economics|
Please note that if you wish to submit or have already submitted your paper to a journal that is not on the list of journals who will accept papers previously published in Covid Economics, you must get their agreement prior to submission to Covid Economics. You also must clear the situation if you have already submitted the same paper to a journal on the list, since some journals specify that they will only accept a suitably revised version.
This paper examines deforestation's effect on the COVID-19 transmission to indigenous peoples and its transmission mechanisms. To that end, I analyze the Brazilian case and use new datasets that cover all the country's municipalities daily. Relying on a fixed-effects model, I find that deforestation is a powerful and consistent variable to explain the transmission of COVID-19 to indigenous populations. The estimates show that one unit increase in deforestation per 100 Km2 is associated, on average, with the confirmation of 2.4 to 5.5 new daily cases of COVID-19 in indigenous people 14 days after the deforestation warnings. One Km2 deforested today results in 9.5% more new COVID-19 cases in two weeks. In accumulated terms, deforestation explains at least 22% of all COVID-19 cases confirmed in indigenous people until 31 August 2020. The evidence suggests that the main mechanisms through which deforestation intensifies human contact between indigenous and infected people are illegal mining and conflicts.
In this study, we estimate the overall impact of the novel Coronavirus pandemic on Chinese exports and differentiate the hypothesized `triple pandemic effect' across its three components: 1) the domestic supply shock; 2) the international demand shock; and 3) the effects of Global Value Chain (GVC) contagion. We find that Chinese exports are very sensitive to the severity of the global Coronavirus outbreaks. Average export elasticity estimates with respect to new Chinese and foreign destination country infections range from -2.5 to -4.6. Against a Covid-19-free counterfactual, our estimates predict that the pandemic has reduced Chinese exports by as much as 40% to 45% during the first half of 2020, but that these losses have peaked and are expected to partially recover by the end of the year. Moreover, we find that all three shocks contribute to the pandemic-induced reduction in Chinese exports, but that GVC contagion exerts the largest and most persistent influence explaining these losses. Among the three shocks, the impact of GVC contagion explains around 75% of the total reduction in Chinese exports, while the domestic supply shock in China accounts for around 10% to 15% and the international demand shock only explains around 5% to 10%. As a result of these varying transmission channels, the pandemic effects appear to be very distinct from those explaining the Great Trade Collapse in 2008-09.
Issue : 52
This paper introduces an information structure into the standard SIR model to investigate the role of targeted lockdown policies in the presence of incomplete information. By allowing for asymptomatic infected agents and symptomatic susceptible agents, such that the presence of a symptom is an imperfect indicator of an agent's health state, we solve for the optimal lockdown policies on symptomatic and non-symptomatic agents. The model is then calibrated to the UK, where we find mitigation measures have reduced the peak of the infection rate from 23.9% to 3.47%, and decreased the number of fatalities by 39.1% if a vaccine is discovered within 18 months. If a uniform lockdown policy is pursued, the costs to the economy are large with GDP falling by 18.3% in the first year. However, through conditioning lockdown policies on the presence of symptoms, these costs may be substantially reduced, with no increase in the number of fatalities.
A survey experiment exposes treatment groups to four messages supporting future vaccination against COVID-19. These treatments emphasize either the risks of the virus or the safety of vaccination, to the respondent personally or to others. For a nationally representative sample, self-reported intent to vaccinate is not significantly different from the control for any message. However, there is a substantial divergence between white non-Hispanic respondents, whose response to all four treatments is close to zero, and non-white or Hispanic respondents, whose intention to vaccinate is over 50% higher in response to a message emphasizing pro-sociality and the safety of others.
How does interconnectedness affect the pandemic? What are the optimal within and between states containment policies? We embed a spatial SIR model into a multi-sector quantitative trade model. We calibrate it to US states and find that interconnectedness increases the death toll by 73,200 lives. A local within-state containment policy minimizes welfare losses relative to a national policy or to one that reduces mobility between states. The optimal policy combines local within- and between-state restrictions and saves 132,200 lives. It includes a peak reduction in mobility of 33% saving approximately 40,000 lives. Different timing of policies across states is key to minimize losses. States like Arizona might have imposed too early internal lockdowns while too late travel restrictions.
This paper examines the effects of pandemics on income inequality, specifically those pandemics that claimed more than 100,000 lives. Given that pandemics are events that rarely occur, we have use data spanning over the last 100 years (1915-2017) and relating to four pandemics. The study includes four countries that had income inequality data covering that period. Using panel data methods – Fixed Effects and Augmented Mean Group estimators – we found a significant effect of these pandemics on declining income inequality. The study argues that based on the characteristics of the COVID-19 pandemic, namely that fatalities are highly concentrated in older age groups, we can neither expect a labor scarcity nor a sharp decline in productivity; however, we could expect a reduction in consumption, the possibility of savings, high unemployment rates, and high public debt ratios. The ultimate effects of COVID-19 on inequality remain unclear so far, as some of its inherent characteristics push for an increase in inequality. In contrast, others push toward a narrowing of the income gap.
We examine self-reported productivity of home workers during lockdown using survey data from the UK. On average, workers report being as productive as at the beginning of the year, before the pandemic. However, this average masks substantial differences across sectors, by working-from-home intensities, and by worker characteristics. Workers in industries and occupations characterized as being suitable for home work according to objective measures report higher productivity on average. Workers who have increased their intensity of working from home substantially report productivity increases, while those who previously always worked from home report productivity declines. Notable groups suffering the worst average declines in productivity include women and those in low-paying jobs. Declines in productivity are strongly associated with declines in mental well-being. Using stated reasons for productivity declines, we provide evidence of a causal effect from productivity to well-being.
We investigate the short term labor market response to the pandemic in Italy and provide a first evaluation of the policies put in place to shield workers from the disruption of economic activity. Using administrative data on a sample of contracts active in the first quarter of 2020, we show that, before the pandemic, workers employed in non-essential activities were in majority men, younger than 35 years old, located in the North of the country and with lower levels of education. When looking at the change in hirings and separations and decomposing it by age, gender, region, type of contract (open-ended or temporary), education level, and sector (essential vs non-essential activities), we find that from the nineth week of the year, there was a pronounced drop in hirings and terminations. On the contrary, firings and quits spiked right after the nineth week, and then dropped significantly, reflecting the effects of the firing freeze and the easing of access to STW compensation schemes. We further explore separations by examining which factors predict the probability of job loss. We find that those workers that were already suffering the consequences of the previous recession (young, temporary, low-skill workers) are those at higher risk of losing their job because of COVID-19. Gender, instead, is a non-significant predictor of job loss in the aggregate.
Issue : 51
We document that racial disparities in COVID-19 in New York City stem from patterns of commuting and housing crowding. During the initial wave of the pandemic, out-of-home activity related to commuting is strongly associated with COVID-19 cases at the ZIP Code level and hospitalization at an individual level. After layoffs of essential workers decreased commuting, case growth continued through household crowding. A larger share of individuals in crowded housing or commuting to essential work are Black, Hispanic, and lower-income. As a result, structural inequalities, rather than population density, help determine the cross-section of COVID-19 risk exposure in urban areas.
The time-dependent reproduction number, Rt, is a key metric used by epidemiologists to assess the current state of an outbreak of an infectious disease. This quantity is usually estimated using time series observations on new cases, or deaths, combining this information with the distribution of the serial interval of transmissions. For a new epidemic, such as COVID-19, the available information on the serial interval is limited. Bayesian methods are often used to combine this limited information with the new cases, with the new cases usually being smoothed by a simple, but to some extent arbitrary, moving average. This paper describes a new class of time series models for tracking and forecasting new cases. The viability of these models and their ability to deal with spikes and second waves is illustrated with data from Germany and Florida. As a by-product, estimates of Rt, together with their standard deviations, can be obtained from the growth rate of new cases. Very few assumptions are needed and those that are made can be checked. This leads us to the conclusion that tracking an epidemic by trying to estimate Rt may be neither necessary nor desirable.
According to conventional wisdom, banks play a special role in providing liquidity in bad times, while capital markets are used to fund investment in good times. Using micro-data on corporate balance sheets following the COVID-19 shock, we provide evidence that instead, the corporate bond market is central to firms' access to liquidity, crowding out bank loans even when the banking sector is healthy. We first show that, contrary to good times, bond issuance is used to increase holdings of liquid assets rather than for real investment. Second, most issuers, including many riskier "high-yield" firms, prefer issuing bonds to borrowing from their bank. Over 40% of bond issuers leave their credit line untouched in 2020Q1. Moreover, a large share of bond issuance is used to repay existing bank loans. This liquidity-driven bond issuance questions the comparative advantage of banks in liquidity provision, and suggests that the V-shaped recovery of bond markets, propelled by the Federal Reserve, is unlikely to lead to a V-shaped recovery in real activity.
On March 24, 2020, India's Prime Minister announced the world's largest COVID-19 lockdown, bringing to a near-halt the economic and social lives of more than one billion Indian residents. This paper quantifies the economic impacts and behavioral changes induced by this unprecedented policy using two unique data sources: Facebook mobility data and a representative sample of previously surveyed low income Delhi households. Compliance with the lockdown was widespread: intra-city movement declined by 80\% following the announcement. The economic consequences have been accordingly severe, with income and days worked falling by 86 and 72\% respectively. Nevertheless, observance of public health directives was high: mask usage rose by 73 percentage points and handwashing became nearly universal, while time spent outdoors and smoking both declined. We also show how government-provided social assistance may have averted more dire predictions of widespread famine, resource scarcity, access to medical care, and security. But the declines in mental health and the near-exhaustion of personal savings, amidst a rising infection rate, indicate an important and evolving role for policy-makers as the crisis continues.
The COVID-19 pandemic and the consequent Government-imposed restrictions have altered the way of life around the globe. Using the newly “Nigeria Baseline COVID-19 National Longitudinal Phone Survey 2020”, this paper contributes to the nascent literature on the Economics of COVID-19 by examining the impact of changes in income and social assistance due to the pandemic on the coping strategies of family business owners. We find that family business owners who experienced a reduction in income and those that received social assistance due to the pandemic are likely to increase their coping level. We discuss the policy implications of these findings.
Issue : 50
The Covid-19 pandemic is a major test for governments around the world. We study the political consequences of (mis-)managing the Covid crisis by constructing a high-frequency dataset of government approval for 35 countries. In the first weeks after the outbreak, approval rates for incumbents increase strongly, consistent with a global “rally around the flag” effect. Approval, however, drops again in countries where Covid cases continue to grow. This is especially true for governments that do not implement stringent policies to control the number of infections. Overall, the evidence suggests that loose pandemic policies are politically costly. Governments that placed more weight on health rather than short-term economic outcomes obtained higher approval.
By focusing on the cost conditions at issuance, I find that not only the Covid-19 pandemic effects were different across bonds and firms at different stages, but also that the market composition was significantly affected, collapsing on investment-grade bonds, a segment in which the share of bonds eligible to the ECB corporate programmes strikingly increased from 15% to 40%. At the same time the high-yield segment shrunk to almost disappear at 4%. In addition to a market segmentation along the bond grade and the eligibility to the ECB programmes, another source of risk detected in the pricing mechanism is the weak resilience to pandemic: the premium requested is around 30 basis points and started to be priced only after the early containment actions taken by the national authorities. On the contrary, I do not find evidence supporting an increased risk for corporations headquartered in countries with a reduced fiscal space, nor the existence of a premium in favour of green bonds, which should be the backbone of a possible "green recovery".
We use population-wide data from linked administrative registers to study the distributional pattern of mortality before and during the Covid-19 pandemic in Belgium. Excess mortality is only found among those aged 65 and over. For this group, we find a significant negative income gradient in excess mortality, with excess deaths in the bottom income decile more than twice as high as in the top income decile for both men and women. However, given the high inequality in mortality in normal times, the income gradient in all-cause mortality is only marginally steeper during the peak of the health crisis when expressed in relative terms. Leveraging our individual-level data, we gauge the robustness of our results for other socioeconomic factors and find that conclusions about the income gradient in excess mortality based on aggregate data can be misguided.
Managing the COVID-19 effectively requires a series of mitigating steps that change over time as function of external conditions and previous mitigating steps. Modeling this effectively requires a multi-disciplinary integrative approach that combines epidemiological, economic and social considerations within a unified modeling environment. Our research focuses on incorporating elements from classical utility theory in combination with control theory and machine learning to better model the dynamics of the non-linear trade-offs inherent in managing the pandemic. We postulate a theoretical formulation on how these tradeoffs can be modeled, and demonstrate empirical results to elucidate the limiting factors in finding efficient solutions.
Using zip code-level data and nonparametric estimation, I present eight stylized facts on the US housing market in the COVID-19 era. Some aggregate results are: (1) growth rate of median housing price during the four months (April-August 2020) since the Federal Reserve’s unprecedented monetary easing has accelerated faster than any four-month period in the lead-up to the 2007-09 global financial crisis; (2) the increase in housing demand in response to lower mortgage interest rates displays a structural break since March 2020 (housing demand has increased by much more than before). These results indicate either the existence of “fear of missing out” or COVID-induced fundamental changes in household behavior. In terms of distributional evidence, I find that the increase of housing demand seems more pronounced among the two ends of the income distribution, possibly reflecting relaxed liquidity constraints at the lower end and speculative demand at the higher end. I also find that the developments in housing price, demand, and supply since April 2020 are similar across urban, suburban, and rural areas. The paper highlights the potential unintended consequences of COVID-fighting policies and calls for further studies of the driving forces of the empirical findings.
This paper studies the causal effect of local exposure to the COVID-19 on voting behavior and electoral outcomes using evidence from the regional elections held in Spain on July 12, 2020. Exploiting the variation in exposure to the COVID-19 and using a difference-in-differences identification strategy, we show that turnover was between and 2.2 and 3.3 percentage points lower in municipalities that experienced positive cases of COVID-19. However, we do not find evidence of changes in the vote shares to the incumbent parties at the regional or national levels. We further discuss fear as the potential mechanism driving our results.
This paper studies the effects of COVID-19 on voting turnout using as a case study an election that took place right after the peak of the first wave of the pandemic, the Basque Country regional elections. With the spread of COVID-19 there is a fear that in-person voting will spread the virus, which adds an additional burden to voters that is expected to decrease turnout. We confirm this hypothesis using a difference-in-difference model. We find that COVID-19 caused turnout to decrease by approximately 4.7% in municipalities affected by the virus compared to those that at the time of the election had not been affected by it. This effect on turnout is higher for municipalities affected also by deaths from coronavirus than when affected only by infected cases.
Issue : 49
We develop a model of human interaction to analyze the relationship between globalization and pandemics. Our framework provides joint microfoundations for the gravity equation for international trade and the Susceptible-Infected-Recovered (SIR) model of disease dynamics. We show that there are cross-country epidemiological externalities, such that whether a global pandemic breaks out depends critically on the disease environment in the country with the highest rates of domestic infection. A deepening of global integration can either increase or decrease the range of parameters for which a pandemic occurs, and can generate multiple waves of infection when a single wave would otherwise occur in the closed economy. If agents do not internalize the threat of infection, larger deaths in a more unhealthy country raise its relative wage, thus generating a form of general equilibrium social distancing. Once agents internalize the threat of infection, the more unhealthy country typically experiences a reduction in its relative wage through individual-level social distancing. Incorporating these individual-level responses is central to generating large reductions in the ratio of trade to output and implies that the pandemic has substantial effects on aggregate welfare, through both deaths and reduced gains from trade.
We document a causal effect of conservative Fox News Channel in the United States on physical distancing during COVID-19 pandemic. We measure county-level mobility covering all U.S. states and District of Columbia produced by GPS pings to 15-17 million smartphones and zip-code-level mobility using Facebook location data. Then, using the historical position of Fox News Channel in the cable lineup as the source of exogenous variation, we show that increased exposure to Fox News led to a smaller reduction in distance traveled and smaller increase in the probability to stay home after the national emergency declaration in the United States. Our results show that slanted media can have a harmful effect on containment efforts during a pandemic by affecting people’s behaviour.
Using data from an original survey conducted in June 2020, this study examines the prevalence, frequency, and productivity of working from home (WFH) practices during the COVID-19 pandemic in Japan. The results reveal that the percentage of employees who practiced WFH was approximately 32%. Labor input attributed to WFH arrangements accounted for approximately 19% of total working hours. Highly educated, high-wage, white-collar employees who work in large firms in metropolitan areas tended to practice WFH. The mean WFH productivity relative to working at the usual workplace was about 60% to 70%, and it was lower for employees who started WFH practices only after the spread of the COVID-19 pandemic. Meanwhile, highly educated, and high-wage employees, as well as long-distance commuters, tended to exhibit a relatively small reduction in WFH productivity.
How do firms' global connectedness and market power affect their performance and resilience during crises? While global production and export networks expose firms to foreign shocks, they potentially make firms less susceptible to domestic shocks through diversification of suppliers and markets. Also, higher market power could provide buffers by allowing bigger margins of adjustments. Using weekly global stock market data, we show that firms with higher global connectedness (via supply chains and exports) and market power (measured by markups) are more resilient to domestic pandemic shocks. These findings contribute to a better understanding of firms' reaction and reallocation during crises.
Utilizing newly available data from the SEC on derivative performance and detailed derivative holdings, this paper studies how derivatives impact mutual fund performance, with an emphasis on the COVID-19 pandemic period. In contrast to previous research concluding derivatives are used for hedging, we find that most active equity funds use derivatives to amplify market exposure. Despite the seemingly small weight, derivatives have a significant impact on funds' leverage and contribute largely to fund returns. In response to the initial outbreak of COVID-19, funds trade more heavily on short derivative positions. This behavior is more prevalent among managers residing in states with early state-level Stay-at home orders, where the risk of recession is likely more salient. Funds that used derivatives for hedging purposes before the crisis significantly outperform nonusers by over 9% during the initial outbreak, as their distribution of derivative returns shifts to the right. By the end of June, they still outperform by 1.6%. On the contrary, funds that used derivatives to amplify market exposure underperform, and their distribution of derivative returns shifts to the left. While they do shift strategies, they are slow to open short positions and remain mostly amplifying funds. Consequently, by the time they shift, the market has already started to recover, so that they lose on their short positions. The shifts in derivative return distributions during the COVID-19 crisis are mostly driven by swap contracts, which have been ignored by prior studies.
The outbreak of COVID-19 led to a spike in the unemployment rate and a decrease in the number of job openings. It is unknown whether this shock in the labor market affects different groups of job seekers equally. With the help of a correspondence study I describe the relationship between labor market conditions and ethnic labor inequality. The results provide evidence of the changes in the ethnic employment gap during the early stage of the COVID-19 outbreak. Moreover, these changes are accompanied by fluctuations in the labor market competition.
Issue : 48
Infectious diseases, ideas, new products, and other “infectants” spread in epidemic fashion through social contact. The Covid-19 pandemic, the proliferation of “fake news,” and the rise of antibiotic resistance have thrust economic epidemiology into the forefront of public-policy debate and re-invigorated the field. Focusing for concreteness on disease-causing pathogens, this paper provides a taxonomy of economic-epidemic models, emphasizing both the biology / immunology of the disease and the economics of the social context. An economic epidemic is one whose diffusion through the agent population is generated by agents' endogenous behavior. I highlight properties of the Nash-equilibrium epidemic trajectory and discuss ways in which public-health authorities can change the game for the better, (i) by imposing restrictions on agent activity to reduce the harm done during a viral outbreak and (ii) by enabling diagnostic-informed interventions to slow or even reverse the rise of antibiotic resistance.
Individualism has long been linked to economic growth. Using the COVID-19 pandemic, we show that such a culture can hamper the economy's response to crises, a period with heightened coordination frictions. Exploiting variation in US counties' frontier experience, we show that more individualist counties engage less in social distancing and charitable transfers, two important collective actions during the pandemic. The effect of individualism is stronger where social distancing has higher externality and holds at the individual level when we exploit migrants for identification. Our results suggest that individualism can amplify economic downturns by exacerbating collective action problems.
We find that an entrepreneur's negative personal attitude towards debt – debt aversion – affects the financing decisions of the businesses they run. We conduct a large-scale survey of entrepreneurs and link it to their firms' registry-based financial information. After controlling for a range of observable traits, firms run by highly debt averse entrepreneurs are about nine percentage points less likely to use debt, compared to baseline debt usage of just under 50%. The same entrepreneurs are also almost 25% less likely to take up government-guaranteed debt during the COVID-19 crisis. We also conduct a set of experiments to strengthen a causal interpretation. The experiments randomize the framing of otherwise identical, hypothetical COVID-19 support policies as debt or grants. Framing policies as debt significantly decreases interest.
We study the effects of international supply chain disruptions on real economic activity and prices during the Covid-19 pandemic. We show that US sectors with a large exposure to intermediate goods imports from China contracted significantly and robustly more than other sectors. In particular, highly exposed sectors suffered larger declines in production, employment, imports, and exports. Moreover, input and output prices moved up relative to other sectors, suggesting that real activity declines in sectors with a high China exposure were not particularly driven by a slump in demand. Quantitatively, differences in China exposures accounts for about 9\% of the cross-sectoral variance of industrial production growth during March and April 2020. The estimated effects are short-lived effects and dissipate by July.
Since COVID-19 broke out, there has been renewed interest in understanding the economic and social dynamics of historical and more recent epidemics and pandemics, from the plagues of Antiquity to modern-day outbreaks like Ebola. These events can have significant impacts on the interplay between poverty and social cohesion, i.e. how different groups in society interact and cooperate to survive and prosper. To that effect, this survey paper provides an overview of how social responses to past epidemics and pandemics were determined by the epidemiological and non-epidemiological characteristics of these outbreaks, with a particular focus on the scapegoating and persecution of minority groups, including migrants. We discuss existing theories as well as historical and quantitative studies, and highlight the cases where epidemics and pandemics may lead to milder or more severe forms of scapegoating. Finally, we conclude with a summary of priorities for future research on epidemics, pandemics and social cohesion and discuss the possible effects and policy implications of COVID-19.
We forecast the short-term evolution of the Mexican economy after the COVID-19 shock. We take into account the fact that there is no similar shock observed in contemporaneous data. We combine an econometric procedure with a basic SIR model of the pandemic. To make the forecasts we first calculate an estimate of the shocks that hit the economy starting in March 2020. We then produce several forecasts in which we make variations on two dimensions: introducing a path for the pandemic or not, and if we do, we consider three scenarios. The introduction of paths of the pandemic in which new cases fall has a positive effect on the economy. The main results are the following. First, the shocks that hit the economy starting in March 2020 have the potential to produce a slow recovery of economic activity. In a forecast not conditioned on any path for the pandemic, the annual growth rate of the economy recovers positive values in the second quarter of 2021. Second, in our baseline scenario that includes a pandemic path based on the SIR model, the recovery is faster, having positive growth rates in the first quarter of 2021. To maximize the benefits of a fall in new cases, policy makers should reduce persistent effects of the initial shock that hit the economy. Otherwise economic activity would tilt towards a longer recession.
Background: Risk communication is a key component of public health interventions during an outbreak. As the coronavirus pandemic unfolded in late 2019, the World Health Organization (WHO) was at the forefront in the development of risk communication strategies. The WHO introduced a range of activities with the purpose of enabling the public to avail verified and timely information on COVID-19 prevention behaviors. Given the various WHO activities to protect the public health during COVID-19, it is important to investigate the extent of familiarity and uptake of the WHO recommendations among the public so far during the pandemic.
Methods: To do this, we conducted a large-scale Pan-European survey covering around 7500 individuals that are representative of populations from seven European countries, collected online during April 2-April 15, 2020. We use descriptive statistics including proportions and correlations and graphical representations such as bar charts to analyze and display the data.
Results: Our findings suggest that information from the WHO in the context of COVID-19 is well trusted and acted upon by the public. Overall familiarity and adherence were quite high in most countries. Adherence was higher for social distancing recommendations compared to hygiene measures. Familiarity and adherence were higher among older, female, and highly educated respondents. However, country level heterogeneities were observed in the level of trust in information from the WHO, with countries severely affected by the pandemic reporting lower levels of trust.
Conclusion: Our findings call for efforts from health authorities to get regular feedback from the public on their familiarity and compliance with recommendations for preventive measures at all stages of the pandemic, to further develop and adapt risk communication as the pandemic evolves.
The unprecedented crisis caused by the coronavirus pandemic has prompted a spontaneous collective effort by the economics profession to contribute both to the immediate policy response to the shock, and also to the debate about the character of the subsequent recovery. This paper describes the current contributions of economists, largely from a UK perspective, and compares – and contrasts – this episode with the activities of economists during WW2. In both cases economists have collectively responded to crisis demands with a strong sense of public service. There are also key differences, including the presence of a formal economics profession in government now in contrast to the earlier period, and also prior critiques of economics as a discipline since at least the 2008 financial crisis. The second world war led to significant innovations in economics and its professional status; the scale of the current crisis may in turn lead to an evolution in the professional character of the discipline.
Issue : 47
The most commonly used test for the presence of SARS-CoV-2 is a PCR test that is able to detect very low viral loads and inform on treatment decisions. Medical research has confirmed that many individuals might be infected with SARS-CoV-2 but not infectious. Knowing whether an individual is infectious is the critical piece of information for a decision to isolate an individual or not. This paper examines the value of different tests from an information-theoretic approach and shows that applying treatment-based approval standards for tests for infection will lower the value of those tests and likely causes decisions based on them to have too many false positives (i.e., individuals isolated who are not infectious). The conclusion is that test scoring be tailored to the decision being made.
This paper examines the implications of lockdown policies for asset prices using a susceptible-infected-recovered model with microeconomic foundations of individual economic behaviours. In our model, lockdown policies reduce (i) labour income by decreasing working hours and (ii) precautionary savings by decreasing susceptible agents' probability of getting infected in the future. We qualitatively show that strengthening lockdown measures negatively impacts asset prices at the time of implementation. Our empirical analysis using data from advanced countries supports this finding. Depending on parameter values, our numerical analysis displays a V-shaped recovery of asset prices and an L-shaped recession of consumption. The rapid recovery of asset prices occurs only if the lockdown policies are insufficiently stringent to reduce the number of new periodic cases. This finding implies the possibility that lenient lockdowns have contributed to rapid stock market recovery at the beginning of the COVID-19 pandemic.
We study the response of daily household spending to the unexpected component of the COVID-19 pandemic, which we label as pandemic shock. Based on daily forecasts of the number of fatalities, we construct the surprise component as the difference between the actual and the expected number of deaths. We allow for state-dependent effects of the shock depending on the position on the curve of infections. Spending falls after the shock and is particularly sensitive to the shock when the number of new infections is strongly increasing. If the number of infections grows moderately, the drop in spending is smaller. We also estimate the effect of the shock across income quartiles. In each state, low-income households exhibit a significantly larger drop in consumption than high-income households. Thus, consumption inequality increase after a pandemic shock. Our results hold for the US economy and the key US states. The findings remain unchanged if we choose alternative state-variables to separate regimes.
Using the universe of Austrian unemployment insurance records until May 2020, we document that the composition of UI claimants during the Covid-19 outbreak is substantially different compared to past times. Using a machine-learning algorithm from Gulyas and Pytka (2020), we identify individual earnings losses conditional on worker and job characteristics.
Covid-19-related job terminations are associated with lower losses in earnings and wages compared to the Great Recession, but similar employment losses. We further derive an accurate but simple policy rule targeting individuals vulnerable to long-term wage losses.
I study the demand for health insurance during the COVID-19 pandemic using Special Enrollment Period (SEP) individual-level enrollment data from the Washington State Affordable Care Act Marketplace. I document that most individuals enrolling in plans during the pandemic are those who lost minimum essential coverage, followed by uninsured individuals making use of Washington’s limited-time SEP for uninsured individuals. I estimate a demand model and find that low-income individuals and young individuals are more premium sensitive. I find that 20.4 percent of the individuals in my analysis sample did not pay their initial premium. Individuals losing minimum essential coverage are less likely to pay their initial premium than individuals using the SEP for other qualifying events. Lower income individuals are less likely to pay the initial premium than higher income individuals. My results suggest three reasons for considering more generous premium subsidies during the remainder of the pandemic: (1) individuals losing minimum essential coverage are already using the exchange to replace lost coverage, (2) consumers are premium sensitive, and (3) there are meaningful differences across demographic groups in the probability of paying the first premium, which is necessary for coverage to take effect.
This paper develops a quantitative life cycle model in which economic decisions impact the spread of the COVID-19 and, conversely, the virus affects economic decisions. The calibrated model is used to measure the welfare costs of the pandemic across the age, income, and wealth distribution and to study the effectiveness of various mitigation policies. In the absence of mitigation, young workers engage in too much economic activity relative to the social optimum, leading to higher rates of infection and death in the aggregate. The paper considers a subsidy-and-tax policy that imposes a tax on consumption and subsidizes reduced work compared to a lockdown policy that caps work hours. Both policies are welfare improving and lead to less infections and deaths. Notably, almost all agents favor the subsidy-and-tax policy, suggesting that there need not be a tradeoff between saving lives and economic welfare.
Mass attendance events are a mainstay of economic and social activity. Such events have public health consequences, facilitating the spreading of disease, with attendant economic consequences. There is uncertainty over the impact such events can have on the spread of disease. We investigate the impact of regular mass outdoor meetings on the spread of a virus by considering football matches in England in February and March 2020 and the spread of Covid-19 into April 2020. There were 340 league and cup football matches with a combined attendance of 1.625m people in March, taking place over 188 of 313 local areas. We look at the occurrence and attendance at matches, and how full the stadia were, and how these variables are related to the spread of Covid-19 in April. We evaluate Covid-19 cases, deaths and excess deaths, all as rates of 100,000 people in an area. We find evidence that mass outdoor events were consistent with more cases and deaths, even after controlling for measurable characteristics of local areas. We find that a football match is consistent with around six additional Covid-19 cases per 100,000 people, two additional Covid-19 deaths per 100,000 people, and three additional excess deaths per 100,000 people. This effect is slightly stronger for the areas of away teams in March, and slightly weaker for matches in February. These results suggest caution in returning to unrestricted spectator attendance at matches. We caveat our analysis though by noting that stadium access and egress routes can be adapted such that some of the opportunities for the spread of an airborne virus could be mitigated. We recommend that the relevant authorities conduct pilot events before determining to what extent fans can return to mass outdoor events.
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Japan's government has taken a number of measures, including declaring a state of emergency, to combat the spread COVID-19. We examine the mechanisms through which the government's policies have led to changes in people's behavior. Using smartphone location data, we construct a daily prefecture-level stay-at-home measure to identify the following two effects: (1) the effect that citizens refrained from going out in line with the government's request, and (2) the effect that government announcements reinforced awareness with regard to the seriousness of the pandemic and people voluntarily refrained from going out. Our main findings are as follows. First, the declaration of the state of emergency reduced the number of people leaving their homes by 8.6% through the first channel, which is of the same order of magnitude as the estimate by Goolsbee and Syverson (2020) for lockdowns in the United States. Second, a 1% increase in new infections in a prefecture reduces people's outings in that prefecture by 0.026%. Third, the government's requests are responsible for about one quarter of the decrease in outings in Tokyo, while the remaining three quarters are the result of information updating on the part of citizens through government announcements and the daily release of the number of infections. Our results suggest that what is necessary to contain the spread of COVID-19 is not strong, legally binding measures but the provision of appropriate information that encourages people to change their behavior.
We use microsimulation to estimate the distributional consequences of covid-19-induced lockdown policies in Argentina, Brazil, Colombia and Mexico. Our estimates of the poverty consequences are worse than many others’ projections because we do not assume that the income losses are proportionally equal across the income distribution. We also simulate the effects of most of the expanded social assistance governments have introduced in response to the crisis. This has a large offsetting effect in Brazil and Argentina, much less in Colombia. In Mexico, there has been no such expansion. Contrary to prior expectations, we find that the worst effects are not on the poorest, but those (roughly) in the middle of the ex ante income distribution. In Brazil we find that poverty among the afrodescendants and indigenous populations increases by more than for whites, but the offsetting effects of expanded social assistance also are larger for the former. In Mexico, the crisis induces significantly less poverty among the indigenous population than it does for the nonindigenous one. In all countries the increase in poverty induced by the lockdown is similar for male- and female-headed households but the offsetting effect of expanded social assistance is greater for female-headed households.
Using data collected from one of the most popular ridesharing platforms, we illustrate how mobility has changed after the exit from the Covid-19 induced confinement. We measure the impact of the Covid-19 outbreak on the level of mobility and the price of ridesharing. Finally, we show that the pandemic has exacerbated ethnic discrimination. Our results suggest that a decision-maker encouraging the use of ridesharing during the pandemic should account for the impact of the perceived health risks on ridesharing prices and should find ways to ensure fair access.
There is a concern among social scientists and policymakers that the COVID-19 crisis might permanently change the nature of work. We study how labor demand in Mexico has been affected during the pandemic by web scraping job ads from a leading job search website. As in the U.S., the number of vacancies in Mexico declined sharply during the lockdown (38 percent). In April there was a change in the composition of labor demand, and wages dropped across the board. By May, however, the wage distribution and the distribution of job ads by occupation returned to their pre-pandemic levels. Overall, there was a slight decline in specific requirements (gender and age), no change in required experience, and a temporary increase in demand for low-skilled workers. Contrary to expectations, opportunities for telecommuting diminished during the pandemic. Using a simple Oaxaca-Blinder decomposition, we find that the variation in the average advertised wage in April is explained more by a higher proportion of low-wage occupations than by a reduction in the wages paid for particular occupations. In sum, we find no evidence of a significant or permanent change in labor demand during the pandemic in Mexico.
We use the Current Employment Statistics Survey microdata to calculate employment changes since February 2020 by employer size. We find that for employers with 1-9 employees, the largest component of employment change since February is due to closings in all months (either temporary or permanent). For 10 or more employees by April, the largest component of employment change since February is employment changes within continuing employers, rather than those reporting zero employment or imputed closures from non-respondents in the survey. In percentage terms, the greatest overall employment losses shifted to larger and larger employers each month. By July, the largest cumulative employment losses were for employers with 100-499 employees, with 8% loss in employment since February, while employers with 1-9 employees had a loss of 4.3% in employment since February.
Recent years have witnessed a surge in demand from investors who invest with a socially responsible mandate. We study stock returns associated with this practice in the COVID-19 crash and the months before it. Based on data on mutual funds, we find that stocks with greater socially conscious investor ownership experience superior returns, lower returns volatility, and better market valuations during the pandemic. No differences are to be found with respect to gross profitability, operating income, and sales growth as well as expectations about the long-term growth rate of earnings per share. This suggests that socially conscious investors can act as a moderating force by mitigating losses in the stock market in a time of crisis.
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In this paper, we provide evidence that the early labor market effects of COVID- 19 have been concentrated on subsets of the workforce already negatively hit by the recent wave of structural change in the occupation and skills of workers. We document that the occupation and education composition of furloughed workers in Denmark is concentrated among individuals with low education or vocational training, as well as specific occupational groups that were on the decline before the crisis hit. Our results strengthen the hypothesis that COVID-19 will accelerate the ongoing structural transitions in the economy.
The COVID-19 pandemic and associated policy responses triggered a historically large wave of capital reallocation between markets, asset classes, and industries. Using high-frequency country-level data, we examine if and how the number of infections, the stringency of the lockdown, and the fiscal and monetary policy response determined the dynamics of portfolio flows, market-implied sovereign risk, and stock prices. We find that these factors played an important role, particularly for emerging markets. Our results indicate that domestic infections had an initial negative impact on flows. Cumulatively, however, the effect was positive and reflected increased demand for financing by affected economies. We also find that both lockdown and fiscal measures supported portfolio flows, driven by an increased supply of funds. Bonds, not equities, were the primary driver of portfolio flows, highlighting a pattern of reallocation to safety. Finally, we show that monetary policy loosening in developed markets led to a cumulative decline in flows, as investors searched for higher yield.
This paper casts some light on the impact of regulatory restrictions on the movement of people across international borders, implemented on health and safety grounds following the COVID-19 outbreak, on services trade costs using some illustrative scenarios where all the countries are assumed to close their borders to passengers, but leave freight trade open. Services trade costs are estimated to increase by an average of 12% of export values across sectors and countries in the medium term in such a hypothetical scenario. The analysis identifies a large variability in the increase in services-trade costs across sectors and across countries, reflecting the stringency of initial regulations and the relative importance of business travel and labour mobility to international services trade.
Social distancing is a matter of individuals’ choices as well as of regulation, and regulation arguably responds to those choices. We analyse weekly panel data on such behaviour for English Upper Tier Local Authorities (UTLAs) from March to July 2020, paying attention to the influence of poverty, as measured by free school meals provision. Panel regressions suggest that, although more stringent regulation and slightly lagged local cases of infection increase social distancing, both effects are weaker in UTLAs with higher levels of poverty. Thus motivated, we develop a two-class (rich/poor) model, in which a Nash non-cooperative equilibrium arises from individual choices in a regulatory regime with penalties for non-compliance. The model yields results in keeping with the empirical findings, indicating the desirability of generous measures to furlough workers in low-paid jobs as a complement to the stringency of general regulation.
We use monthly and daily transaction data from Iran, disaggregated by provinces, good and service categories, and retail store segments to gauge the impact of government emergency loans on consumption patterns. We find that emergency loans are positively related with higher consumption of non-durable and semi-durable goods, suggesting that the emergency loans were predominantly used for their intended purpose. The effects were strongest in the first few days and then dissipated over time. We find effects only for in-store but not online transactions and in poorer rather than richer provinces, suggesting that it is the poorer who reacted more strongly with higher consumption to the emergency loans.
This paper documents that the employment effects of financial aid to U.S. states during the Great Recession were strongly unevenly distributed across sectors. We show that state fiscal relief had a double dividend: not only did it preserve a substantial number of jobs, but it also fostered employment most strongly in the sectors hit hardest by the recession. We exploit differences in the distribution of recessionary job losses across states to draw conclusions for the Covid-19 recession. Our results suggest that the double dividend of state fiscal relief cannot be taken for granted.
The COVID-19 pandemic causes sharp reductions in economic output and sharp increases in government expenditures. This increases the riskiness of sovereign borrowing both domestically and internationally. We propose a framework to study debt sustainability by introducing domestic debt into a sovereign default model, in which the government sets distortionary labour taxes and decides whether to repay its past domestic and foreign obligations. The results show, that foreign default is more likely after a negative productivity shock, while domestic default is more likely after a negative expenditure shock. Even in the case of a recently proposed broad restructuring of foreign debt, governments may still selectively default on their domestic debt obligations.
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We develop an ECON-EPI network model to evaluate policies designed to improve health and economic outcomes during a pandemic. Relative to the standard epidemiological SIR set-up, we explicitly model social contacts among individuals and allow for heterogeneity in their number and stability. In addition, we embed the network in a structural economic model describing how contacts generate economic activity. We calibrate it to the New York metro area during the 2020 COVID-19 crisis and show three main results. First, the ECON-EPI network implies patterns of infections that better match the data compared to the standard SIR. The switching during the early phase of the pandemic from unstable to stable contacts is crucial for this result. Second, the model suggests the design of smart policies that reduce infections and at the same time boost economic activity. Third, the model shows that re-opening sectors characterized by numerous and unstable contacts (such as large events or schools) too early leads to fast growth of infections.
Covid-19 vaccine prioritization is key if the initial supply of the vaccine is limited. A consensus is emerging to first prioritize populations facing a high risk of severe illness in high-exposure occupations. The challenge is assigning priorities next among high-risk populations in low-exposure occupations and those that are young and healthy but work in high-exposure occupations. We estimate occupation-based infection risks and use age-based infection fatality rates in a model to assign priorities over populations with different occupations and ages. Among others, we find that 50-year-old food-processing workers and 60-year-old financial advisors are equally prioritized. Our model suggests a vaccine distribution that emphasizes age-based mortality risk more than occupation-based exposure risk. Designating some occupations as essential does not affect the optimal vaccine allocation unless a stay-at-home order is also in effect. Even with vaccines allocated optimally, 1.37% of the employed workforce is still expected to be infected with the virus until the vaccine becomes widely available, provided the vaccine is 50% effective, and assuming a supply of 60mil doses.
This paper investigates whether the COVID-19 crisis has affected the way we think about (political) institutions, as well as our broader (policy) attitudes and values. We fielded large online survey experiments in Italy, Spain, Germany and the Netherlands, well into the first wave of the epidemic (May-June), and included outcome questions on trust, voting intentions, policies & taxation, and identity & values. With a randomised survey flow we vary whether respondents are given COVID-19 priming questions first, before answering the outcome questions. With this treatment design we can also disentangle the health and economic effects of the crisis, as well as a potential “rally around the flag” component. We find that the crisis has brought about severe drops in interpersonal and institutional trust, as well as lower support for the EU and social welfare spending financed by taxes. This is largely due to economic insecurity, but also because of health concerns. A rallying effect around (scientific) expertise combined with populist policies losing ground forms the other side of this coin, and suggests a rising demand for competent leadership.
By scraping data of almost 17 trillion plays of songs on Spotify in six European countries, this work provides evidence that the lockdown imposed in the midst of the COVID-19 pandemic significantly changed the music consumption in terms of nostalgia. This work constructs a binary measure of nostalgia consumption of music and employs country-specific logistic regressions in which lockdown is taken as a treatment that interacts with a quadratic trend. The lockdown altered the trend of nostalgia consumption upward, which peaked roughly 60 days after the lockdown. A placebo test shows that the upward turn of slope is not an annual pattern. On the other hand, COVID incidence rate does not provide significant additional explanatory power to the model. This work shows that Spotify's users react to the lockdown even when COVID incidence rate is low and the impact stays high even the incidence rate has peaked, suggesting that demand for nostalgia tends to respond to the drastic and lasting change caused by the lockdown rather than to the fluctuations in the viral infection.
We examine the short-term labour market effects of COVID-19 and the associated national lockdown in Australia by estimating person-fixed-effects models using the Longitudinal Labour Force Survey. COVID-19 decreased labour force participation (LFP) by 2.1%, increased unemployment by 1.1% and reduced weekly working hours by 1.1. The national lockdown decreased LFP by 3.3%, increased unemployment by 1.7%, and decreased weekly working hours by 2.5. The probability of working on Fridays decreased by 10% while working fewer hours due to being on leave, work shifts, not having enough work and losing jobs all increased due to the lockdown. The pandemic and the lockdown increased underemployment and job search efforts significantly. In terms of heterogeneity of these effects, our analysis shows that those with up to high-school education experienced larger reductions in their LFP and working hours than others. However, immigrants and individuals with shorter job tenure or occupations unsuitable for remote work were hit the hardest in terms of unemployment.
This note evaluates the expected economic toll of the Covid-19 pandemic in the Consensus Forecast surveys. It employs the surveys' forecasts at different horizons. Its main findings are as follows. First, the recovery is expected to be neither U- nor V-shaped but ``akin to a lopsided square root sign'' (Tett (2020)). Second, because the recovery is slow and incomplete, GDP losses during the Lockdown represent a small fraction of the total GDP loss, expected to reach 3 to 4% per annum for the developed countries under review. Third, there are massive differences in the economic toll among countries, which are only partly explained by their public-health performances.
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This study compares this year’s trend of EUIPO’s trademark applications during May and the first twenty days of June to 2019. There are four important findings. First, overall trademark applications appear to be at the same level to last year. Second, there is significant heterogeneity by country. While many countries are at the same levels to last year, China is an outlier increasing its filings dramatically compared to 2019. Certain countries also experience a sharp decrease including Canada and Brazil. Third, the presence of entrants is higher in 2020 compared to the same period of 2019. Finally, there are some clear winners and losers in terms of business activity. Overall, service-related endeavors are less frequent compared to last year while certain product-related initiatives have experienced a significant increase. This study urges dissemination of trademark applications, across Offices, in bulk to facilitate empirical work. Unlike patent applications, which take eighteen months to become known, information on trademark applications is disclosed relatively quickly. Since they can approximate real-time business expectations of demand and are related to innovation and firm value, they can provide us with significant insights in the short-run until more data become available.
Knowing the prevalence of the COVID-19 infection in a population of interest, and how it changes over time and across space, is of fundamental importance for public health. Unfortunately, the fraction of cases who turn out to be positive in a test provides a distorted picture of the prevalence of the infection because the tested cases are not a random sample of the population. Since random testing of the population is costly and complicated to carry out, in this note we show how to use the available information, in conjunction with credible assumptions about unknown quantities, to obtain a range of plausible values for the prevalence of the infection. We discuss the difference between two alternative measures of prevalence and argue that one of the two is much harder to pin down with the data currently available. We apply our method to the Italian data.
Fragmented by policies, united by outcomes: This is the picture of the United States that emerges from our analysis of the spatial diffusion of Covid-19 and the scattered lock-down policies introduced by individual states to contain it. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states' lock-down policies on infections in neighboring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. Spatial impulse response functions reveal that the diffusion across counties is persistent for up to ten days after an increase in adjacent counties. We then develop a spatial version of the epidemiological SIR model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighboring states. We incorporate lock-down policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous U.S. states and DC. Our results suggest that, had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run.
This paper uses administrative, survey, and online vacancy data to analyze the short-term labor market impacts of the COVID-19 lockdown in Greece. We find that flows into unemployment have not increased; in fact, separations were lower than would have been expected given trends in recent years. At the same time, employment was about 12 percent lower at the end of June than it would have been without the pandemic. The interrupted time series and difference-in-differences estimates indicate that this was due to a dramatic slowdown in hiring during months when job creation typically peaks in normal years, mostly in tourism. While we do not formally test the reasons for these patterns, our analysis suggests that the measures introduced to mitigate the effects of the crisis in Greece have played an important role. These measures prohibited layoffs in industries affected by the crisis and tied the major form of income support to the maintenance of employment relationships.
Does the ranking of Covid-19 cases by municipalities follow a Zipf ’s law (i.e. an estimated Pareto exponent of one)? This note tries to answer this question using daily data from Brazil for the Mar 30 - Aug 06 period. We used a Poisson Pseudo Maximum Likelihood (PPML) estimator for our estimates and the result is that the Pareto exponent of the ranking of Covid-19 cases is converging to the one obtained for the population ranking and seems to follow the pattern of the Zipf ’s Law. We did the same exercise using Italian regions’ daily data which we use as a proxy to a long run equilibrium as the pandemic is apparently under control there. Contrary to Brazil, the Pareto exponent for the ranking for Covid-19 cases does not converge to the one estimated for the regular population. We try to advance some rationale for this contrasting behavior between them.
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Using online data for prices and real-time debit card transaction data on changes in expenditures for Switzerland allows us to track inflation on a daily basis. While the daily price index fluctuates around the official price index in normal times, it drops immediately after the lockdown related to the COVID19 pandemic. Official statistics reflect this drop only with a lag, specifically because data collection takes time and is impeded by lockdown conditions. Such daily real-time information can be useful to gauge the relative importance of demand and supply shocks and thus inform policy makers who need to determine appropriate policy measures.
The COVID-19 outbreak has cut China’s supply of and raised the world’s demand for face masks, disinfectants, ventilators, and other critical medical goods. This article studies the economic and political factors that are associated with China’s exports of medical equipment during the first two months of the global pandemic. Regression results show that—controlled for demand factors—countries with stronger past economic ties with China import more critical medical goods from China at both the national level and the level of Chinese provinces. Friendly political relations, such as the twinning of provinces, appear to work as a substitute for pre-existing economic ties at the provincial level. These findings imply that, to secure access to medical equipment in crises, countries are well advised to either diversify their sources or to develop closer relations with Beijing and China’s provinces.
Using a simple economic model in which social-distancing reduces contagion, we study the implications of waning immunity for the epidemiological dynamics and social activity. If immunity wanes, we find that COVID-19 likely becomes endemic and that social-distancing is here to stay until the discovery of a vaccine or cure. But waning immunity does not necessarily change optimal actions on the onset of the pandemic. Decentralized equilibria are virtually independent of waning immunity until close to peak infections. For centralized equilibria, the relevance of waning immunity decreases in the probability of finding a vaccine or cure, the costs of infection (e.g., infection-fatality rate), and the presence of other NPIs that lower contagion (e.g., quarantining and mask use). In simulations calibrated to July 2020, our model suggests that waning immunity is virtually unimportant for centralized equilibria until at least 2021. This provides vital time for individuals and policymakers to learn about immunity against SARS-CoV-2 before it becomes critical.
Attempts to constrain the spread of Covid-19 included the temporal reintroduction of travel restrictions and border controls within the Schengen area. While such restrictions clearly involve costs, their benefits have been disputed. We use a new set of daily regional data of confirmed Covid-19 cases from the respective statistical agencies of 18 Western European countries. Our data starts with calendar week 10 (starting 2nd March 2020) and extends to calendar week 17 (ending 26th April 2020), which allows us to test for treatment effects of border controls. Based on PPML methods and a Bayesian INLA approach we find that border controls had a significant effect to limit the pandemic.
This paper establishes a methodology that can be used to measure the behavior of International Monetary Fund (IMF) program design and eventually the outcomes of IMF programs in response to the COVID-19 crisis. We create an IMF COVID RECOVERY INDEX by coding IMF programs based on the extent to which they recommend or condition that borrowing countries increase efforts to combat the virus, protect the vulnerable, and stage a green recovery in accordance with direction from IMF leadership and fiscal guidance notes generated by the IMF Fiscal Affairs Department. Relative to earlier research that suggests the IMF falls short in making such policies part of recovery efforts during times past, our preliminary research indicates that the IMF is indeed prioritizing health and social spending during this crisis—albeit more so in programs where it has little leverage over the implementation of such recommendations. However, IMF support for greening the recovery does not match the rhetoric from IMF leadership or from fiscal guidance notes issued by the IMF Fiscal Affairs department at this time. The IMF COVID RECOVERY INDEX will be updated in real time on the internet, and eventually be used in econometric exercises that examine the extent to which IMF support for confronting the virus, protecting the vulnerable, and mounting a green recovery is associated with those desired outcomes.
This paper studies the labor market effects of non-pharmaceutical interventions (NPIs) to combat the COVID-19 pandemic. We focus on the Nordic countries which showed one of the highest variations in NPIs despite having similar community spread of COVID-19 at the onset of the pandemic: While Denmark, Finland and Norway imposed strict measures (`lockdowns'), Sweden decided for much lighter restrictions. Empirically, we use novel administrative data on weekly new unemployment and furlough spells from all 56 regions of the Nordic countries to compare the labor market outcomes of Sweden with the ones of its neighbors. Our evidence suggests that the labor markets of all countries were severely hit by the pandemic, although Sweden performed slightly better than its neighbors. Specifically, we find the worsening of the Swedish labor market to occur around 2 to 3 weeks later than in the other Nordic countries, and that its cumulative sum of new unemployment and furlough spells remained significantly lower during the time period of our study (up to week 21 of 2020).
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Herd immunity is a central concept in epidemiology. It refers to a situation where the amount of recovered and immune individuals is high enough to protect susceptibles from contracting the disease. Herd immunity can be obtained naturally when individuals recover from the disease, or artificially after the administration of an appropriate vaccine. The present paper addresses the question of the amount of public spending in medical research to obtain a vaccine which maximizes welfare. Public spending is assumed to reduce the waiting time to discover a vaccine against an infectious disease evolving according to a SIR model. Both linear and logarithmic preferences are considered with and without time discounting. Worth to note, we show that if an economy has a sufficiently performing technology, then the government should invest as much as the initial public budget allows.
This paper reviews the literature on incorporating behavioural elements into epidemiological models of pandemics. While modelling behaviour by forward-looking rational agents can provide some insight into the time paths of pandemics, the non-stationary nature of Susceptible-Infected-Removed (SIR) models of viral spread makes characterisation of resulting equilibria dicult. Here I posit a shortcut that can be deployed to allow for a tractable equilibrium model of pandemics with intuitive comparative statics and also a clear prediction that effective reproduction numbers (that is, R) will tend towards 1 in equilibrium. This motivates taking ^R = 1 as an equilibrium starting point for analyses of pandemics with behavioural agents. The implications of this for the analysis of widespread testing, tracing, isolation and mask-use is discussed.
Pandemics have heterogeneous effects on the health and economic outcomes of members of the population. To stay in power, politician-policymakers have to consider the health vulnerability- economic vulnerability (HV–EV) profiles of their coalition. We show that the politically optimal pandemic policy (POPP) reveals the HV–EV profile of the smallest, rather than the largest, group in the coalition. The logic of political survival dictates that the preferences of the least loyal, most pivotal, members of the coalition determine policy.
The goal of this paper is to study the impact of the lockdown policy on voting behaviour, during the COVID-19 pandemic. We focus on France, where a differential lockdown was implemented across departments, based on the local diffusion of the disease. In particular, the country has been divided in two areas, red and green, subject to a “hard” and a “soft” lockdown, respectively. To measure voting behaviour, before and after the policy, we rely on 2020 French municipal elections: the first round took place before the introduction of the restrictions, while the second round was delayed after the end of the lockdown. We estimate a Spatial Regression-Discontinuity-Design model comparing the difference in outcomes between the two electoral rounds, at the border of red and green areas. The main results suggest that lockdown regulations significantly affected electoral outcomes. First, in localities under a harder lockdown, the incumbent's vote share is higher as well as the consensus for Green parties. Second, voter turnout is larger where more stringent restrictions are adopted. These results suggest that lockdown measures strongly lead citizens to rally around the local incumbent politicians.
We consider an SIR model where the probability of infections between infected and susceptible individuals are viewed as Poisson trials. The probabilities of infection between pairwise susceptible-infected matches are thus order statistics. This implies that the reproduction rate is a random variable. We derive the first two moments of the distribution of Rt conditional on the information available at time t−1 for Poisson trials drawn from an arbitrary parent distribution with finite mean. We show that the variance of Rt is increasing in the proportion of susceptible individuals in the population, and that ex ante identical populations can exhibit large differences in the path of the virus. This has a number of implications for policy during pandemics. We provide a rationale for why shelter-in-place orders may be a better containment measure than mandating the use of masks because of their impact on the variance of the reproduction rate.
While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support.
In this study, we introduce a data-mining approach to measure industry- specific risks related to COVID-19. We examine company risk reports filed to the U. S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies’ awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries.
The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is up- dated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.
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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 that individuals in Florida and Texas (versus California and New York), men (versus women) and people who perceive fewer benefits of protective behaviors (versus those perceive more benefits) report lower levels of self-protecting behaviors. Broadly, our findings align with many typical relationships between health and socio-economic status. Moreover, we show 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.
To better understand the trade-offs at play as US states take measures to slow the spread of COVID-19, we investigate the effectiveness of alternative mitigation strategies using panel data estimates informed by epidemiological models. Our analysis evaluates public health outcomes using estimates of the effective reproduction number (Rt), which must drop below 1.0 to achieve sustained reductions in the infectious population. We fit outcomes for Rt on mitigation measures adopted by states, using daily data from early February through late June. Although all of the measures examined help reduce Rt, their effectiveness varies. Reductions in personal mobility on the scale achieved in April can reduce Rt by about a half and are especially effective when paired with stay-at-home orders. Alternatively, our estimates suggest that the virus could be brought under control using less-costly remediation measures. Those measures would likely involve more testing (at a rate of at least 1.75 million per day, if used in isolation), mask wearing requirements (which, in some specifications, are equivalent to testing 1.1mn persons per day), and restrictions on seated dining (which are effective when masks are not mandated). Finally, our estimates suggest that the US is nowhere near the point where “herd immunity” alone can bring infections under control
Effects of the COVID-19 shocks in the Japanese labor market vary across people of different age groups, genders, employment types, education levels, occupations, and industries. We document heterogeneous changes in employment and earnings in response to the COVID-19 shocks, observed in various data sources during the initial months after onset of the pandemic in Japan. We then feed these shocks into a life-cycle model of heterogeneous agents to quantify welfare consequences of the COVID-19 shocks.
In each dimension of the heterogeneity, the shocks are amplified for those who earned less prior to the crisis. Contingent workers are hit harder than regular workers, younger workers than older workers, females than males, and workers engaged in social and non-flexible jobs than those in ordinary and flexible jobs. The most severely hurt by the COVID-19 shocks has been a group of female, contingent, low-skilled workers, engaged in social and non-flexible jobs and without a spouse of a different group.
The paper introduces voluntary social distancing to the canonical epidemiology model, integrated into a conventional macroeconomic model. The model is extended to include treatment, vaccination, and government-enforced lockdown. Infection-averse individuals face a trade-off between a costly social distancing and the risk of getting infected and losing next-period labor income. We find an individual’s social distancing is proportional to the welfare loss she incurs when moving to the infected compartment. It increases in the individual’s psychological discount factor but decreases in the probability of receiving a vaccination. Quantitatively, a laissez-faire social distancing flattens the infection curve that minimizes the economic damage of the epidemic. A government-enforced social distancing is more effective in flattening the infection curve but has a detrimental effect on the economy.
We discuss the impact of a Covid-like shock on a simple toy economy, described by the Mark-0 Agent-Based Model that we developed and discussed in a series of previous papers. We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our toy economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss. This is due to the existence of a self-sustained ``bad'' state of the economy. We then discuss two policies that attempt to moderate the impact of the shock: giving easy credit to firms, and the so-called helicopter money, i.e. injecting new money into the households savings. We find that both policies are effective if strong enough, and we highlight the potential danger of terminating these policies too early. Interestingly, when policy is successful, inflation post-crisis is significantly increased. While we only discuss a limited number of scenarios, our model is flexible and versatile enough to allow for a much wider exploration, thus serving as a useful tool for the qualitative understanding of post-Covid recovery.
This paper investigates the “cultural” transmission of the SARS-CoV-2 outbreak. Using data from Germany we observe that in predominantly Catholic regions with stronger social and family ties, the spread and the resulting deaths per capita were much higher compared to non-Catholic ones at the local NUTS-3 level. The result is strengthened with Difference-in-Difference estimates at the regional NUTS-1 level. This finding could help explain the rapid spread and high death toll of the virus in some European countries compared to others. Looking at differences within a specific country in a well identified setting eliminates biases due to different social structures, health care systems, specific policies and measures, and testing procedures for the virus that can confound estimates and hinder comparability across countries. Further, we use individual level data as well as mobility data from mobile devices to investigate potential mechanisms. The results highlight the cultural dimension of the spread and could suggest the implementation of targeted mitigation measures in light of disease outbreaks.
With the help of growth forecasts and a simple structural model, we build a likely forward-looking account of the depth, length and shape of the recession as well as of the demand and supply shocks that are driving it. The results point to a V-shaped recession with partial recovery in advanced economies and to an L-shaped recession in emerging and developing economies. In addition, the projected shapes likely involve, in advanced economies, an output level shock, and in emerging and developing economies, an output growth shock. The depth and shape of the recession in output is important for fiscal debt sustainability analysis; in this matter the results are robust to the model parameters and assumptions. In turn, the depth and length of the recession in the output gap is critical for monetary and fiscal policies; in this matter we had to appeal to an assumption about the extent of the demand shock. The simple structural model does not have the problem of univariate filters that can misleadingly attribute to demand shocks a large part of the variability of output that is actually originated in supply shocks.
The Covid-19 crisis has led to disruption to schooling across the world. Though it is recognized that pupils are suffering immediate learning loss, there exists a lack of understanding as to how this disruption might affect longer-term educational outcomes. This study considers this issue by examining the effect of school disruption in England due to restrictions put in place to manage the Foot and Mouth Disease epidemic in cattle in 2001. Using a difference in difference approach, I analyze whether primary schools that had been significantly disrupted by the epidemic experienced lower performance in standardized tests for pupils aged 11 in English, maths and science in the year of the outbreak and in subsequent years. I find that primary schools that had been significantly disrupted by the measures to contain the epidemic exhibited achievement falls in the year immediately after the outbreak, driven by sizeable falls in maths performance. The negative effects weaken in subsequent years suggesting that the effects of school disruption had faded out to some extent by the time that cohorts that were younger at the time of exposure took the age 11 tests.
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The COVID-19 global pandemic has caused significant global economic and social disruption. In McKibbin and Fernando (2020), we used data from historical pandemics to explore seven plausible scenarios of the economic consequences if COVID-19 were to become a global pandemic. In this paper, we use currently observed epidemiological outcomes across countries and recent data on sectoral shutdowns and economic shocks to estimate the likely global economic impacts of the pandemic under six new scenarios. The first scenario explores the outcomes if the current course of COVID-19 is successfully controlled, and there is only a mild recurrence in 2021. We then explore scenarios where the opening of economies results in recurrent outbreaks of various magnitudes and countries respond with and without economic shutdowns. We also explore the impact if no vaccine becomes available and the world must adapt to living with COVID-19 in coming decades. The final scenario is the case where a given country is in the most optimistic scenario (Scenario 1), but the rest of the world is in the most pessimistic scenario. The scenarios demonstrate that even a contained outbreak will significantly impact the global economy in the coming years. The economic consequences of the pandemic under plausible scenarios are substantial and the ongoing economic adjustment is far from over.
In the COVID-19 pandemic, lockdowns and containment measures were a fundamental tool to control the spread of the virus. In this article, we analyze data from 120 countries seeking to assess the stringency of de jure lockdown policies, comparing them with their de facto compliance and empirically analyzing the determinants of social distancing noncompliance. We find that, from a de jure perspective, almost all the strictest and longest lockdowns took place in emerging or developing economies. However, when analyzing its de facto compliance, we document a generalized and increasing non-compliance over time, which is significantly higher in emerging and developing economies. We show that lockdown compliance declines with time, and is lower in countries with stricter quarantines, lower incomes and higher levels of labor precariousness.
We estimate the effect of the coronavirus (COVID-19) pandemic on racial animus as measured by Google searches and Twitter posts, including a commonly used anti-Asian racial slur. Our empirical strategy exploits the plausibly exogenous variation in the timing of the first COVID-19 diagnosis across regions in the United States. We find that the first local diagnosis leads to an immediate increase in racist Google searches and Twitter posts, with the latter mainly due to existing Twitter users posting the slur for the first time. This increase could indicate a rise in future hate crimes as we document a strong correlation between the use of the slur and anti-Asian hate crimes using historic data. Moreover, we find that the rise in animosity is directed at Asians rather than other minority groups and is stronger in hours and on days when the connection between the disease and Asians is more salient, as proxied by the number of President Trump’s tweets mentioning China and COVID-19 simultaneously. In contrast, the negative economic impact of the pandemic plays little role in the initial increase in racial animus. Our results suggest that de-emphasizing the connection between the disease and a particular racial group can be effective in curbing current and future racial animus.
COVID-19 has uprooted many aspects of parents’ daily routines, from their jobs to their childcare arrangements. In this paper, we provide a novel description of how parents in England living in two-parent opposite-gender families are spending their time under lockdown. We find that mothers’ paid work has taken a larger hit than that of fathers’, on both the extensive and intensive margins. We find that mothers are spending substantially longer in childcare and housework than their partners and that they are spending a larger fraction of their paid work hours having to juggle work and childcare. Gender differences in the allocation of domestic work cannot be straightforwardly explained by gender differences in employment rates or earnings. Very large gender asymmetries emerge when one partner has stopped working for pay during the crisis: mothers who have stopped working for pay do far more domestic work than fathers in the equivalent situation do.
It is shown that the standard Susceptible Infectious Recovered model of an epidemic implies that there for a large set of epidemic parameter values there will be increasing returns to scale if the objective is to limit the economic cost of infection. The explanation is that if an epidemic has a high basic reproduction number, a given amount of social distancing will not have much effect. The same amount may however be very effective if the reproduction number is lower, (but still larger than one.)
The Scandinavian countries of Denmark, Iceland, Norway and Sweden have approached the first months of the 2020 novel coronavirus pandemic with a range of economic and health policies that have resulted in disparate outcomes. Though similar in behavioral norms and institutions, Denmark, Iceland and Norway chose a precautionary approach that formally shut down schools and businesses to protect human health, while Sweden took a Business-As-Usual (BAU) approach aimed at maintaining normal economic and social activities. Iceland and Denmark have further invested in testing, tracking and containing the disease. Economic costs of the pandemic and government fiscal and monetary interventions to reduce their impacts have been dramatic and similar across countries, while Sweden has had the most severe loss of life. Using a panel from the four countries since the beginning of the pandemic, we calculate lives saved from stricter interventions by estimating cases and deaths as functions of behavior and government interventions with a bioeconomic model, then estimating the additional lives lost if these interventions did not occur. Comparison of the countries reveals three important lessons for both policies aimed at the pandemic and broader goals with high uncertainty levels: (1) the precautionary approach can be lowest cost, while still expensive; (2) detection and monitoring (e.g. testing and tracking) are integral to a successful precautionary approach; and (3) expecting tradeoffs between economic activity and health creates a false dichotomy – they are complements not substitutes. Pandemic policy should focus on minimizing expected costs and damages rather than attempting to exchange health and safety for economic well-being.
Social distancing measures have been introduced in many countries in response to the COVID-19 pandemic. The rate of compliance to these measures has varied substantially. We study how cultural differences can explain this variance using data on mobility in Swiss cantons between January and May 2020. We find that mobility declined after the outbreak but significantly less in the German-speaking region. Contrary to the evidence in the literature, we find that within the Swiss context, higher generalized trust in others is strongly associated with lower reductions in individual mobility. Additionally, support for a limited role of the state in matters of welfare is also found to be negatively associated with mobility reduction. We attribute our results to a combination of these cultural traits having altered the trade-off between the chance of contracting the virus and the costs associated with significant alterations of daily activities.
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We present a comprehensive analysis of the performance and flows of U.S. actively managed equity mutual funds during the COVID-19 crisis of 2020. We find that most active funds underperform passive benchmarks during the crisis, contradicting a popular hypothesis. Funds with high sustainability ratings perform well, as do funds with high star ratings. Fund outflows largely extend pre-crisis trends. Investors favor funds that apply exclusion criteria and funds with high sustainability ratings, especially environmental ones. Our finding that investors remain focused on sustainability during this major crisis suggests they view sustainability as a necessity rather than a luxury good.
Using survey responses across nearly 500 listed firms in 10 emerging markets from early April, we find the vast majority of firms were negatively affected by COVID-19. Firms reacted by reducing investment rather than payroll. There is a surprising degree of support vis-à-vis employees, customers, other stakeholders and broader society. Although stock prices initially reacted to the impact of the crisis, delayed stock price reactions suggest evidence of inefficient markets. Furthermore, we find evidence that stakeholder-centric firms experienced lower stock price declines during the crisis drawdown.
I examine how financial markets interact with news about the COVID-19 pandemic. A twelve topic model optimizes the trade-off between number of topics and topic coherence. Using this model, I show that before mid-March 2020 markets react more to the same quantum of news when volatility is higher – a phenomenon I call hypersensitivity. Formal tests identify a structural break in mid-March, post which markets are no longer hypersensitive. In the hypersensitive stage, markets are overly volatile and overreact to news. Despite hypersensitivity, lagged prices better forecast future COVID-19 case counts than do lagged news.
Using individual, race-disaggregated, and georeferenced death data collected by the Cook County Medical Examiner, we look at the impact of COVID-19 on African Americans and at its determinants. First, we provide evidence that - as of June 16, 2020 - blacks in Cook County are dying from COVID-19 at a rate 1.3 times higher than their population share. Second, by combining the spatial distribution of mortality with the redlining maps for the Chicago area, we establish that - after the epidemic outbreak - historically lower-graded neighborhoods display a sharper increase in mortality, driven by blacks. Thus, we uncover a persistence influence of the racial segregation induced by the lending practices of the 1930s, by way of a diminished resilience of African Americans to the COVID-19 shock. Such influence is channeled through socioeconomic status and household composition, and magnified in combination with a higher black share.
Covid-19 and the measures taken to contain it have led to unprecedented constraints on work and leisure activities, across the world. This paper uses nationally representative surveys to document how people of different ages and incomes have been affected across six countries (China, South Korea, Japan, Italy, UK and US). We first document changes in economic variables (income and consumption) and leisure. Second, we document self-reported negative and positive non-financial effects of the crisis. We then examine attitudes towards recommendations (wearing a mask in particular) and the approach taken by public authorities. We find similarities across countries in how people of different generations have been affected. Young people have experienced more drastic changes to their lives, and overall they are less supportive of these measures. These patterns are less clear across income groups: while some countries have managed to shield lower income individuals from negative consequences, others have not. We also show that how people have been affected by the crisis (positively or negatively) does little to explain whether or not they support measures implemented by the public authorities. Young people are overall less supportive of such measures independently of how they have been affected.
We analyze the universe of point-of-sale (POS) transactions before and during the COVID-19 lockdown in Mexico. We find three key results. First, consumption in Mexico fell by 23 percent in the April-June quarter of 2020. Second, reductions in consumption were highly heterogeneous across sectors and states, with states and activities related to tourism the most affected. Third, using variation over time and states, we estimate the elasticity of POS expenditures with respect to geographic mobility (measured using cellphone location data) to be slightly less than 1. This estimate suggests that spending in developing countries may be more responsive to mobility than in developed countries, and that mobility indicators could be used as a real-time proxy for consumption in some economies.
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The transmission and incidence of COVID-19 infections differ markedly across areas in the U.S. Using daily infection rates at the county level, this paper explores how population density and the organization of the city correlate to the speed of transmission and shelter-in-place responses (staying home, avoiding travel). Population density is associated with higher transmission speeds in particular at the start of outbreaks. Density is also associated with stronger sheltering responses, but mostly in later phases of the outbreak. There is a considerable additional role of the urban form (i.e., public transport, work-from-home and local incomes), in transmission and sheltering. Over the course of the pandemic, workplace connections are increasingly less likely to predict infection, and phone movement shows that people avoid heavily infected areas. Altogether, this suggests that densely populated places are initially prone to faster viral spread, and later develop stronger sheltering responses. The considerable spatial differences in both the speed of transmission and the mobility responses to local infection could explain differences in the pandemic's toll across cities and counties.
Two decades after the SARS outbreak, Asia is confronted with COVID-19 which has caused a greater economic impact to the region. In this paper, using China and the ASEAN's experiences of SARS and COVID-19 as a case study, we aim to identify the economic impact of a pandemic that is associated with global production linkages. We construct a novel general equilibrium model of production networks with epidemiological dynamics. Calibrating the model with the OECD inter-country input-output tables for the pre-SARS and pre-COVID-19 periods, and controlling for disease dynamics across years, we find that, in the absence of policy intervention, greater importance of China in the global value chains is associated with greater economic impacts, both within China and in the ASEAN region.
Nonpharmaceutical interventions against the spread of SARS-CoV-2 in Germany included the cancellation of mass events (from March 8), closures of schools and child day care facilities (from March 16) as well as a “lockdown” (from March 23). This study attempts to assess the effectiveness of these interventions in terms of revealing their impact on infections over time. Dates of infections were estimated from official German case data by incorporating the incubation period and an empirical reporting delay. Exponential growth models for infections and reproduction numbers were estimated and investigated with respect to change points in the time series. A significant decline of daily and cumulative infections as well as reproduction numbers is found at March 8, March 10 and March 3, respectively. Further declines and stabilizations are found in the end of March. There is also a change point in new infections at April 19, but daily infections still show a negative growth. From March 19, the reproduction numbers fluctuate on a level below one. The decline of infections in early March 2020 can be attributed to relatively small interventions and voluntary behavioural changes. Additional effects of later interventions cannot be detected clearly. Liberalizations of measures did not induce a re-increase of infections. Thus, the effectiveness of most German interventions remains questionable. Moreover, assessing of interventions is impeded by the estimation of true infection dates and the influence of test volume.
The COVID-19 pandemic has put the global economy under a scanner. India has also been impacted by the pandemic and as a result, policymakers have undertaken significant set of measures to address the challenge. In this context, using daily state-level data, we utilize the staggered timing of the implementation of lockdown to ascertain its impact on the number of Covid19 cases. Our analysis appears to suggest that notwithstanding the lockdown, the number of Covid19 cases increased by 80% and furthermore, there was a differential impact across states, depending on their extent of health preparedness. Robustness tests support these findings.
Information is an important policy tool for managing epidemics, but issues with data collection may hinder its effectiveness. Focusing on Covid-19 in Mexico, we ask whether delays in reporting deaths affect individuals’ beliefs and behavior. Leveraging an online survey, we randomly provide information to respondents either accounting or not for delays in death reports. We find that not accounting for delays leads to a lower perceived risk of contagion and intention to comply with social distancing. An equilibrium model incorporating the endogenous behavioral response documented by our intervention illustrates the effect of reporting delays on the evolution of the epidemic
The COVID-19, after hitting hard the developed regions of Europe and the United States, is now fast spreading in relatively less developed regions including the Latin America, South Asia and the African continent. In this paper, we examine the impact of socioeconomic conditions on the health outcomes by COVID-19 and the moderating role of government emergency measures on the relationship between socioeconomic conditions and the health outcomes by COVID-19. Using a panel dataset consisting of 9529 daily observations from 80 countries over the period from January 22 to May 20, 2020, we find that socioeconomic circumstances have strong negative association with COVID-19 confirmed cases and deaths per million people. Quantitatively, a one standard deviation improvement in socioeconomic conditions lowers COVID-19 confirmed cases and deaths per million people by one half. Next, with the help of interaction terms between socioeconomic conditions and government emergency policies, we find that stringent social distancing measures and generous income support programs help to lower the cases and deaths particularly in countries with poor socioeconomic conditions. These findings have important implications to design the right set of government policies to lower the lives losses in countries and regions with poor socioeconomic conditions.
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We use high-frequency indicators to analyze the economic impact of COVID-19 in Europe and the United States during the early phase of the pandemic. We document that European countries and U.S. states that experienced larger outbreaks also suffered larger economic losses. We also find that the heterogeneous impact of COVID-19 is mostly captured by observed changes in people’s mobility, while, so far, there is no robust evidence supporting additional impact from the adoption of non-pharmaceutical interventions. The deterioration of economic conditions preceded the introduction of these policies and a gradual recovery also started before formal reopening, highlighting the importance of voluntary social distancing, communication, and trust-building measures.
COVID-19 hit firms by surprise. In a high frequency, representative panel of German firms, the business outlook declined and business uncertainty increased only when the spread of the COVID-19 pandemic led to domestic policy changes: The announcement of nation-wide school closures on March 13 caused by far the largest change in business perceptions. In contrast, business perceptions hardly reacted to any other potential source of information: Firms did not learn from foreign policy measures, even if they relied on inputs from China or Italy. The local, county-level spread of COVID-19 cases affected expectations and uncertainty, albeit to a much lesser extent than the domestic policy changes.
Fear and imposed isolation worldwide due to the outbreak of the coronavirus disease (COVID-19) have raised alarms about its impact on mental health on a global scale. Associated with the respiratory disease and the public health threat, confinement, isolation, and social distance were presented as the only effective measures to prevent the spread of the virus. For those who already have psychological disorders, it is one additional factor of distress and tension. For those who do not have them, this is, in the overwhelming majority, a whole new situation and a possible cause of anxiety, stress, and depression. Besides, the severe anticipated global recession, following the lockdown of economies resulting from the virus containment strategies, should lead to substantial increases in unemployment rates and indebtedness levels, which are both risk factors for suicide. History has shown us that previous pandemics and recessions harmed population mental health, having a negative impact, namely in suicidal behaviors. The present literature review intends to alert to the prevention of suicide amid the COVID-19 pandemic, based on past similar scenarios of epidemics, such as the Spanish flu and SARS, and recessions, namely the Great Recession, the Asian economic crisis (1997-1998) and the Russian economic crisis (early 1990s). A positive sign was the fact that at the end of March, several organizations and entities worldwide, namely in Portugal, had (already) made available resources to tackle population stress and avoid negative impacts on mental health. Moreover, specialized publications had warned about the possible effects of COVID-19 on suicidal behaviors. Two months after, the subject was still active and alive on the Web. The literature also shows that the recipe to mitigate depressions and suicide behaviors in times of pandemics and recessions seems to be known: investment in mental healthcare, namely suicide prevention services, and in active employment policies.
In order to get the COVID-19 pandemic under control, most governments around the globe have adopted some sort of containment policies. In the light of the enormous costs of these policies, in many countries highly controversial discussions on the adequacy of the chosen policies evolved. We contribute to this discussion by evaluating three waves of containment measures adopted by the German government. Based on a spatio-temporal endemic-epidemic model we show that in retrospective, only the first wave of containment measures clearly contributed to flattening the curve of new infections. However, a real-time analysis using the same empirical model reveals that based on the then available information, the adoption of additional containment measures was warranted. Moreover, our spatio-temporal analysis shows that a one-size-fits-all policy, as it was adopted in Germany on the early stages of the epidemic, is not optimal.
A renewed interest in the ability to work remotely has arisen due to COVID-19. This paper seeks to understand the technological antecedents of that ability. We construct county-level measures of the ability to work at home (wah) using industry and occupation data from a variety of sources, and correlate these with county-level measures of automation and new-task implementation. Empirical results suggest that regions that faced automation produced job opportunities with lower wah, while regions that faced new innovation demonstrate no clear pattern. Finally we show that regions with low wah tend to employ lower-skilled immigrant populations, and have suﬀered higher unemployment due to COVID. Even as many technologies increase the ability of workers to work remotely, automating technologies tend to counter this, raising the potential for the need to shutdown certain industrial centers due to the COVID pandemic.
We use monthly credit card data from the Federal Reserve's Y-14M reports to study the early impact of the COVID-19 shock on the use and availability of consumer credit. First, we find that in counties severely affected by the pandemic, creditworthy borrowers reduce their credit card balances and credit card transactions, while the least creditworthy borrowers increase their outstanding balances. Second, while both local pandemic severity and non-pharmaceutical interventions (NPIs) have a significantly negative effect on credit use, the pandemic itself is the main driver. Third, we report a drastic reduction in credit card originations, which is more pronounced in counties affected by pandemic severity and in counties with more stringent NPIs. Finally, we find a reduction in the credit limits and an increase in the APR spreads of newly issued credit cards to the riskiest borrowers, which is consistent with a “flight-to-safety” response of banks to the COVID-19 shock.
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The scientific community has come together in an unprecedented effort to find a COVID-19 vaccine. However, the success of any vaccine depends on the share of the population that gets vaccinated. We design a survey experiment in which a nationally representative sample of 3,133 adults in the U.S. state their intentions to vaccinate themselves and their children for COVID-19. In the experiment, we account for uncertainty about the severity and infectiousness of COVID-19, as well as inconsistencies in risk communication from government authorities, by varying these factors across treatments. We find that 20% of people in the U.S. would decline the vaccine. General vaccine hesitancy (including not having had a flu shot in the last two years), distrust of vaccine safety, and vaccine novelty are among the most important deterrents to vaccination. Further, our results suggest that inconsistent risk messages from public health experts and elected officials reduce vaccine uptake. We use our survey results in an epidemiological model to explore conditions under which a vaccine could achieve herd immunity. We find that in a middle-of-the-road scenario with central estimates of model parameters, a vaccine will benefit public health by saving many lives but nevertheless may fail to achieve herd immunity.
We propose a simple method based on firms' balance sheets and sectoral predictions of sales growth to determine the firms that will become illiquid month by month as the Covid-19 crisis unfolds. We apply the method to the population of Italian incorporated businesses to the end of 2020. We find that at the peak, around 200,000 companies employing 3.3 million workers would become illiquid. The progression is fast, with 180,000 firms turning illiquid already by April. The liquidity shortage, defined as the "negative" liquidity stock of illiquid firms, amounts to 72 billion. We evaluate the Italian government liquidity decree, which provides guarantees for bank loans under four different facilities of increasing complexity. Assuming that firms have access to all the facilities, almost all firms are able to cover their liquidity shortfalls. The issue is the speed of implementation: the facilities supplying more liquidity are more complex to administrate, and many firms require these facilities to cover their liquidity shortfalls. Overall, we conclude that even in the case of a second wave after the summer, which would increase the liquidity shortfall substantially, firms' liquidity needs are manageable under the current schemes of liquidity provision.
This paper estimates the effect of non-pharmaceutical intervention (NPI) policies on public health during the recent COVID-19 outbreak by considering a counterfactual case for Sweden. Using a synthetic control approach, I find that strict initial lockdown measures played an important role in limiting the spread of the COVID-19 infection and that Swedish policymakers would have eventually reduced the infection cases by more than half had they followed those policies. As people dynamically adjust their behavior in response to information and policies, the impact of NPIs becomes visible with a time lag of around 5 weeks. An alternative difference-in-differences research design that allows for changes in behavioral patterns also confirms the effectiveness of a strict lockdown policy. Finally, extending the analysis to excess mortality, I find that the lockdown measures would have lowered excess mortality in Sweden by 23 percentage points, with a steep age gradient of more than 30 percentage points for the most vulnerable elderly cohort. The outcome of this study can help policymakers lay out future policies to further protect public health, as well as facilitate an economic plan for recovery.
Sharp changes in consumer expenditure may bias inflation during the Covid-19 pandemic. Using public data from debit card transactions, I quantify these changes in consumer spending, update CPI basket weights and construct an alternative price index to measure the effect of the Covid-induced weighting bias on the Swiss consumer price index. I find that inflation was higher during the lock-down than suggested by CPI inflation. The annual inflation rate of the Covid price index was -0.4% by April 2020, compared to -1.1% of the equivalent CPI. Persistent “low-touch” consumer behaviour can further lead to inflation being underestimated by more than a quarter of a percentage point until the end of 2020.
This paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people's voluntarily behavioral response to new information of transmission risks. Our analysis finds that both policies and information on transmission risks are important determinants of Covid-19 cases and deaths and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our counterfactual experiments suggest that nationally mandating face masks for employees on April 1st could have reduced the growth rate of cases and deaths by more than 10 percentage points in late April, and could have led to as much as 17 to 55 percent less deaths nationally by the end of May, which roughly translates into 17 to 55 thousand saved lives. Our estimates imply that removing non-essential business closures (while maintaining school closures, restrictions on movie theaters and restaurants) could have led to -20 to 60 percent more cases and deaths by the end of May. We also find that, without stay-at-home orders, cases would have been larger by 25 to 170 percent, which implies that 0.5 to 3.4 million more Americans could have been infected if stay-at-home orders had not been implemented. Finally, not having implemented any policies could have led to at least a 7 fold increase with an uninformative upper bound in cases (and deaths) by the end of May in the US, with considerable uncertainty over the effects of school closures, which had little cross-sectional variation.
This paper examines the individual records of patients treated for COVID-19 during the early phase of the epidemic in Ontario. We trace out daily transitions of patients through medical care of different intensity and address the right truncation in the database. We also examine the sojourn times and reveal duration dependence in the treatments for COVID-19. The transition model is used to estimate and forecast the counts of patients treated for COVID-19 in Ontario, while accommodating the right truncation and right censoring in the sample. This research is based on the Public Health Ontario (PHO) dataset from May 07, 2020.
I examine the short-term labor market effects of the Great Lockdown in the United States. I analyze job losses by task content (Acemoglu & Autor 2011), and show that they follow underlying trends; jobs with a high non-routine content are especially well-protected, even if they are not teleworkable. The importance of the task content, particularly for non-routine cognitive analytical tasks, is strong even after controlling for age, gender, race, education, sector and location (and hence for differential demand shocks). Jobs subject to higher structural turnover rates are much more likely to be terminated, suggesting that easier-to-replace employees were at a particular disadvantage, even within sectors; at the same time, there is evidence of labor hoarding for more valuable matches. Individuals in low-skilled jobs fared comparatively better in industries with a high share of high-skilled workers.
Using high-frequency panel data for U.S. counties, I estimate the full dynamic response of COVID-19 cases and deaths to exogenous movements in mobility and weather. I find several important results. First, weather and mobility are highly correlated and thus omitting either factor when studying the COVID-19 effects of the other is likely to result in substantial omitted variable bias. Second, temperature is found to have a negative and significant effect on future COVID-19 cases and deaths, though the estimated effect is sensitive to which measure of mobility is included in the regression. Third, controlling for weather, overall mobility is found to have a large positive effect on subsequent growth in COVID-19 cases and deaths. The effects become significant around 2 weeks ahead and persist through around 8 weeks ahead for cases and around 9 weeks ahead for deaths. The peak impact occurs 4 to 6 weeks ahead for cases and around 8 to 9 weeks ahead for deaths. The effects are largest for mobility measured by time spent away from home and time spent at work, though significant effects also are found for time spent at retail and recreation establishments, at transit stations, at grocery stores and pharmacies, and at parks. Fourth, I find that public health non-pharmaceutical interventions affect future COVID-19 cases and deaths, but that their effects work entirely through, and not independent of, individuals' mobility behavior. Lastly, the dynamic effects of mobility on COVID-19 outcomes are found to be generally similar across counties, though there is evidence of larger effects in counties with high cases per capita and that reduced mobility relatively late.
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Most integrated models of the covid pandemic have been developed under the assumption that the policy-sensitive reproduction number is certain. The decision to exit from the lockdown has been made in most countries without knowing the reproduction number that would prevail after the deconfinement. In this paper, I explore the role of uncertainty and learning on the optimal dynamic lockdown policy. I limit the analysis to suppression strategies. In the absence of uncertainty, the optimal confinement policy is to impose a constant rate of lockdown until the suppression of the virus in the population. I show that introducing uncertainty about the reproduction number of deconfined people reduces the optimal initial rate of confinement.
How does the nature of work – teleworkability and contact intensity – shape the distribution of health, labor income, and unemployment risks, created by the COVID-19 pandemic? To answer this question, we consider two contexts. First, we show that the existing spousal nature-of-work-based occupational sorting in the United States matters for the distribution of these risks. In particular, we show that it mitigates the risk of catching COVID-19 through intra-household contagion relative to the case of zero sorting. Furthermore, we show that it creates a larger fraction of couples, who are excessively exposed to labor income and unemployment risks, relative to the case of zero sorting. Second, we document that teleworkable occupations require higher education and experience levels as well as greater cognitive, social, character, and computer skills relative to non-teleworkable occupations. This discrepancy affects labor income and unemployment risks by increasing the likelihood of skill mismatch for newly unemployed workers. Our results imply that the current economic downturn may have long-run effects on employment prospects and earnings of workers who had non-teleworkable or high-contact-intensity jobs at the onset of the COVID-19 outbreak. We discuss the relevant policy implications and associated policy constraints that follow from our findings.
While the SARS-CoV2 pandemic has led to a rapid increase in unemployment across the United States, some states have fared better than others at minimizing economic damage and suppressing the disease burden. We examine the political factors behind these outcomes at the individual and institutional levels. First, using new daily data from the Gallup Panel between March and June on roughly 45,000 individuals, we document that heterogeneity in beliefs about the pandemic and social distancing behaviors is driven primarily by political affiliation. In fact, it is systematically more predictive than factors directly connected to the disease, including age, county infections per capita, and state public health policies. Second, we investigate how partisanship led states to adopt laxer or stricter policies during the pandemic. While the more extreme policies have had negative effects on either economic activity or public health, middle-of-the-road policies (e.g., mask-mandates) have been more effective at curbing infections without significant economic damages. However, the effectiveness of these policies—and compliance with them—is mediated by political affiliation. Our results suggest that partisanship can have persistent effects on economic activity and health beyond its effects on sentiment, moving individuals and institutions away from optimal policy.
This paper compares the performance of safe haven assets during two stressful stock market regimes – the 2008 Global Financial Crisis (GFC) and COVID-19 pandemic. Our analysis across the ten largest economies in the world shows that the traditional choice, gold, acts as a safe haven during the GFC but fails to protect investor wealth during COVID. Our results suggest that investors might have lost trust in gold. Furthermore, silver does not serve as a safe haven during either crisis, while US Treasuries and the Swiss Franc generally act as strong safe havens during both crises. The US dollar acts as a safe haven during the GFC for all the countries except for the United States, but only for China and India during COVID. Finally, Bitcoin does not serve as a safe haven for all countries during COVID; however, the largest stablecoin, Tether, serves as a strong safe haven. Thus, our results suggest that, during a pandemic, investors should prefer liquid and stable assets rather than gold.
Understanding the determinants and implications of delays in reporting COVID-19 deaths is important for managing the epidemic. Contrasting England and Mexico, we document that reporting delays in Mexico are larger on average, exhibit higher geographic heterogeneity, and are more responsive to the total number of occurred deaths in a given location-date. We then estimate simple SIR models for each country to illustrate the implications of not accounting for reporting delays. Our results highlight the fact that low and middle-income countries are likely to face additional challenges during the pandemic due to lower quality of real-time information.
Since the first death in China in early January 2020, the coronavirus (Covid-19) has spread across the globe and dominated the news headlines leading to fundamental changes in the health, social and political landscape, and an unprecedented negative impact on the current and future prospects of households, businesses and the macro-economy. In this paper, we examine consumer spending responses to the onset and spread of Covid-19, and subsequent government imposed lockdown in Great Britain, GB (England, Scotland, Wales). Our sample period spans January 1st 2020 to 7th April 2020. This allows us to observe consumer spending behavior from the initial incubation phase of the crisis. We partition the sample period into incubation (1st-17th January), outbreak (January 18th-February 21st), fever (February 22nd-March 22nd), lockdown (March 23rd–May 10th 2020) and stay alert (May 11th- June 18th) phases. Using a high frequency transaction level proprietary dataset comprising 101,059 consumers and 23 million transactions made available by a financial technology company, we find that discretionary spending declines during the fever period as the government imposed lockdown becomes imminent, and continues to decline throughout the lockdown period. Shortly after the May 10th ‘stay alert’ announcement by Prime Minister Johnson, a short-term decline in spending across all nations occurs. However, a week later, spending is at the same level as that observed prior to the announcement. There is a strong increase in groceries spending consistent with panic buying and stockpiling behaviour in the two weeks following the World Health Organisation (WHO) announcement describing Covid-19 as a pandemic. Variations in the level and composition of consumer spending across nations and regions (particularly during the early stages of the outbreak period), and by age, gender and income level are also observed. Our results are of particular relevance to government agencies tasked with the design, execution and monitoring economic impacts arising from the spread of the virus and the public health measures imposed to mitigate the health costs of the crisis.
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The value of statistical life (VSL) is a risk-to-money conversion factor that can be used to accurately approximate an individual’s willingness-to-pay for a small change in fatality risk. If an individual’s VSL is (say) $7 million, then she will be willing to pay approximately $7 for a 1-in-1-million risk reduction, $70 for a 1-in-100,000 risk reduction, and so forth.
VSL has played a central role in the rapidly emerging economics literature about COVID-19. Many papers use VSL to assign a monetary value to the lifesaving benefits of social-distancing policies, so as to balance those benefits against lost income and other policy costs. This is not surprising, since VSL (known in the U.K. as “VPF”: value of a prevented fatality) has been a key tool in governmental cost-benefit analysis for decades and is well established among economists.
Despite its familiarity, VSL is a flawed tool for analyzing social-distancing policy—and risk regulation more generally. The standard justification for cost-benefit analysis appeals to Kaldor-Hicks efficiency (potential Pareto superiority). But VSL is only an approximation to individual willingness to pay, which may become quite inaccurate for policies that mitigate large risks (such as the risks posed by COVID-19)—and thus can recommend policies that fail the Kaldor-Hicks test.
This paper uses a simulation model of social-distancing policy to illustrate the deficiencies of VSL. I criticize VSL-based cost-benefit analysis from a number of angles. Its recommendations with respect to social distancing deviate dramatically from the recommendations of a utilitarian or prioritarian social welfare function. In the model here, it does indeed diverge from Kaldor-Hicks efficiency. And its relative valuation of risks and financial costs among groups differentiated by age and income lacks intuitive support. Economists writing about COVID-19 need to reconsider using VSL.
We document a decline in mental well-being after the onset of the Covid-19 pandemic in the UK. This decline is more than twice as large for women as for men. We seek to explain this gender gap by exploring gender differences in: family and caring responsibilities; financial and work situation; social engagement; health situation, and health behaviours, including exercise. We discuss two dimensions of gender differences, the extent to which particular circumstances relate to well-being and the share of individuals facing a given circumstance. Overall, we find that differences in family and caring responsibilities can explain a part of the gender gap, but the bulk is explained by social factors such as loneliness. Other factors such as financial difficulties or age are similarly distributed across genders and thus play little role in explaining the gap.
In this paper, we estimate the effect of the 1918 influenza pandemic on income inequality in Italian municipalities. Our identification strategy exploits the exogenous diffusion of influenza across municipalities by infected soldiers on leave from World War I operations at the peak of the pandemic. Our measures of income inequality come from newly digitized historical administrative records on Italian taxpayer incomes. We show that in the short-/medium-run (i.e., after five years), income inequality is higher in Italian municipalities more afflicted by the pandemic. The effect is mostly explained by a reduction in the share of income held by poorer people. Finally, we provide initial evidence that these differences in income inequality persist even after a century.
This paper uses a survey representative of the UK online population to assess the willingness to accept loss of certain goods. We had conducted an initial survey in February, focusing on ‘free’ online goods and some potential substitutes and comparators. Consistent with other contingent valuation studies, consumers on average assigned valuations to many of these goods, particularly when benchmarked against revenue figures for the services. Our pilot studies, discussed in a forthcoming paper, also suggested that the actual valuations are not well anchored, but the methodology can give consistent rankings among goods. It is also a useful way to assess changes in valuations. Repeating the survey in May, during the UK, lockdown, we observed significant changes in the valuations of different goods and services, with some large differences by age and gender. In this sense the lockdown has acted as a natural experiment testing for the extent to which digital goods and physical goods are substitutes. These valuation changes may indicate which services are most valuable in a post-pandemic world where more activity takes place online. They also provide important, policy-relevant insights into distributional questions
The COVID-19 shock and its unprecedented financial consequences have brought about vast uncertainty concerning the future of climate actions. We study the cross-section of stock returns during the COVID-19 shock to explore investors’ views and expectations about environmental issues. The results show that firms with responsible strategies on environmental issues experience better stock returns. This effect is mainly driven by initiatives addressing climate change (e.g., reduction of environmental emissions and energy use), is more pronounced for firms with greater ownership by investors with long-term orientation and is not observed prior to the COVID-19 crisis. Overall, the results indicate that the COVID-19 shock has not distracted investors’ attention away from environmental issues but on the contrary led them to reward climate responsibility to a larger extent.
Despite the COVID-19 pandemic is currently spreading all over the world, we still observe dramatic variation between and, even within, countries in the speed of the infection, in the observed fatality rates and in the effectiveness of the containment measures put in place by most countries. This paper sheds light on the role of culture exploiting the large cultural variation between German and Latin (French and Italian) speaking regions in Switzerland. Consistently with the large difference in social contacts across generations between these two distinct cultural groups, it shows that the disease affected disproportionately elderly people only in Latin regions. Then, it shows that cultural differences are also associated with different levels of compliance with the containment measures put in place by the Swiss government. Mobility data by Google and Apple clearly show that people living in Latin-speaking regions started reducing their movements a week before the lockdown and then complied more strictly than their German counterparts with the policy. This differential compliance across language regions clearly affected the epidemic curves. Using an event study design, we reveal that Latin regions experiencing a faster decline in the growth rate of new cases, hospitalizations and deaths than their German counterpart.
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This paper investigates telework in Japan during the spread of COVID-19. Using unique survey data, we show which occupations are suited to telework. Our results show that the use of telework increased from 6% in January to 10% in March and reached 17% in June 2020, although remarkably the level is still lower than that of other developed countries (e.g. 37% in Europe). Furthermore, we found that some occupations such as services with face-to-face communication are the most unsuitable for telework. They tended to suffer from negative impacts, such as largely reduced incomes and working hours.
We study the market reactions following staggered lockdown events across U.S. states during Covid-19. We find that returns on firms located in lockdown states are higher following the lockdown. We interpret these market reactions as reflecting updated beliefs of market participants in the light of events that follow the lockdowns, such as compliance with stay-at-home orders. The effect is (a) only significant when the firm’s county has a high number of infections, (b) larger for firms in essential industries, and (c) larger for states with Democratic trifecta. While lockdown extension announcements are associated with negative market reactions, the reaction is still positive when the county’s number of infections is high. These findings suggest that the market perceives Non-Pharmaceutical Interventions, when effective, to be beneficial for businesses.
After fitting a topic model to 79,597 COVID-19-related paragraphs in 11,183 conference calls over the period January to April 2020, we obtain measures of firm-level exposure and response to COVID-19 for 3,019 U.S. firms. We show that despite many different ways through which COVID-19 affects their operations, firms with a strong corporate culture do better in the midst of a pandemic than their peers without a strong culture. Moreover, firms with a strong culture are more likely to emphasize community engagement and adopt digital technology, and are no more likely to engage in cost cutting than their peers without a strong culture. To explore the channels through which culture makes firms resilient to the pandemic, we show that firms with a strong culture have higher sales per employee and lower cost of goods sold per employee during the first quarter of 2020. Our results provide support for the notion that corporate culture is an intangible asset designed to meet unforeseen contingencies as they arise (Kreps 1990).
The literature has long documented social capital as a key social determinant of health. However, because personal social interactions are implicated in the spread of viral infections, areas with high levels of social capital may have been especially at risk during the early phases of the COVID-19 pandemic when spread could not be halted by behavioral changes. We analyzed data from US counties on laboratory-confirmed COVID-19 cases and COVID-19 deaths and relate county level social capital with the number of days it took a county to reach 10 or 15 cases (from January 22) and with case fatality rate in the county between January 22 and May 8 2020. From January 22 on average it took 68 days for a county to reach at least 10 COVID-19 cases. Disease spread was faster the higher the social capital in a community. In counties with average levels of social capital 10 cases were identified by March 29, but in counties with social capital one SD above the average 10 cases were identified by March 26. The difference is equivalent to the difference estimated across two counties that differ in population density by 12,000 people per square mile. Other things being equal we estimate lower case fatality in higher social capital counties, with a reduction of between 0.2% and 0.4% points per SD difference in social capital. As governments lift mandatory social distancing, social capital may play a key role as a social determinant of health.
The COVID-19 pandemic has led to considerable changes in retail shopping. There has been a significant increase in online shopping compared to on-premise, but due to social distancing and other safety regulations, there have also been significant changes in how on-premise retail is conducted. Prior studies have demonstrated significant effects on product-market concentration from the move to more online shopping, but here we focus on the effects on concentration due to the common change of moving from self-service stores to counter-service. Using a pre-COVID field experiment of a move in the opposite direction, our results suggest that an increase in counter-service shopping is likely to increase product-market concentration, potentially overwhelming the opposite change from the move to online shopping.
This paper shows that the labour market opportunities available to an agent has a significant bearing on how that agent experiences the outbreak of an epidemic. I consider two types of labour (i) market labour that can only produce output in close physical proximity, and (ii) remote labour that can produce output at a distance. This paper develops a Two Agent New Keynesian model extended to include an epidemic bloc and dual feedback between economic decisions and the evolution of the epidemic. I show that an agent restricted to only supply market labour experiences higher death rates vis-à-vis their share of the population, and suffers larger declines in labour and consumption over the course of the epidemic. Post-epidemic, these agents are significantly worse off than their counterparts who can work from home and hence a more unequal society emerges. I then show that simple containment policies, while leading to larger losses in economic prosperity as measured by output loss, can significantly reduce death rates across the population, bring the death rates of the two groups closer together, and reduce the inequality that emerges post epidemic.
What is the role of cultural goods and services during a universal pandemic crisis? This paper aims to demonstrate that culture is predominantly a public good for preserving mental health. We argue that the function of culture in human life has evolutionary roots in individual self-defence of mental health from uncertainty. The current paper uses primary data from a pilot survey conducted during the pandemic COVID-19 combined with Google trends data used to illustrate the effect of the pandemic on aggregate level. Our outcome variables are happiness during COVID-19 and propensity to help others in the periods before and after the start of the pandemic. The evidence from Probit and Heckman sample selection models suggests that people can obtain a mental-health shield for crisis periods through consumption of cultural goods and services in the past. Meanwhile, spontaneous cultural practices during times of uncertainty (such as singing with others) are associated with higher pro-social propensity to help other people. This shows that on micro-level culture is generally under-estimated in its potential role as a public good guaranteeing the psychological resilience in socio-economic shocks. On aggregate level, data about public spending on culture is associated with lower anxiety and less viral fear of death. Therefore, culture should be seriously explored as a tool for mental health prevention, which would be a primary justifications for much more extensive public spending on culture.
The spread of novel coronavirus (COVID-19) infections has led to substantial changes in consumption patterns. While demand for services that involve face-to-face contact has decreased sharply, online consumption of goods and services, such as through e-commerce, is increasing. The aim of this study is to investigate whether online consumption will continue to increase even after COVID-19 subsides, using credit card transaction data. Online consumption requires upfront costs, which have been regarded as one of the factors inhibiting the diffusion of online consumption. However, if many consumers made such upfront investments due to the coronavirus pandemic, they would have no reason to return to offline consumption after the pandemic has ended, and high levels of online consumption should continue.
Our main findings are as follows. First, the main group responsible for the increase in online consumption are consumers who were already familiar with online consumption before the pandemic and purchased goods and service both online and offline. These consumers increased the share of online spending in their spending overall and/or stopped offline consumption completely and switched to online consumption only. Second, some consumers that had never used the internet for purchases before started to use the internet for their consumption activities due to COVID-19. However, the share of consumers making this switch was not very different from the trend before the crisis. Third, by age group, the switch to online consumption was more pronounced among youngsters than seniors. These findings suggest that it is not the case that during the pandemic a large number of consumers made the upfront investment necessary to switch to online consumption, so a certain portion of the increase in online consumption is likely to fall away again as COVID-19 subsides.
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We study the spread of SARS-CoV-2 infections and COVID-19 deaths by county poverty level in the US. We first document a U-shaped relationship between county groupings by poverty level and the intensity of coronavirus events defined as either coronavirus infections or COVID-19 related deaths. The U-shaped relationship prevails for counties with high population density while in counties with low population density, poorer counties exhibit much higher numbers in coronavirus cases, both in infections and deaths. Second, we investigate the patterns of coronavirus events following the announcements of state level stay-at-home mandates. We distinguish between four groups of states: First, Second, Third and Late Movers. Among First Movers—also the states with the largest share of infections—we observe a decrease in the average number of weekly new cases in rich and poor counties two weeks following the mandate announcement. The average numbers of cases per week in richer counties then quickly converges to the number reported in middle income counties, while the poorer counties show a much slower decrease in coronavirus cases. This pattern is accompanied by a dramatic reduction in mobility in all county groupings. Third, comparing counties in Second and Third Mover states, we show that a few days of delay in non-pharmaceutical interventions (NPIs) results in significantly larger numbers of coronavirus cases compared to states that introduce a mandate quicker. Finally, we use weather shocks as instruments to address endogeneity of the announce
We study planned price changes in German firm-level survey data to infer the relative importance of supply and demand during the Covid-19 pandemic. Supply and demand forces coexist, but demand deficiencies dominate in the short run. Quarter-on-quarter producer price inflation is predicted to decline by as much as 1.5 percentage points through August 2020. These results imply a role for demand stimulus policy to buffer the Covid-19 economic crisis.
We measure the effect of lockdown policies on employment and GDP across countries using individual- and sector-level data. Employment effects depend on the ability to work from home, which ranges from about half of total employment in rich countries to around 35% in poor countries. This gap reflects differences in occupational composition, self-employment levels, and individual characteristics across countries. GDP effects of lockdown policies also depend on countries' sectoral structure. Losses in poor countries are attenuated by their higher value-added share in essential sectors, notably agriculture. Overall, a realistic lockdown policy implies GDP losses of 20-25% on an annualized basis.
We document the magnitudes of and mechanisms behind socioeconomic differences in travel behavior during the COVID-19 pandemic. We focus on King County, Washington, one of the first places in the U.S. where COVID-19 was detected. We leverage novel and rich administrative and survey data on travel volumes, modes, and preferences for different demographic groups. Large average declines in travel, and in public transit use in particular, due to the pandemic and related policy responses mask substantial heterogeneity across socioeconomic groups. Travel intensity declined considerably less among less-educated and lower-income individuals, even after accounting for mode substitution and variation across neighborhoods in the impacts of public transit service reductions. The relative inability of less-educated and lower-income individuals to cease commuting explains at least half of the difference in travel responses across groups.
In response to the surge of Covid-19 cases nations focused on reducing mobility to contain transmission of the virus. This change in mobility patterns can be achieved through two channels; (1) voluntary reductions in mobility due to rising public awareness and (2) explicit social distancing policies imposed by governments. In India, two weeks prior to the national lockdown imposed by the central government on 24th March, state governments had started independently enacting social distancing measures. However, there is little empirical evidence on the efficacy of the initial state-level restrictions, in comparison to the national lockdown. Even fewer studies have commented on the role of public awareness in reducing mobility. This paper contributes by comparing the impact of two policy events on mobility: the first Covid-19 social distancing policy imposed by each state and the imposition of the lockdown. We further explore how the news of the first reported case in a state impacted public awareness. The above effects were estimated by using an event study Difference-in-Differences model with time-varying treatment. Results show that while people did seek information in response to the perception that Covid-19 had ‘reached’ their state, they did not reduce out-of-home mobility significantly. However, starting from the second day after the lockdown, time spent in residence increased significantly for each day by 3-4% for the next 21 days. This is in sharp contrast to the insignificant effect of states’ own first policy on mobility. The intervention of the central government had a much larger and persistent impact on mobility than the initial state-level policies, indicating that a unified, coordinated policy intervention is more effective than isolated, subnational efforts.
How should unemployment benefits vary in response to the economic crisis induced by the COVID-19 pandemic? We answer this question by computing the optimal unem- ployment insurance response to the COVID-induced recession. We compare the optimal policy to the provisions under the CARES Act-which substantially expanded unemployment insurance and sparked an ongoing debate over further increases-and several alternative scenarios. We find that it is optimal first to raise unemployment benefits but then to begin lowering them as the economy starts to reopen - despite unemployment remaining high. We also find that the $600 UI supplement payment implemented under CARES was close to the optimal policy. Extending this UI supplement for another six months would hamper the recovery and reduce welfare. On the other hand, a UI extension combined with a re-employment bonus would further increase welfare compared to CARES alone, with only minimal effects on unemployment.
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While they are rare, superspreading events (SSEs), wherein a few primary cases infect an extraordinarily large number of secondary cases, are recognized as a prominent determinant of aggregate infection rates (R0). Existing stochastic SIR models incorporate SSEs by fitting distributions with thin tails, or finite variance, and therefore predicting almost deterministic epidemiological outcomes in large populations. This paper documents evidence from recent coronavirus outbreaks, including SARS, MERS, and COVID-19, that SSEs follow a power law distribution with fat tails, or infinite variance. We then extend an otherwise standard SIR model with the fat-tailed power law distributions, and show that idiosyncratic uncertainties in SSEs will lead to large aggregate uncertainties in infection dynamics, even with large populations. That is, the timing and magnitude of outbreaks will be unpredictable. While such uncertainties have social costs, we also find that they on average decrease the herd immunity thresholds and the cumulative infections because per-period infection rates have decreasing marginal effects. Our findings have implications for social distancing interventions: targeting SSEs reduces not only the average rate of infection (R0) but also its uncertainty. To understand this effect, and to improve inference of the average reproduction numbers under fat tails, estimating the tail distribution of SSEs is vital.
Social distancing is important to slow the community spread of infectious disease, but it creates enormous economic and social cost. It is thus important to quantify the benefits of different measures. We study the ban of mass gatherings, an intervention with comparably low cost. We exploit exogenous spatial and temporal variation in NBA and NHL games, which arise due to the leagues' predetermined schedules, and the suspension of the 2019-20 seasons. This allows us to estimate the impact of these mass gatherings on the spread of COVID-19 in affected US counties. One additional mass gathering increased the cumulative number of COVID-19 deaths in affected counties by 13 percent.
Since late 2019, Covid-19 has devastated the global economy, with indirect implications for the environment. As governments’ prioritized health and implemented measures such as isolation, the closure of non-essential businesses and social distancing, many workers lost their jobs, were furloughed, or started working from home. Consequently, the world of work has drastically transformed and this period is likely to have major implications for mobility, transportation and the environment. We have estimated the variability of people to engage in remote work and social distancing using O*NET data and Irish Census data. We show that while those who commute by car have a relatively high potential for remote work, they are less likely to be able to engage in social distancing in their workplace. While this may be negative for employment prospects in the short run, this dynamic has the potential for positive environmental implications in the short and long run.
We apply the SIR-macro model proposed by Eichenbaum et al. (2020) in its complete version to comparatively study the interaction between economic decisions and COVID-19 epidemics in five different Brazilian states: São Paulo (SP), Amazonas (AM), Ceará (CE), Rio de Janeiro (RJ) and Pernambuco (PE). Our objective is to analyze qualitatively how the main intrinsic differences of each of these states can affect the epidemic dynamics and its consequences. For this purpose, we compute and compare the model for each of the states, both in competitive equilibrium and under optimal containment policy adoption, and analyze the implications of optimal policy adoption. We conclude that the intrinsic characteristics of the five different states imply relevant differences in the general dynamics of the epidemic, in the optimal containment policies, in the effect of the adoption of these policies and the severity of the economic recessions. Our study can serve as an alert for policymakers of countries of huge dimensions and interstate heterogeneity as Brazil for the necessity of discriminating policies by states or regions instead of adopting a single unified policy for the whole country.
This paper uses a macroeconomic model to analyse the transmission of the COVID19-pandemic and its associated lockdown and quantify the stabilising effects of the economic policy response. Our simulations identify firm liquidity problems as crucial for shock propagation and amplification. We then quantify the effects of short-term work allowances and liquidity guarantees - central policy strategies in the European Union. The measures reduce the output loss of COVID19 and its associated lockdown by about one fourth. However, they cannot prevent a sharp but temporary decline in production.
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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.
Liquidity restrictions on investors, like the redemption gates and liquidity fees introduced in the 2016 money market fund (MMF) reform, are meant to improve financial stability during crisis. However, we find evidence that they may have exacerbated the run on prime MMFs during the Covid-19 crisis. Severe outflows from prime MMFs amid frozen short-term funding markets led the Federal Reserve to intervene with the Money Market Mutual Fund Liquidity Facility (MMLF). By providing “liquidity of last resort,” the MMLF successfully stopped the run on prime MMFs and gradually stabilized conditions in short-term funding markets.
Covid-19 induced job losses occurred predominantly in industries with intensive worker-client interaction as well as in pink-collar and blue-collar occupations. We study the ability of fiscal policy to stabilize employment by occupation and industry during the Covid-19 crisis. We use a multi-sector, multi-occupation macroeconomic model and investigate different fiscal policy instruments that help the economy recover faster. We show that fiscal stimuli foster job growth for hard-hit pink-collar workers, whereas stimulating blue-collar job creation is more challenging. A cut in labor taxes performs best in stabilizing total employment and the employment composition.
The COVID-19 pandemic has resulted in over 2 billion people in the world affected by lockdowns. This has significant socioeconomic implications, especially in areas such as crime, where police resources are diverted from crime prevention towards enforcing lockdowns. Also, mobility restrictions imposed by lockdowns might make it harder for criminals to find victims. The net effect of these opposite forces is unknown. This study analyzes the effect of lockdowns on criminal activity in the state of Bihar, India. A sharp regression discontinuity design is implemented harnessing the sudden introduction of a state-wide lockdown and novel high-frequency criminal case data. The results show that lockdown decreases aggregate crime by 44 percent. Negative large effects are observed in diverse types of crimes such as murder (61 percent), theft (63 percent), and crimes against women (64 percent), among others. This seems to be driven by the higher search costs faced by criminals. Finally, by exploiting geographic variation in terms of lockdowns' severity across districts, this study shows that relaxing lockdowns' initial restrictions increase crime, but the increment is lower in less restrictive lockdowns than in restrictive ones. While economically-motivated crimes increased, violent crimes were not impacted. This suggests that the economic downturn produced by the lockdown might be driving these effects. Policy recommendations are discussed.
This paper develops a choice-theoretic equilibrium model of the labor market in the presence of a pandemic. It includes heterogeneity in productivity, age and the ability to work at home. Worker and firm behavior changes in the presence of the virus, which itself has equilibrium consequences for the infection rate. The model is calibrated to the UK and counterfactual lockdown measures are evaluated. We find a different response in both the evolution of the virus and the labor market with different degrees of severity of lockdown. We use these insights to make a labor market policy prescription to be used in conjunction with lockdown measures. Finally we find that, while the pandemic and ensuing policies impact the majority of the population negatively, consistent with recent studies, the costs are not borne equally. While the old face the highest health risks, it is the young low wage workers who suffer the most income and employment risk.
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We document a number of striking features about the initial impact of the pandemic on local commerce across 16 US cities. There are two novel contributions from this analysis: exploration of neighborhood-level effects and shifts between offline and online purchasing channels. In our analysis we use approximately 450 million credit card transactions per month from a rolling sample of 11 million anonymized customers between October 2019 and March 2020. Across the 16 cities we profile, consumers decreased spend on the set of goods and services we define as ``local commerce" by 12.8% between March 2019 and March 2020. Growth in all 16 cities was negative. Consumers shifted a substantial share of local commerce spend online, such that year over-year growth in online spend was small, but positive, at 1.5%. With respect to grocery and pharmacy purchases, online spend grew at least three times as fast as offline spend. Overall spend declines were uniform across neighborhoods of differing median household income, though lower-income neighborhoods experienced the highest proportion of extreme negative declines. We also find evidence that many low-income neighborhoods are increasing spend on online grocery slower than others, but increasing their use of online restaurants the fastest. Consumers in low-income neighborhoods also tend to live farther from the grocery stores at which they shop. Compared to their counterparts in higher-income neighborhoods, consumers in low-income neighborhoods have not been more likely to shop at grocery stores closer to where they live since the onset of the pandemic.
During the COVID-19 crisis, while the world economy suffered the worst crisis since the Great Depression, the reactions of stock markets have raised concerns. Several economists (including some Nobel laureates) have seen these reactions as evidence that stock markets are not fully efficient, while others have emphasized the difficulty of assessing the dramatic flow of information about the pandemic and its economic consequences. In this paper, we assess how stock markets have integrated public information about the COVID-19, the subsequent lockdowns and the policy reactions. Although the COVID-19 shock has been global, not all countries have been impacted in the same way, and they have not reacted in the same way. We take advantage of this strong heterogeneity. We consider a panel of 74 countries with daily information about the health and economic crisis, from January to April 2020. Stock market reaction can be summarized as follows. 1) Stock markets initially ignored the pandemic (until Feb. 21), before reacted strongly to the growing number of infected people (Feb. 23 to Mar. 20), while volatility surged and concerns about the pandemic arose; following the intervention of central banks (Mar. 23 to Apr. 30), however, shareholders no longer seemed troubled by news of the health crisis, as prices rebound all around the world. 2) Country-specific characteristics appear to have had no influence on stock market response. 3) Investors were sensitive to the number of COVID-19 cases in neighbouring and wealthy countries. 4) Credit facilities and government guarantees, lower policy interest rates, and lockdown measures mitigated the decline in domestic stock prices. Overall, these results suggest that stock markets have been less sensitive to each country’ macroeconomic fundamentals prior the crisis, than to their short-term reaction during the crisis. However, our selected variables explain only a small part of the stock market variations, so it is hard to deny that the link between stock price movements and fundamentals have been anything other than loose.
Evidence from past economic crises indicates that recessions often affect men’s and women’s employment differently, with a greater impact on male-dominated sectors. The current COVID-19 crisis presents novel characteristics that have affected economic, health and social phenomena over wide swaths of the economy. Social distancing measures to combat the spread of the virus, such as working from home and school closures, have placed an additional tremendous burden on families. Using new survey data collected in April 2020 from a representative sample of Italian women, we analyse jointly the effect of COVID-19 on the working arrangements, housework and childcare of couples where both partners work. Our results show that most of the additional workload associated to COVID-19 falls on women while childcare activities are more equally shared within the couple than housework activities. According to our empirical estimates, changes to the amount of housework done by women during the emergency do not seem to depend on their partners’ working arrangements. With the exception of those continuing to work at their usual place of work, all of the women surveyed spend more time on housework than before. In contrast, the amount of time men devote to housework does depend on their partners’ working arrangements: men whose partners continue to work at their usual workplace spend more time on housework than before. The link between time devoted to childcare and working arrangements is more symmetric, with both women and men spending less time with their children if they continue to work away from home. For home schooling, too, parents who continue to go to their usual workplace after the lockdown are less likely to spend greater amounts of time with their children than before. Finally, analysis of work-life balance satisfaction shows that working women with children aged 0-5 are those who say they find balancing work and family more difficult during COVID-19. The work-life balance is especially difficult to achieve for those with partners who continue to work outside the home during the emergency.
Using longitudinal microdata for the UK over the period 2009-2020 we control for pre-existing previous trends in mental health in order to isolate and quantify the effects of the Covid-19 pandemic. Mental health in the UK worsened by 8.1% on average as a result of the pandemic and by much more for young adults and for women which are groups that already had lower levels of mental health before Covid-19. Hence inequalities in mental health have been increased by the pandemic. Even larger effects are observed for measures of mental health that capture the number of problems reported or the fraction of the population reporting any frequent or severe problems, which more than doubled.
This paper uses novel and comprehensive data on electronic payments from SIBS, the main provider of point of sale terminals and on-line payments in Portugal, to study the impact of the Great Lockdown on purchases. The data aggregates all individual transactions into monthly observations, by municipality and sector, between 2018 and 2020. We employ a difference-in-differences event study that relies on the assumption that the monthly evolution of purchases in the first four months of 2020 would be parallel to that of the two previous years. We identify a massive causal impact on overall purchases, from a baseline year-on-year monthly growth rate of 10% to a decrease of 45%. The sign and magnitude of the impact varies considerably across sectors. Purchases of essential goods such as supermarkets and groceries increase mildly, contrasting with severe contractions in sectors that were closed by government order or depend heavily on tourism, including the leisure industry and restaurants. We find suggestive evidence of initial stockpiling of goods, postponing of essential expenditures, and rapid recovery of purchases in tech and entertainment, possibly to adapt to the confinement. Transactions with foreign-owned cards cause an even greater negative contraction. We disentangle the total effect into the intensive margin of the average transaction and the extensive margin of the number of transactions. Buyers adjust their shopping strategies in rational ways to minimize public health risks: they go less often to supermarkets and buy more each time, and visit local groceries more.
Reminders to promote social distancing have been ubiquitous throughout the COVID-19 crisis, but little is known about their effectiveness. Existing studies find positive impacts on intentions to comply, but no evidence exists of actual behavioural change. We conduct a randomised controlled trial with a representative sample of Danish residents, who receive different versions of a reminder to stay home as much as possible at the height of the crisis. We measure impacts on both intentions to comply and on actions in the following days (i.e., whether subjects report having stayed home in a follow-up survey). We find that the reminder significantly increases people’s intentions to stay home when it emphasises the consequences of non-compliance for the respondent or his/her family, while it has no impact when the emphasis is on other people or the country as a whole. Changes in intentions, however, translate into weaker changes in actions that are not statistically significant, despite potential concerns of self-reported compliance being overstated. This is consistent with the existence of important intention-to-action gaps. Only people who are in relatively poor health are significantly more likely to stay home after receiving the reminder with an emphasis on personal and family risks. This shows that while reminders may be useful to protect groups at risk by increasing their own compliance with social distancing, such a tool has no significant impact on the behaviour of those who face limited personal risks but could spread the disease.
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We analyze the impact of the COVID-19 pandemic and government policies on firms' aid take-up, layoff and furlough decisions using newly collected survey data for 10,642 small, medium and large Danish firms. This is the first representative sample of firms reporting the pandemic's impact on their revenue and labor choices, showing a steep decline in revenue and a strong reported effect of labor aid take-up on lower job separations. First, we document that relative to a normal year, a quarter more firms have experienced revenue declines exceeding 35 percent. Second, we characterize the firms that took up aid and the type of aid package they chose - labor-based aid, fixed cost support or fiscal-based tax delays. Third, we compare their actual layoff and furlough decisions with reported counterfactual decisions in the absence of aid.
We study liquidity conditions in the corporate bond market since the onset of the COVID-19 pandemic. We find that in mid-March 2020, as selling pressure surged, dealers were wary of accumulating inventory on their balance sheets, perhaps out of concern for violating regulatory requirements. As a result, the cost to investors of trading immediately with a dealer surged. A portion of transactions migrated to a slower, less costly process wherein dealers arranged for trades directly between customers without using their own balance sheet space. Interventions by the Federal Reserve appear to have relaxed balance sheet constraints: soon after they were announced, dealers began absorbing inventory, bid-ask spreads declined, and market liquidity started to improve. Interestingly, liquidity conditions improved for bonds that were eligible for the Fed’s lending/purchase programs and for bonds that were ineligible. Hence, by allowing dealers to unload certain assets from their balance sheet, the Fed’s interventions may have helped dealers to better intermediate a wide variety of assets, including those not directly targeted.
Latin American countries introduced rapid emergency measures to sustain the income of informal workers and their families during shelter-in-place orders to contain COVID-19. The effectiveness of these measures is limited. The coverage and replacement rates of usual labor income are high among the first quintile of the population but fairly low in the second and third quintiles, where a substantial fraction of households are informal and have limited ability to telework. If governments plan to extend lockdown measures or reintroduce them in the future, they might need to consider broader income transfers for the lower-middle class.
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%.
How do optimal policies to control the spread of SARS-CoV-2 vary across countries? In an influential recent paper, Eichenbaum, Rebelo, and Trabandt (2020) incorporate economic behavior into a standard epidemiological model calibrated to the United States, finding that spontaneous social distancing will fall short of the social optimum without policy intervention. In this paper, we apply and extend their model to explore how optimal policy varies across contexts depending on demography, comorbidities, and health system strength -- which affect the infection fatality rate -- as well as poverty -- which affects agents' willingness to forego current consumption to reduce disease risk. Calibrating the model to Uganda, we calculate an optimal path for a containment policy equivalent to a 4% consumption tax over one year (compared to a 40% tax in the U.S.), which reduces predicted mortality by roughly 5.5% (compared to 28.2% in the U.S.). Notably, the normative predictions of the model constitute poor positive predictions. Actual containment policies in Uganda and many other developing countries with high poverty and favorable demographics have been relatively severe, and have been met with broad public approval despite high economic costs. Within the model, widespread overestimation of the risk of contracting and dying from the disease provides one possible explanation for this pattern.
Many countries are taking measures stopping productive activities to slow down the spread of COVID-19. At times these measures have been criticized as being excessive and too costly. In this paper we make an attempt to understand the optimal response to an infectious disease. We find that the observed policies are very close to a simple welfare maximization problem of a planner who tries to stop the diffusion of the disease. These extreme measures seem optimal in spite of the high output cost that it may have in the short run, and for various curvatures of the welfare function. The desire for cost smoothing makes more likely that either mitigation or no intervention strategies are optimal, but it does not greatly affect the optimal duration and intensity of the quarantines. We then study the possibility of either complementing or substituting the quarantine policy with random testing. We find that testing is a very close substitute of quarantine and can substantially reduce the need for indiscriminate quarantines.
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Since the start of the ongoing coronavirus pandemic, the relationship between national female leaders and their effectiveness in handling the COVID-crisis has received a lot of media attention. In this paper we scrutinise this association more systematically. We ask if there is a significant and systematic difference by gender of the national leader in the number of COVID-cases and deaths in the first quarter of the pandemic. We also examine differences in policy responses by male vs. female leaders as plausible explanations for the differences in outcomes. Using a constructed dataset for 194 countries, a variety of socio-demographic variables are used to match nearest neighbours. Our findings show that COVID-outcomes are systematically better in countries led by women and, to some extent, this may be explained by the proactive and coordinated policy responses adopted by them. We use insights from behavioural studies and leadership literature to speculate on the sources of these differences, as well as on their implications. Our hope is that this article will serve as a starting point to illuminate the discussion on the influence of national leaders in explaining the differences in country COVID-outcomes.
We estimate the impact of non-pharmacological interventions (NPIs) on COVID-19 deaths in Scandinavia. We exploit policy variation between Denmark and Norway on the one hand and Sweden on the other. The former deployed relatively more stringent lockdowns, the latter did not. Our difference-in-differences models show that stricter lockdown policies are associated with fewer COVID-19 deaths.
This paper derives a Model-Inferred DIStancing (MIDIS) measure using an extended version of the Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) framework. The paper argues that, when a disease has an incubation period, explicitly accounting for the exposed compartment is necessary in this class of epidemiological models. An important advantage of the proposed identification strategy lies in its ease to put into practice by other researchers because it employs a relatively simple model and readily available data. When MIDIS is taken to data, results exhibit cross-country and over-time heterogeneity in social distancing during the COVID-19 pandemic. Furthermore, MIDIS is highly correlated with the mobility data, and it embeds both governmental and behavioral responses to the COVID-19 pandemic. Finally, as an application, the paper uses MIDIS to explain output losses experienced during the pandemic, and there exists a robust positive correlation between the two|with sizable economic effects.
We nowcast the economic effects of the Covid-19 pandemic and related lockdown measures in the UK and then analyse the distributional and budgetary effects of the estimated individual income shocks, distinguishing between the effects of automatic stabilisers and those of the emergency policy responses. Under conservative assumptions about the exit strategy and recovery phase, we predict that the rescue package will increase the cost of the crisis for the public budget by an additional £26 billion, totalling over £60 billion. However, it will allow to contain the reduction in the average household disposable income to 1 percentage point, and will reduce poverty rate by 1.1 percentage points (at a constant poverty line), with respect to the pre-Covid situation. We also show that this progressive effect is due to the increased generosity of Universal Credit, which accounts for only 20% of the cost of the rescue package.
Countries across the world responded to the COVID-19 pandemic with what might well be the set of biggest state-led mobility and activity restrictions in the history of mankind. But how effective were these measures across countries? Compared to multiple recent studies that document an association between such restrictions and the control of the contagion, we use an instrumental variable approach to estimate the causal effect of these restrictions on mobility, and the growth rate of confirmed cases and deaths attributed to COVID-19. Using the level of stringency in the rest of the world to predict the level of stringency of the restriction measures in a country, we show while stricter contemporaneous measures affected mobility, stringency in seven to fourteen days mattered for containing the contagion. Heterogeneity analysis reveal that even though the restrictions reduced mobility more in relatively less-developed countries, the causal effect of a reduction in mobility was higher in more developed countries. We propose several explanations. Our results highlight the need to complement mobility and activity restrictions with other health and information measures, especially in less-developed countries, to combat the COVID-19 pandemic effectively.
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. We exploit within country variation in social capital and Covid-19 cases to show that
high-social-capital areas accumulated between 12% and 32% fewer Covid-19 cases per capita 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.
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After an initial period of crisis management, governments must consider what measures against the spread of the novel coronavirus to keep in place until a vaccine or reliable therapy arrives. Informing public policy requires understanding not only disease dynamics and social distancing effectiveness, but also economic features to evaluate the costs and benefits of different actions. This study adapts a workhorse epidemiological model to account for both age-dependent risks and job-dependent social distancing measures and costs. Simulations calculate the costs of six different degrees of restrictions, with sensitivity analysis to several uncertain underlying disease parameters. A novel contribution is contrasting private cost-benefit calculations with the external costs and benefits to society as a whole. The least-cost policy likely involves continued isolation of all who can work or study at home, while other workers practice strong social distancing. For the US, this strategy saves on the order of $10 trillion as compared to simply isolating vulnerable individuals. The benefits of requiring other workers to stay at home only outweigh the wage losses if social distancing measures are insufficiently effective. Immunity is a critical parameter and its absence dramatically increases the costs of weak actions. Further research into the nonmonetary costs of isolation would be valuable. The value of the risks a single person can impose on the rest of society by not staying at home can be substantial, generally increases as restrictions loosen, and should be weighed against the private benefits of returning to circulation.
This paper estimates the drop in profits and the equity shortfall triggered by the Covid-19 shock and the subsequent lockdown, using a representative sample of 80,972 Italian firms. We find that a 3-month lockdown entails an aggregate yearly drop in profits of €170 billion, with an implied equity erosion of €117 billion for the whole sample, and €31 billion for firms that became distressed, i.e., ended up with negative book value after the shock. As a consequence of these losses, about 17% of the sample firms, whose employees account for 8.8% of total employment in the sample (about 800,000 employees), become distressed. Small and mediumsized enterprises (SMEs) are affected disproportionately, with 18.1% of small firms, and 14.3% of medium-sized ones becoming distressed, against 6.4% of large firms. The equity shortfall and the extent of distress are concentrated in the Manufacturing and Wholesale Trading sectors and in the North of Italy. Since many firms predicted to become distressed due to the shock had fragile balance sheets even prior to the Covid-19 shock, restoring their equity to their pre-crisis levels may not suffice to ensure their long-term solvency.
We adapt a SEIRD differential model with asymptomatic population and Covid deaths, which we call SEAIRD, to simulate the evolution of COVID-19, and add a control function affecting both the diffusion of the virus and GDP, featuring all direct and indirect containment policies; to model feasibility, the control is assumed to be a piece-wise linear function satisfying additional constraints. We describe the joint dynamics of infection and the economy and discuss the trade-off between production and fatalities. In particular, we carefully study the conditions for the existence of the optimal policy response and its uniqueness. Uniqueness crucially depends on the marginal rate of substitution between the statistical value of a human life and GDP; we show an example with a phase transition: above a certain threshold, there is a unique optimal containment policy; below the threshold, it is optimal to abstain from any containment; and at the threshold itself there are two optimal policies. We then explore and evaluate various profiles of various control policies dependent on a small number of parameters.
We analyze government interventions to support firms facing liquidity needs during a lockdown in a competitive model of financial intermediation. Banks and firms have legacy balance sheets at the lockdown date. Firms' liquidity needs can be financed by banks that are subject to risk-weighted capital requirements and funded with insured deposits. An increase in firms' overall claims to external investors aggravates moral hazard problems and reduces expected output. The government can support firms directly through transfers or indirectly through guarantees to new bank loans or reductions in the capital requirement. As a result of the diversification of idiosyncratic firm risks conducted by banks, a reduction in the capital requirement only creates costs for the government following negative aggregate shocks that lead to banks' failure. A pecking order on the government policies that maximize output as a function of the government's budget is derived. For low budget, a reduction in capital requirements is optimal and is fully transmitted to firms through increases in banks' leverage. For medium budget, the capital requirement reduction becomes slack and needs be combined with transfers to firms or loan guarantees. For high budget, transfers are strictly necessary.
The health, economic and security impacts of the Covid-19 pandemic are playing out in volatile and potentially catastrophic ways, especially in conflict-affected states. The disease arrived in India during a period of heightened public protests, riots and religious polarization. In this paper, I document early evidence of the causal impact of Covid-19 proliferation on conflict risks across Indian districts. I use text-mining of conflict event descriptions to define two new measures of religious and pandemic-related conflict in addition to the standard measures of real-time conflict events provided by The Armed Conflict Location & Event Data Project (ACLED). Event study analysis indicates a sustained decline in conflict after the first Covid-19 case is reported, driven by a decrease in religious conflict and public protests. However, I also document a countervailing increase in the probability of Covid-19 related conflict. Poor districts and districts with low health infrastructure in particular demonstrate an increase in riots. These real-time findings are of first-order importance for policymakers and public administrators straddling a narrowing stringency corridor between maintaining public health and tolerance of containment policies.
We extract aggregate demand and supply shocks for the US economy from real-time survey data on inflation and real GDP growth using a novel identification scheme. Our approach exploits non-Gaussian features of macroeconomic forecast revisions and imposes minimal theoretical assumptions. After verifying that our results for US post-war business cycle fluctuations are largely in line with the prevailing consensus, we proceed to study output and price fluctuations during COVID-19. We attribute two thirds of the decline in 2020:Q1 GDP to a negative shock to aggregate demand. In contrast, regarding the staggeringly large decline in GDP in 2020:Q2, we estimate two thirds of this shock was due to a reduction in aggregate supply. Statistical analysis suggests a slow recovery due to persistent effects of the supply shock, but surveys suggest a somewhat faster rebound with a recovery in aggregate supply leading the way.
How do people balance health/money concerns during a pandemic? And, how does the communication over this trade-off affect individual preferences? We address these questions using a hypothetical field experiment (randomized controlled trial, RCT) involving around 2000 students enrolled in a big university in the south of Italy. We compare four different framings in order to investigate whether a positive and more paternalistic framing which focuses on protective strategies (“safeguard”) induces more conservative preferences than a more “crude” framing which focuses on potential losses (“costs”). We find that paternalistic framing on the health side induces individuals to give greater relevance to the health dimension. The effect is sizeable and stronger among females and altruistic individuals. Moreover, irrespective of the framing, we find a large heterogeneity in student’s preferences over the trade-off. Economics students and students who have directly experienced the economic impact of the pandemic are found to favor polices that take in greater account the economic side of the tradeoff.
The “social distancing” measures taken to contain the spread of COVID-19 impose economic costs that go beyond the contraction of GDP. Since different occupations are not equally affected, this supply shock may have distributional implications. Here, we evaluate the potential impact of enforced social distancing on wage inequality and poverty across Europe. We compute a Lockdown Working Ability (LWA) index which represents the capacity of individuals to work under a lockdown given their teleworking index −that we obtain for European occupations using 2018 EU-LFS− and whether their occupation is essential or closed. Combining our LWA index and 2018 EU-SILC, we calculate individuals’ potential wage losses under six scenarios of lockdown. The Lockdown Incidence Curves show striking differential wage losses across the distribution, and we consistently find that both poverty and wage inequality rise in all European countries. These changes increase with the duration of the lockdown and vary with the country under consideration. We estimate an increase in the headcount index of 3 percentage points for overall Europe, while the mean loss rate for the poor is 10.3%, using the 2 months lockdown simulation. In the same scenario, inequality measured by the Gini coefficient increases 2.2% in all Europe, but more than 4% in various countries. When we decompose overall inequality in Europe into between- and within-countries components, both elements significantly increase with the lockdown, being the change of the latter more important.
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I calibrate a Multi-Risk SIR model on the covid pandemic to analyze the impact of the age-specific confinement and PCR testing policies on incomes and mortality. Two polar strategies emerge as potentially optimal. The suppression policy would crush the curve by confining 90% of the population for 4 months to eradicate the virus. The flatten-the-curve policy would reduce the confinement to 30% of the population for 5 months, followed by almost one year of free circulation of the virus to attain herd immunity without overwhelming hospitals. Both strategies yield a total cost of around 15% of annual GDP when combining the economic cost of confinement with the value of lives lost. I show that hesitating between the two strategies can have a huge societal cost, in particular if the suppression policy is stopped too early. Because seniors are much more vulnerable, a simple recommendation emerges to shelter them as one deconfines young and middle-aged people in order to build our collective herd immunity. By doing so, one reduces the death toll of the pandemic together with the economic cost of the confinement, and the total cost is divided by a factor 2. I also show that expanding the mass testing capacity to screen people sent back to work has a large benefit under various scenarios. This analysis is highly dependent upon deeply uncertain epidemiologic, sociological, economic and ethical parameters.
Many countries around the world have implemented stringent containment measures to halt the spread of the 2019 coronavirus disease (Covid-19) and limit the number of fatalities. Though crucial to slow the course of the pandemic, these measures entail large short-term economic costs. This paper tries to quantify these effects using daily data on real-time containment measures implemented by countries around the world as well as daily indicators of economic activity such as Nitrogen Dioxide (NO2) emissions, international and domestic flights, energy consumption, maritime trade, and retail mobility indices. Results suggest that containment measures have had, on average, a very large impact on economic activity—equivalent to a loss of about 15 percent in industrial production over a 30-day period following the implementation of containment measures. Using a novel database on discretionary fiscal and monetary policy measures implemented by countries in response to the crisis, we find that these policy measures have been effective in mitigating some of these costs. Finally, we find that among different types of containment measures, while stay-at-home requirements and workplace closures are the most effective in curbing both infections and deaths, they are also those associated with the largest economic costs.
We test whether earlier social distancing affects the progression of a local COVID-19 outbreak. We exploit county-level rainfall on the last weekend before statewide lockdown. After controlling for historical rainfall, temperature, and state fixed-effects, current rainfall is a plausibly exogenous instrument for social distancing. Early distancing causes a reduction in cases and deaths that persists for weeks. The effect is driven by a reduction in the chance of a very large outbreak. The result suggests early distancing may have sizable returns, and that random events early in an outbreak can have persistent effects on its course.
We analyse how air traffic across countries contributed to the propagation of COVID-19 by fitting a Spatial Durbin-Watson model adapted to local projections. Such a model explicitly accounts for spatial dependence of observations and allows to track the effect of domestic and foreign new infections over time. Our estimates show that air travel-induced cases amount to 8-9% of all confirmed cases on average, and that these infections from abroad came in two waves: in mid-March and the fourth week of March. We also evaluate that air travel restrictions had a marked impact in reducing the progression of the pandemic from April onward. Closing all air traffic 4 weeks earlier could have prevented between 7,000 and 7,800 deaths. Based on standard values of a statistical life and on the latest estimates of GDP loss induced by air travel restrictions, we conclude that, just as social distancing, spatial distancing might be a cost-effective way to tackle COVID-19 in the short run.
Time series models are developed for predicting future values of a variable which when cumulated is subject to an unknown saturation level. Such models are relevant for many disciplines, but here attention is focussed on the spread of epidemics and the applications are for coronavirus. The time series models are relatively simple but are such that their specification can be assessed by standard statistical test procedures. In the generalized logistic class of models, the logarithm of the growth rate of the cumulative series depends on a time trend. Allowing this trend to be time-varying introduces further flexibility and enables the effects of changes in policy to be tracked and evaluated.
Stay-at-home orders (SAHOs) have been implemented in most U.S. states to mitigate the spread of COVID-19. This paper quantifies the short-run impact of these containment policies on search behavior and labor demand for child care. The child care market may be particularly vulnerable to a SAHO-type policy shock, given that many providers are liquidity-constrained. Using plausibly exogenous variation from the staggered adoption of SAHOs across states, we find that online job postings for early care and education teachers declined by 13% after enactment. This effect is driven exclusively by private-sector services. Indeed, hiring by public programs like Head Start and pre-kindergarten has not been influenced by SAHOs. In addition, we find little evidence that child care search behavior among households has been altered. Because forced supply-side changes appear to be at play, our results suggest that households may not be well-equipped to insure against the rapid transition to the production of child care. We discuss the implications of these results for child development and parental employment decisions.
The outbreak of the Coronavirus Disease 2019 (COVID-19) is inevitably affecting remittance-dependent countries through economic downturns in the destination countries, and restrictions on travel and sending remittances to their home country. This paper explores the potential impacts of the COVID-19 pandemic on the welfare of remittance-dependent households using a dataset collected in heavily remittance-dependent regions in the Philippines prior to the outbreak. First, the empirical model pins down the relationship between the macroeconomic performance of the destination countries, the amount of remittances, and the welfare of households. Second, we use the difference in the IMF’s forecasts for the 2020 GDP before and after the COVID-19 crisis to project potential impacts on households caused by the COVID-19 pandemic. Our projection shows that remittance inflow will decrease by 23-32% and household spending per capita will decline by 2.2-3.3% in one year as a result of the pandemic.
We evaluate the effects of containment measures on flattening the COVID-19 infection curve in Germany. Constructing a regional daily panel dataset, we make use of the fact that different containment measures were implemented by the German state governments at different times and not uniformly nationwide. The results show that the cancellation of mass events, school and childcare closures and curfews played an important role, just as further unobserved factors beyond government interventions. In contrast, we find only limited evidence for additional effects of the closures of service sectors in public life.
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A key question is how countries can gradually exit the covid-19 lockdown in order to re-open their economies and mitigate the huge economic costs that the lockdown is imposing. Answering this question is the first step of the analysis proposed in this paper. Using a benchmark country known to be severely hit by the virus (Belgium), it compares the epidemiological effects of different stereotyped exit strategies. It concludes that, in order to avoid a rebound in infections and follow a relatively quick path toward ending the epidemic, the re-opening of the economy and the society must be very cautious and strict measures of social distancing and an ambitious and effective testing programme must be implemented. The second step, and main point of the paper, consists of exploring the role of a country's culture, more particularly the prevailing contact habits and norms. This is done by substituting the pattern of inter-individual interactions of two other countries for the pattern observed in the benchmark country. The results are striking: differences in the way people interact, and more specifically the frequencies of their contacts within and between age groups, seem to (partly) explain variations in the incidence of the virus and performances in battling against it. More precisely, if Belgium inherited the interaction pattern of Germany when exiting the lockdown, it could achieve the objective of (partial) re-opening of the economy with more moderate policies than the ones it actually needs. And, conversely, if it inherited the social structure of Italy, it would have to take even more stringent measures lest the cost to bear as a result of economic re-opening should be (much) heavier. In addition to differences in the effectiveness of public health policies and in the genetic make-up of population groups, cultural specificities thus appear to play a significant role in explaining international and inter-regional variations in the incidence of the virus and the impact of public interventions.
The COVID19 pandemic has caused shocks to the demand for home childcare (with the closure of schools and nurseries) and the supply of home childcare (with many people not working). We collect real-time data on daily lives to document that UK families with young children have been doing the equivalent of a working week in childcare. Women have been doing the greater share, but overall, the gender childcare gap (the difference between the share of childcare done by women and the share done by men) for the additional, post-COVID19 hours is smaller than that for the allocation of pre-COVID19 childcare. However, the amount of additional childcare provided by men is very sensitive to their employment – the allocation has become more equal in households where men are working from home and where they have been furloughed/ lost their job. There are likely to be long-term implications from these changes – potentially negative for the careers of parents of young children; but also, more positively for some families, for sharing the burden of childcare more equally in the future.
We analyse the economics and epidemiology of different scenarios for a phased restart of the UK economy. Our economic model is designed to address the unique features of the COVID-19 pandemic. Social distancing measures affect both supply and demand, and input-output constraints play a key role in restricting economic output. Standard models for production functions are not adequate to model the short-term effects of lockdown. A survey of industry analysts conducted by IHS Markit allows us to evaluate which inputs for each industry are absolutely necessary for production over a two month period. Our model also includes inventory dynamics and feedback between unemployment and consumption. We demonstrate that economic outcomes are very sensitive to the choice of production function, show how supply constraints cause strong network effects, and find some counter-intuitive effects, such as that reopening only a few industries can actually lower aggregate output. Occupation-specific data and contact surveys allow us to estimate how different industries affect the transmission rate of the disease. We investigate six different re-opening scenarios, presenting our best estimates for the increase in R0 and the increase in GDP. Our results suggest that there is a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output. This corresponds to a situation in which all non-consumer facing industries reopen, schools are open only for workers who need childcare, and everyone who can work from home continues to work from home.
This paper shows that the optimal combination of social distancing and case detection allows for complete and efficient eradication of COVID-19. The first contribution is theoretical. I show that the optimal suppression-policy is a simple function of observable sufficient-statistics, making it easily implementable. I prove that optimal social distancing is the strongest when an outbreak is detected, and then gradually relaxed. If case detection is sufficiently efficient, social distancing vanishes wholly and quickly; otherwise, it needs to stay in place until a vaccine arrives. The second contribution is quantitative. I find that, if Italy adopts digital contact tracing, total suppression costs only 0.8% of annual GDP. In sharp contrast, under the current detection efficiency, the total cost of suppression amounts to at least 14% of GDP.
We survey a representative sample of US households to study how exposur to the Covid-19 stock market crash affects expectations and planned behavior. Wealth shocks are associated with upward adjustments of expectations about retirement age, desired working hours, and household debt, but have only small effects on expected spending. We provide correlational and experimental evidence that beliefs about the duration of the stock market recovery shape households' expectations about their own wealth and their planned investment decisions and labor market activity. Our findings shed light on the implications of household exposure to stock market crashes for expectation formation.
We develop a multiple-events model and exploit within and between country variation in the timing, type and level of intensity of various public policies to study their dynamic effects on the daily incidence of COVID-19 and on population mobility patterns across 135 countries. We remove concurrent policy bias by taking into account the contemporaneous presence of multiple interventions. The main result of the paper is that cancelling public events and imposing restrictions on private gatherings followed by school closures have quantitatively the most pronounced effects on reducing the daily incidence of COVID-19. They are followed by workplace as well as stay-at-home requirements, whose statistical significance and levels of effect are not as pronounced. Instead, we find no effects for international travel controls, public transport closures and restrictions on movements across cities and regions. We establish that these findings are mediated by their effect on population mobility patterns in a manner consistent with time-use and epidemiological factors.
During the COVID-19 epidemic in Japan between March and April 2020, Internet surveys were conducted to construct panel data to investigate changes at the individual level regarding preventive behaviors and mental conditions by surveying the same respondents at different times. Specifically, the difference-in-difference (DID) method was used to explore the impact of the COVID-19 state of emergency declared by the government. Key findings were: (1) the declaration led people to stay home, while also generating anger, fear, and anxiety. (2) The effect of the declaration on the promotion of preventive behaviors was larger than the detrimental effect on mental conditions. (3) Overall, the effect on women was larger than that on men.
We analyse whether the various types of lockdowns implemented around the world mitigated the surge in infections and reduced mortality related to the covid-19, and whether their effectiveness differed in developing vs. developed countries. Our data cover 184 countries from December 31st 2019 to May 4th 2020, and identifies when lockdowns were adopted, along with confirmed cases and deaths. We find that reducing movements within countries has been effective in developed economies -averting about 650,000 deaths- but not in developing ones, that countries that acted fast fared better, and that closing borders has had no appreciable effect, even after fifty-days.
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The COVID-19 pandemic has already led to dramatic policy responses in most advanced economies, and in particular sustained lockdowns matched with sizable transfers to much of the workforce. This paper provides a preliminary quantitative analysis of how aggregate policy responses should differ in developing countries. To do so we build an incomplete-markets macroeconomic model with epidemiological dynamics that features several of the main economic and demographic distinctions between advanced and developing economies relevant for the pandemic. We focus in particular on differences in population structure, fiscal capacity, healthcare capacity, the prevalence of ‘hand-to-mouth’ households, and the size of the informal sector. The model predicts that blanket lockdowns are generally less effective in developing countries at reducing the welfare costs of the pandemic, saving fewer lives per unit of lost GDP. Age-specific lockdown policies, on the other hand, may be even more potent in developing countries, saving more lives per unit of lost output than in advanced economies.
I use simple correlations and regression analysis to study how the number of confirmed Covid-19 cases and the number of deaths with Covid-19 per 100,000 people is related with the socioeconomic characteristics of local areas in England and Wales. I find that local areas that have larger households, worse levels of self-reported health and a larger fraction of people using public transport have more Covid-19 infections per 100,000 people. For mortality, household size and use of public transport are less important, but there is a clear relation with age, ethnicity and self-reported health. Local areas with an older population, a larger share of black or Asian population and worse levels of self-reported health have more Covid-19 deaths per 100,000 people. The relation between self-reported health and infections and mortality suggests that encouraging a healthy lifestyle can help prevent the spread of infection and reduce mortality. Also, as many countries now begin to relax lockdown measures, policymakers should pay particular attention to reducing the risk of infection in public transport.
Policy makers responding to COVID-19 need to know people's relative valuation of health over wealth. Loosening and tightening lockdowns moves a society along a (perceived) health-wealth trade-off and the associated changes have to accord with the public's relative valuation of health and wealth for maximum compliance. In our survey experiment (N=4,618), we randomize information provision on economic and health costs to assess public preferences over this trade-off in the UK and the US. People strongly prioritize health over wealth, but the treatment effects suggest these priorities will change as experience of COVID-19 deaths and income losses evolves.
Information also has heterogeneous/polarizing effects. These results encourage policy caution. Individual differences in health-wealth valuation highlight this study's importance because they map onto compliance with current lockdown measures.
The COVID-19 pandemic and social-distancing as well as stay-at-home orders can directly affect mental health and quality of life. In this ongoing project, we analyze rich data from Telefonseelsorge, the largest German emergency helpline service, to better understand the effect of the pandemic and of local lockdown measures on mental health–related helpline contacts. First, looking at Germany–wide changes, we find that overall helpline contacts increase by around 25% in the first week of the lockdown and slowly decrease again after the third lockdown week. Our results suggest that the increase is not driven by financial worries or fear of the virus itself, but reflects heightened loneliness, anxiety, and suicidal ideation. Second, we exploit spatial variation in policies among German federal states to assess whether the effect depends on the stringency of local measures. Preliminary evidence suggests that the average effect is more pronounced in states that implemented stricter measures.
This paper examines the impact of the Severe Acute Respiratory Syndrome (SARS) epidemic on China's trade. Using quarterly transaction-level trade data of all Chinese firms, we find that firms in regions with local transmission of SARS experienced lower import and export growth at both the intensive and extensive margins, compared to those in the unaffected regions. The affected firms' trade growth remained lower two years after SARS. Products that are more capital-intensive, skill-intensive, upstream in the supply chains, and differentiated experienced a smaller export decline but a stronger recovery. Small exporters were more likely to exit, slowing down trade recovery.
The Austrian ski resort of Ischgl is commonly claimed to be ground zero for the diffusion of the SARS-CoV-2 virus across Germany. Drawing on data for 401 German counties, we find that conditional on geographical latitude and testing behavior by health authorities, road distance to Ischgl is indeed an important predictor of infection cases, but — in line with expectations — not of fatality rates. Were all German counties located as far from Ischgl as the most distant county of Vorpommern-Rügen, Germany would have seen about 48% fewer COVID-19 cases. A simple diffusion model predicts that the absolute value of the distance-to-Ischgl elasticity should fall over time when inter- and intra-county mobility are unrestricted. We test this hypothesis and conclude that the German lockdown measures have halted the spread of the virus.
This paper examines the impact of the COVID-19 pandemic on commercial real estate prices. We construct a novel measure of listed commercial real estate (CRE) portfolios’ exposure to the growth in COVID-19 cases using a large, granular sample of firms’ individual commercial property holdings. We document a negative relationship between this geographically weighted case growth and risk-adjusted returns. However, there is substantial variation across property types: the retail and hospitality sectors react the most negatively while technology sector reacts positively to the exposure of their portfolios to growth in COVID-19 cases. After conditioning on the property type focus of a firm, days since the beginning of the portfolio’s exposure to the outbreak, the weighted-average population density of the counties in which the portfolio manager is invested, and the extent to which the portfolio is concentrated by property type and geography, other firm characteristics have little effect on the negative stock price impact of the pandemic. Despite negative short-term market reactions, our findings suggest that the sensitivity of CRE returns to increases in reported COVID-19 cases is reduced after announcements of stay-at home orders and state of emergency declarations. We argue that the effects of COVID-19 that we observe in highly liquid stock markets are indicative of pricing effects occurring in private CRE markets.
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This paper investigates whether security markets price the effect of social distancing on firms’ operations. We document that firms that are more resilient to social distancing significantly outperformed those with lower resilience during the COVID-19 outbreak, even after controlling for the standard risk factors. Similar cross-sectional return differentials already emerged before the COVID-19 crisis: the 2014-19 cumulative return differential between more and less resilient firms is of similar size as during the outbreak, suggesting growing awareness of pandemic risk well in advance of its materialization. Finally, we use stock option prices to infer the market’s return expectations after the onset of the pandemic: even at a two-year horizon, stocks of more pandemic-resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting their lower exposure to disaster risk. Hence, going forward, markets appear to price exposure to a new risk factor, namely, pandemic risk.
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. We find increases in the fraction of those being retired across the whole age distribution with women and blacks driving a large part of the accelerated retirement.
This paper studies the role of international trade of essential goods during a pandemic. We consider a multi-country, multi-sector model with essential and non-essential goods. Essential goods provide utility relative to a reference consumption level, and a pandemic consists of an increase in this reference level. Each country produces domestic varieties of both types of goods using capital and labor subject to sectoral adjustment costs, and all varieties are traded internationally subject to trade barriers. We study the role of international trade of essential goods in mitigating or amplifying the impact of a pandemic. We find that the effects depend crucially on the countries' trade imbalances in essential goods. Net importers of these goods are relatively worse off during a pandemic than net exporters. The welfare losses of net importers are lower in a world with high trade barriers, while the reverse is the case for net exporters. Yet, once a pandemic arrives, net exporters of essential goods benefit from an increase in trade barriers, while net importers benefit from a decrease in them. These findings are consistent with preliminary evidence on changes in trade barriers across countries during the COVID-19 pandemic.
How do individuals adjust their consumption in response to information disseminated through peers and the social network? Using United States data on consumption, coupled with geographic friendship ties to measure social connectivity, this paper quantifies the role of social networks as a propagation mechanism for understanding aggregate fluctuations in consumption. Using the COVID-19 pandemic as a source of variation, we find that a 10\% rise in cases and deaths in counties connected through the social network is associated with a 0.64\% and 0.33\% decline in consumption expenditures--roughly three to seven times as large as the direct effects of local cases or deaths. Counties more socially connected to epicenter countries of the pandemic also saw a bigger drop in consumption. These effects are concentrated among consumer goods and services that rely more on social-contact, suggesting that individuals incorporate the experiences from their social network to inform their own consumption choices. We are working on incorporating this microeconomic evidence into a heterogeneous agent model and social interaction to study the aggregate demand implications.
In March of 2020, banks faced the largest increase in liquidity demands ever observed. Firms drew funds on a massive scale from pre-existing credit lines and loan commitments in anticipation of cash flow disruptions from the economic shutdown designed to contain the COVID-19 crisis. The increase in liquidity demands was concentrated at the largest banks, who serve the largest firms. Pre-crisis financial condition did not limit banks’ liquidity supply. Coincident inflows of funds to banks from both the Federal Reserve’s liquidity injection programs and from depositors, along with strong pre-shock bank capital, explain why banks were able to accommodate these liquidity demands.
We analyze how to optimally engage in social distancing (SD) in order to minimize the spread of an infectious disease. We identify conditions under which the optimal policy is single-peaked, i.e., first engages in increasingly more social distancing and subsequently decreases its intensity. We show that the optimal policy might delay measures that decrease the transmission rate substantially to create “herd-immunity” and that engaging in social distancing sub-optimally early can increase the number of fatalities. Finally, we find that optimal social distancing can be an effective measure in substantially reducing the death rate of a disease.
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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.
In times of crisis, humans have a tendency to turn to religion for comfort and explanation. The 2020 COVID-19 pandemic is no exception. Using daily data on Google searches for 95 countries, this research demonstrates that the COVID-19 crisis has increased Google searches for prayer (relative to all Google searches) to the highest level ever recorded. More than half of the world population had prayed to end the coronavirus. The rise amounts to 50% of the previous level of prayer searches or a quarter of the fall in Google searches for flights, which dropped dramatically due to the closure of most international air transport. Prayer searches rose at all levels of income, inequality, and insecurity, but not for the 10% least religious countries. The increase is not merely a substitute for services in the physical churches that closed down to limit the spread of the virus. Instead, the rise is due to an intensified demand for religion: We pray to cope with adversity.
This paper assesses the differential impacts on mental health of distinct public policy lockdown responses to the early part of the Covid-19 pandemic. It develops novel narrative economics of language approach born at the intersection of cultural economics, big data and narrative economic analysis. This approach is reliant on the study of language to extract cultural and behavioural insights with socioeconomic relevance. We sourced Google trend data for seed keywords, death and suicide, and employed difference-in-differences and regression discontinuity estimation techniques to conduct two investigations. First, we compared the amount of emotional distress experienced by British and Italian residents before and after the implementation of lockdown policies. Second, we extended our analysis to include a country that did not impose a lockdown, Sweden, as a control, which facilitated a natural quasi-experiment. Our main findings are that the lockdown policy affected public mental health, yet the dominant factor for public mental health was the cognitive bias of salient public death toll statistics. Countries had a pre-existing culturally relative disposition towards death-related anxiety, and the magnitude of their response to the pandemic varies in a cultural hysteresis manner. Searches for the keyword suicide decreased during the pandemic, while the interest in trivia remained unaffected, as indicated through searches for the keyword chair. Significant spillovers from one ” specific national lockdown public policies to another country’s mental health are identified.
Since the beginning of the COVID-19 crisis, our perception of the world significantly changed. In this paper we show the results of 3 studies that collectively illustrate a novel mechanism through which this has happened. We document the effect of social distancing on our perceptions, through the moderating effect of ambiguity aversion. In experiment 1 we show that ambiguity aversion predicts illusory pattern perception, defined as identifying faces in white noise pictures. In experiment 2 we show that ambiguity aversion also predicts higher cognitive level illusory pattern perception, defined as belief in conspiracy theories. Experiment 3 shows, through two uniquely timed questionnaires, that ambiguity aversion increases significantly from before to after the lockdown (due to the COVID-19 pandemic) for a sample of over 300 subjects. Remarkably, this difference in ambiguity aversion is no longer significant when we control for the drop in regular social contact over this period.
We measure labor demand and supply shocks at the sector level around the Covid-19 outbreak, by estimating a Bayesian structural vector autoregression on monthly statistics of hours worked and real wages and applying the methodology proposed by Baumeister and Hamilton (2015). Our estimates suggest that two-thirds of the 16.24 percentage point drop in the growth rate of hours worked in April 2020 are attributable to supply. Most sectors were subject to historically large negative labor supply and demand shocks in March and April 2020, but there is substantial heterogeneity in the size of these shocks across sectors. Leisure and Hospitality was particularly affected. We find positive labor demand shocks for sectors such as Retail Trade, and Information in March 2020 that vanish in April 2020. We show that our estimates of supply shocks are correlated with sectoral measures of telework.
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Drastic public health measures such as social distancing or lockdowns can reduce the loss of human life by keeping the number of infected individuals from exceeding the capacity of the health care system but are often criticized because of the social and economic costs they entail. We question this view by combining an epidemiological model, calibrated to capture the spread of the COVID-19 virus, with a multisector model, designed to capture key characteristics of the U.S. Input Output Tables. Our two-sector model features a core sector that produces intermediate inputs not easily replaced by inputs from the other sector, subject to minimum-scale requirements. We show that, by affecting workers in this core sector, the high peak of an infection not mitigated by social distancing may cause very large upfront economic costs in terms of output, consumption and investment. Social distancing measures can reduce these costs, especially if skewed towards non-core industries and occupations with tasks that can be performed from home, helping to smooth the surge in infections among workers in the core sector.
Since the first outbreak was reported in Wuhan, China in late December 2019, the 2019 coronavirus disease (COVID-19) has spread to over 200 countries/territories globally. In response, many countries have implemented several containment measures to halt the spread of the virus and limit the number of fatalities. It remains unclear the extent to which these unprecedented measures have been successful. The paper examines this question using daily data on the number of COVID-19 cases and deaths as well as on real-time containment measures implemented by countries around the world. Results suggest that containment measures have been, on average, very effective in flattening the “pandemic curve” and reducing the number of fatalities. These effects have been stronger in countries where containment measures have been implemented faster and have resulted in less mobility—de facto, more social distancing—and in those with lower temperatures, lower population density, a larger share of an elderly population and stronger health systems. Among different types of containment measures, stay-at-home orders seems to have been more effective in reducing the number of deaths. However, these adjustments benefitted mostly highly educated workers and white collars. Overall, low-income individuals faced worse labor market outcomes and suffered higher psychological costs.
As COVID-19 has spread across the globe, several observers noticed that countries still administering an old vaccine against tuberculosis–the BCG vaccine–have had fewer COVID-19 cases and deaths per capita in the early stages of the outbreak. This paper uses a geographic regression discontinuity analysis to study whether and how COVID-19 prevalence changes discontinuously at the old border between West Germany and East Germany. The border used to separate two countries with very different vaccination policies during the Cold War era. We provide formal evidence that there is indeed a sizable discontinuity in COVID-19 cases at the border. However, we also find that the difference in novel coronavirus prevalence is uniform across age groups and show that this discontinuity disappears when commuter flows and demographics are accounted for. These findings are not in line with the BCG hypothesis. We then offer an alternative explanation for the East-West divide. We simulate a canonical SIR model of the epidemic in each German county, allowing infections to spread along commuting patterns. We find that in the simulated data, the number of cases also discontinuously declines as one crosses from west to east over the former border.
We develop an econometric model of consumer panic (or panic buying) during the COVID-19 pandemic. Using Google search data on relevant keywords, we construct a daily index of consumer panic for 54 countries from January to late April 2020. We also assemble data on government policy announcements and daily COVID-19 cases for all countries. Our panic index reveals widespread consumer panic in most countries, primarily during March, but with significant variation in the timing and severity of panic between countries. Our model implies that both domestic and world virus transmission contribute significantly to consumer panic. But government policy is also important: Internal movement restrictions - whether announced by domestic or foreign governments - generate substantial short run panic that largely vanishes in a week to ten days. Internal movement restrictions announced early in the pandemic generated more panic than those announced later. In contrast, travel restrictions and stimulus announcements had little impact on consumer panic.
We use helpline calls to measure psychological and social suffering in the population at a daily frequency. Our data are from Switzerland’s most popular free anonymous helpline, focusing on the Covid-19 crisis period. We compare calls (a) between the pandemic period of 2020 and the corresponding period of 2019 and (b) along the timeline of the lockdown. We find the total volume of calls to have grown in line with the long-run trend. To the extent that calls did increase, this was mainly explained by worries linked directly to the pandemic: calls by persons over 65 and calls about fear of infection. Encouragingly, calls about violence were down on the previous year. Calls about addiction and suicidality increased during the initial phase of the lockdown, plateaued, and returned to their 2019 levels once gradual opening started. Calls about relationship problems decreased in the early phase of the lockdown, and gradually increased, again reaching 2019 levels once opening up started. Overall, these results suggest that psychological and social strain is of second-order importance relative to the medical anxieties generated by the pandemic.
The economic effects of a pandemic crucially depend on the extent to which countries are connected in global production networks. In this paper we incorporate production barriers induced by COVID-19 shock into a Ricardian model with sectoral linkages, trade in intermediate goods and sectoral heterogeneity in production. We use the model to quantify the welfare effect of the disruption in production that started in China and quickly spread across the world. We find that the COVID-19 shock has a considerable impact on most economies in the world, especially when a share of the labor force is quarantined. Moreover, we show that global production linkages have a clear role in magnifying the effect of the production shock. Finally, we show that the economic effects of the COVID-19 shock are heterogeneous across sectors, regions and countries, depending on the geographic distribution of industries in each region and country and their degree of integration in the global production network
The spread of COVID-19 and implementation of “social distancing” policies around the world have raised the question of how many jobs can be done at home. This paper uses skills surveys from 53 countries at varying levels of economic development to estimate jobs’ amenability to working from home. The paper considers jobs’ characteristics and uses internet access at home as an important determinant of working from home. The findings indicate that the amenability of jobs to working from home increases with the level of economic development of the country. This is driven by jobs in poor countries being more intensive in physical/manual tasks, using less information and communications technology, and having poorer internet connectivity at home. Women, college graduates, and salaried and formal workers have jobs that are more amenable to working from home than the average worker. The opposite holds for workers in hotels and restaurants, construction, agriculture, and commerce. The paper finds that the crisis may exacerbate inequities between and within countries. It also finds that occupations explain less than half of the variability in the working-from-home indexes within countries, which highlights the importance of using individual-level data to assess jobs’ amenability to working from home.
Coronavirus has been portrayed as the “great equalizer”. None seems immune to the virus and to the economic consequences of the lockdown measures imposed to contain its diffusion. We exploit novel data from two real time surveys to study the early impact on the labor market of the lockdown in Italy – one of the two countries, with China, hit hard and early. COVID was not a “great economic equalizer.” Quite on the contrary. Low-educated workers, blue collars and low-income service workers were more likely to have stopped working both three-week and six-week after the lockdown. Low-educated workers were less likely to work from home. Blue collars worked more from their regular workplace, but not from home. Low-income service workers were instead less likely to work from the regular workplace. For both blue collars and low-income service workers, the monthly labor income dropped already in March. Not surprisingly, they were less in agreement with the public policy measures that required the closing of (non-essential) business and activities. Some positive adjustments took place between the third and the sixth week from the lockdown: the share of idle workers dropped, as the proportion of individuals working at home and from their regular workplace increased. However, these adjustments benefitted mostly highly educated workers and white collars. Overall, low-income individuals faced worse labor market outcomes and suffered higher psychological costs.
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This paper estimates a SEIRD (susceptible-exposed-infected-recovered-deaths) epidemic model of COVID-19, which accounts for both observed and unobserved states and endogenous mobility changes induced by lockdown policies. The model is estimated on Lombardy and London – two regions that had among the worst outbreaks of the disease in the world – and used to predict the evolution of the epidemic under different policies. We show that policies targeted also at mitigating the probability of contagion are more effective in containing the spread of the disease, than the one aimed at just gradually reducing the mobility restrictions. In particular, we show that if the probability of contagion is decreased between 20% and 40% of its original level before the outbreak, while increasing mobility, the total death toll would not be higher than in a permanent lockdown scenario. On the other hand, neglecting such policies could increase the risk of a second epidemic peak even while lifting lockdown measures at later dates. This highlights the importance during the containment of the disease of promoting “soft” policy measures that could reduce the probability of contagion, such as, wearing masks and social distancing.
I modify the basic SEIR model to incorporate demand for health care. The model is used to study the relative effectiveness of policy interventions that include social distancing, quarantine, contact tracing, and random testing. A version of the model that is calibrated to the Ferguson et al (2020) model suggests that permanent, high-intensity social distancing reduces mortality rates and peak ICU demand substantially, but that a policy that relaxes high-intensity social distancing over time in the context of a permanent efficient quarantine regime is even more effective. Adding contact tracing and random testing to this policy further improves outcomes. For the policies considered, employment outcomes are determined by their respective social distancing components, not their quarantine component or health outcomes. Given the uncertainty surrounding the disease parameters, especially the transmission rate of the disease, and the effectiveness of policies, the uncertainty for health outcomes, however, is very large.
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.
This paper uses a difference-in-differences framework to estimate the causal impact on the mortality rate of non-pharmaceutical interventions (NPIs) used to fight the 1918 inuenza pandemic. The results suggest that NPIs such as school closures and social distancing introduced a trade-off. While they could lower the fatality rate during the peak of the inuenza pandemic, they might also have reduced the herd immunity and significantly increased the death rate in subsequent years. There is no significant association between the implementation of NPIs and cities' growth.
Looking at 342 million residents in 21 EU countries, we estimate that 99 million individuals live in households which cannot cover for two months of the most basic expenses – food at home, utilities and rent/mortgage on their single main residence - only from their savings in bank accounts. Without privately earned income but with (pre-covid19) pension income and public transfers, 57 million have savings for less than 2 months. Government support in the form of employment protection schemes and beyond is thus fundamental to ensure livelihood during the covid19 shock, yet many individuals would remain vulnerable if ensured 50% of their gross privately earned income. We estimate mortgage and rent suspension can decrease in half the number of individuals at risk. We find there are stark differences between countries and that individuals born outside of the EU are particularly vulnerable. Those dependent on their income will be forced to resume work earlier and take higher health risks
In this paper, I hypothesize that internal migrants are key agents in the diffusion of viruses. Self-isolation and closure of economic activities in outbreak areas generate many people jobless or socially isolated. Recently settled migrants might therefore choose to return to their home towns, thus spreading the virus further. To test the existence and the quantitative importance of this mechanism, I use subnational data for Italy. I use panel data at the regional-daily level and exploit detailed data on individuals' changes of residence between Italian regions before Covid to measure, for each region, the number of potential of return migrants from outbreak areas. The results suggest that regions with more exposed to return migration experienced more Covid deaths throughout nearly all stages of diffusion of the virus. A back-of-the-envelope calculation suggests that, had all regions had the same number of migrants in outbreak areas as the one at the tenth percentile, Italy would have experienced around 2000 fewer Covid deaths, i.e, 22-24 percent fewer deaths than the regions outside the outbreak areas actually experienced.
We estimate a nonlinear VAR model allowing for the impact of uncertainty shocks to depend on the average outlook of the economy measured by survey data. We find that, in response to the same uncertainty shock, industrial production and inflation's peak decrease is around three and a half times larger during pessimistic times. We build scenarios for a path of innovations in uncertainty consistent with the COVID-19-induced shock. Industrial production is predicted to experience a year-over-year peak loss of between 15.1% and 19% peaking between September and December 2020, and subsequently to recover with a rebound to pre-crisis levels between May and August 2021. The large impact is the result of an extreme shock to uncertainty occurring at a time of very negative expectations on the economic outlook.
A widely held belief is that autocratic governments have been more effective in reducing the movement of people to curb the spread of Covid-19. Using the Oxford COVID-19 Government Response Tracker (OxCGRT), and a real-time dataset with daily information on travel and movement across 111 countries, we find that autocratic regimes imposed more stringent lockdowns and relied more on contact tracing. However, we find no evidence that autocratic governments were more effective in reducing travel, and evidence to the contrary: countries with democratically accountable governments introduced less stringent lockdowns but were more effective in reducing geographic mobility at the same level of policy stringency. In addition, building on a large literature on cross-cultural psychology, we show that for the same policy stringency, countries with collectivist cultural traits experienced larger declines in geographic mobility relative to their more individualistic counterparts. We conclude that, in terms of reducing mobility, collectivist and democratic countries have implemented relatively effective responses to Covid-19.
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This paper studies optimal lockdown policies in a dynamic economy without government commitment. A lockdown imposes a cap on labor supply, which lowers economic output but improves health prospects. A government would like to commit to limit the extent of future lockdowns in order to increase investment by supporting a more optimistic economic outlook. However, such a commitment may not be credible since investment decisions are sunk at the time when the government decides on lockdowns. Rules that limit a government's future policy discretion can improve the efficiency of lockdowns, even in the presence of noncontractible information.
We use high-frequency Google search data, combined with data on the announcement dates of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic in U.S. states, to isolate the impact of NPIs on unemployment in an event-study framework. Exploiting the differential timing of the introduction of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we analyze how Google searches for claiming unemployment insurance (UI) varied from day to day and across states. We describe a set of assumptions under which proxy outcomes (e.g., Google searches) can be used to estimate the causal parameter of interest (e.g., share of UI claims caused by NPIs) when data on the outcome of interest (e.g., daily UI claims) are limited. Using this method, we quantify the share of overall growth in unemployment during the COVID-19 pandemic that was directly due to each of these NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures could explain 4.4% and 8.5% of UI claims respectively, while the other NPIs did not increase UI claims.
The literature documents a strong positive association between social capital and health. However, because personal social interactions are implicated in the spread of viral infections, areas with high levels of social capital may be especially at risk during the COVID-19 pandemic. Social capital comprises not only a cognitive component (i.e. norms of reciprocity and trust) but also a relational component (i.e. social relationships and networks). We use data from counties in the United States to provide evidence on the extent to which community level responses such as reducing mobility to comply with social distancing advice and regulations are related with social capital. In line with predictions we find that individuals reduced mobility earlier and to a higher degree in counties with high levels of social capital than in counties with low levels of social capital.
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 this policy response. 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 financial sector bailouts. An alternative intervention that targets aid to firms at risk of bankruptcy prevents more bankruptcies at much lower fiscal cost, but only enjoys marginally higher welfare. Finally, we study longer-run consequences for firm leverage and intermediary health when pandemics become the new normal.
Epidemiological models assume gravity-like interactions of individuals across space without microfoundations. We combine a simple epidemiological framework with a dynamic model of individual location choice. The model predicts that flows of people across space obey a structural gravity equation. By means of an application to data from Great Britain we show that our structural-gravity framework: provides a rationale for quarantines; offers a clear mapping from observed geography to the spread of a disease; and makes it possible to evaluate the welfare impact of (expected and unexpected) mobility restrictions in the face of a deadly epidemic.
The rapid and dramatic diffusion of the Covid-19 epidemic in Italy was tackled by the Italian government with social distancing measures and with the suspension of all economic activities, except "essential" sectors. A lively policy debate on more refined criteria to choose what activities to allow and to suspend in the future led INAIL (National Institute for Insurance against Accidents at Work) to develop a measure of the risk of contagion in the workplace. In this paper we exploit this novel source of information about the risk of contagion in the workplace to study, for the first time, the crossindustry relationship between the estimated risk of contagion at work and the adoption of robots, in order to test the hypothesis that robotisation may facilitate social distancing and lower the risk of contagion. The analysis, which includes various controls of possible automation-related confounding factors and addresses possible issues of endogeneity, provides evidence that industries employing more robots per worker in production tend to exhibit a lower risk of contagion due to Covid-19. Results and policy implications for the selection of suspension criteria are discussed.
The effects of the Covid lockdown have been very severe in Italy, with a reduction in the value of potential output produced peaking at 69% for the construction and real estate and 63% for Mechanics. As a result, GDP is expected to drop by around 10% in 2020, according to most forecasts. Most activities were reopened on May 4th, although within strict social distancing and health safety guidelines. In this paper we argue that a targeted exit from the lockdown could have been implemented instead. Priority could have been given to those activities with the greatest impact on the national economy. This targeted strategy, combined with an assessment of the inherent health risks of each activity, would have reduced the risks of a second wave of contagion, still reactivating gross output and jobs to a similar extent of the general reopening actually implemented. In this study we propose a methodology to identify production activities for which total or partial closures or reopening would have the greatest impact on the country's GDP, output and employment, using input output tables and network centrality measures in production chains. The administrative lockdown implemented up to May 4th, if kept for one year, would wipe out 52% of GDP. The targeted reopening proposed here would reduce this negative impact by 70%. Our methodology could be applied also in the in the unfortunate event of a new wave of contagion and a new targeted lockdown.
This paper presents a new data set collected on representative samples across 6 countries: China, South Korea, Japan, Italy, the UK and the four largest states in the US. The information collected relates to work and living situations, income, behavior (such as social-distancing, hand-washing and wearing a face mask), beliefs about the Covid 19 pandemic and exposure to the virus, socio-demographic characteristics and pre-pandemic health characteristics. In each country, the samples are nationally representative along three dimensions: age, gender, and household income, and in the US, it is also representative for race. The data were collected in the third week of April 2020. The data set could be used for multiple purposes, including calibrating certain parameters used in economic and epidemiological models, or for documenting the impact of the crisis on individuals, both in financial and psychological terms, and for understanding the scope for policy intervention by documenting how people have adjusted their behavior as a result of the Covid-19 pandemic and their perceptions regarding the measures implemented in their countries. The data is publicly available.
Issue : 16
Is a lockdown an effective means to limit the spread of the COVID-19 pandemic? We study the case of Sweden—one of the few countries without a lockdown—and use synthetic control techniques to develop a counterfactual lockdown scenario. First, we use a “donor pool” of European countries to construct a doppelganger that behaves just like Sweden in terms of infections before the lockdown. Second, we find that infection dynamics in the doppelganger since the lockdown do not systematically differ from the actual dynamics in Sweden. Third, we study Google mobility data and find that Swedes adjusted their activities in similar ways as in the doppelganger, although to a somewhat lesser extent.
On average, women comprise a smaller share of deaths from Covid-19. However, variation in the share of Covid-19 deaths for women across countries and US States suggests that biological factors cannot fully account for this gender difference. I hypothesize that women’s participation in the workforce is related to women’s share of Covid-19 deaths. I show that the percent of the full-time workforce comprised by women is positively related to the percent of female Covid-19 deaths across countries. I also show that the percent of the full-time workforce comprised by women is positively related to the incidence of various diseases associated with a more severe impact of Covid-19 and the percent of medical doctors and nurses who are women. My results suggest that in countries where women participate more fully in the workforce, women may be more susceptible to Covid-19 due to increased exposure to the virus and a higher risk of developing comorbidities. Future research should be careful to consider social factors when examining gender differences in health outcomes
We provide real-time evidence on the impact of Covid-19 restrictions policies on conflicts globally. We combine daily information on conflict events and government policy responses to limit the spread of coronavirus to study how conflict levels vary following shutdown and lockdown policies. We use the staggered implementation of restriction policies across countries to identify their effect on conflict incidence and intensity. Our results show that imposing a nation-wide shutdown reduces the likelihood of daily conflict by around 9 percentage points. The reduction is driven by a drop in the incidence of battles, protests and violence against civilians. Across actors the decline is significant for conflicts involving political militias, protesters and civilians. We also observe a significant cross-country heterogeneity in the effect of restriction policies on conflict: no conflict reduction is observed in low income countries and in societies more fractionalized along ethnic or religious lines. We discuss the potential channels that can explain this heterogeneity.
Governments are starting to ease restrictions to economic activity. The risks of easing these measures too soon, or in misguided ways, are obvious, not only for public health but also for the economy. A world with no lockdown and a pandemic spreading rapidly through the population does not make for a healthy economy; nor, in all likelihood, does a world in which containment measures have to be repeatedly re-instated after being eased prematurely or in sub-optimal ways. We discuss some key economic issues that the UK government needs to face when thinking about how best to get people back into work: we assemble some basic empirical evidence, identify some challenges that policy-makers will need to confront, and discuss some policy considerations
Assuming that there is no other solution than herd immunity in front of the current pandemic, on which categories of citizens should we build this herd immunity? Given the fact that young people face a mortality rate which is at least a thousand times smaller than people aged 70 years and more, there is a simple rational to build it on these younger generations. The transfer of some mortality risk to younger people raises difficult ethical issues. However, none of the familiar moral or operational guidelines (equality of rights, VSL, QALY, ...) that have been used in the Western world over the last century weights the value of young lives 1000 times or more than the lives of the elders. This suggests that Society could offer covid protection to the elders by confining them as long as this herd immunity has not been attained by the younger generations. This would be a potent demonstration of intergenerational solidarity towards the most vulnerable people in our community. The welfare gain of this age-specific deconfinement strategy is huge, as it can reduce the global death toll by more than 80%.
This paper develops a Nash-equilibrium extension of the classic SIR model of infectious-disease epidemiology ("Nash SIR"), endogenizing people's decisions whether to engage in economic activity during a viral epidemic and allowing for complementarity in social-economic activity. An equilibrium epidemic is one in which Nash equilibrium behavior during the epidemic generates the epidemic. There may be multiple equilibrium epidemics, in which case the epidemic trajectory can be shaped through the coordination of expectations, in addition to other sorts of interventions such as stay-at-home orders and accelerated vaccine development. An algorithm is provided to compute all equilibrium epidemics.
In this paper we construct country specific indices of mobility and trust. We use Google Covid 19 Community Mobility Reports for the former, and World Values Survey and the European Values Study for the latter. We find that the trust index has some power in explaining mobility attitudes of nations, and trust increases mobility around workplaces, groceries/pharmacies, parks, and transit stations. We then present a model where people decide whether to stay at home or go out and if they go out how much effort to spend to protect themselves from the disease which has positive externalities on others. We assume that the effort cost of protection depends on the norm in the community and show that more people can go out when either the norm increases or people put more weight on it. Interpreting the weight on the norm as a measure of trust, our theory sheds light on the empirical findings.
This note outlines a simple method for estimating the spread of the COVID 19 virus in the absence of data on test results for a large, random sample of the population. It applies the method to the UK, and other countries, and finds that to match data on daily new cases of the virus, the estimated model favours high values for the number of people infected but asymptomatic. That result is very sensitive to whether the transmission rate of the virus is different for symptomatic and asymptomatic cases, something about which there is significant uncertainty. This illustrates how difficult it is to estimate the spread of the virus until very large samples of the population can be tested. Nonetheless, there is evidence that the infection may have spread far enough to mean that the trajectory of falling new cases could be maintained with some easing of restrictions.
Issue : 15
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 historical mortality from 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 considers a modification of the standard Susceptible-Infected-Recovered (SIR) model of epidemic that allows for different degrees of compulsory as well as voluntary social distancing. It is shown that the fraction of population that self-isolates varies with the perceived probability of contracting the disease. Implications of social distancing both on the epidemic and recession curves are investigated and their trade off is simulated under a number of different social distancing and economic participation scenarios. We show that mandating social distancing is very effective at flattening the epidemic curve but is costly in terms of employment loss. However, if targeted towards individuals most likely to spread the infection, the employment loss can be somewhat reduced. We also show that voluntary self-isolation driven by individual's perceived risk of becoming infected kicks in only towards the peak of the epidemic and has little or no impact on flattening the epidemic curve. Using available statistics and correcting for measurement errors, we estimate the rate of exposure to COVID-19 for 21 Chinese provinces and a selected number of countries. The exposure rates are generally small but vary considerably between Hubei and other Chinese provinces as well as across countries. Strikingly, the exposure rate in Hubei province is around 40 times larger than the rates for other Chinese provinces, with the exposure rates for some European countries being 3-5 times larger than Hubei (the epicenter of the epidemic). The paper also provides country-specific estimates of the recovery rate, showing it to be about 21 days (a week longer than the 14 days typically assumed), and relatively homogeneous across Chinese provinces and for a selected number of countries.
Using real-time register data we document the magnitude, dynamics and socio-economic characteristics of the crisis-induced temporary and permanent layoffs in Norway. We find evidence that the effects of social distancing measures quickly spread to industries that were not directly affected by policy. Close to 90% of layoffs are temporary, although this classification may change as the crisis progresses. Still, there is suggestive evidence of immediate stress on a subset of firms that manifests itself in permanent rather than temporary layoffs. We find that the shock had a strong socio-economic gradient, hit a financially vulnerable population, and parents with younger children, and was driven by layoffs in smaller, less productive, and financially weaker firms. Consequently though, the rise in unemployment likely overstates the loss of output associated with the layoffs by about a third.
We develop and calibrate a search-theoretic model of the labor market in order to forecast the evolution of the aggregate US labor market during and after the coronavirus pandemic. The model is designed to capture the heterogeneity of the transitions of individual workers across states of unemployment, employment and across different employers. The model is also designed to capture the trade-offs in the choice between temporary and permanent layoffs. Under reasonable parametrizations of the model, the lockdown instituted to prevent the spread of the novel coronavirus is shown to have long-lasting negative effects on unemployment. This is so because the lockdown disproportionately disrupts the employment of workers who need years to find stable jobs.
This paper presents an economic model of an epidemic in which susceptible individuals may engage in costly social distancing in order to avoid becoming infected. Infected individuals eventually recover and acquire immunity, thereby ceasing to be a source of infection to others. Under non-cooperative and forward-looking decision making, equilibrium social distancing arises endogenously around the peak of the epidemic, when disease prevalence reaches a critical threshold determined by preferences. Spontaneous, uncoordinated social distancing thus acts to flatten the curve of the epidemic by reducing peak prevalence. In equilibrium, social distancing stops once herd immunity sets in, but acts to extend the duration of the epidemic beyond the benchmark of a non-behavioral epidemiological model. Comparative statics with respect to the model parameters indicate that the curve becomes flatter(i) the more infectious the disease is and (ii) the more severe the health consequences of the disease are for the individuals.
We provide an initial assessment of the US Paycheck Protection Program by studying the 273 public firms that received a total of $929 million in loans between April 7 - 27, 2020. Despite receiving significant media coverage, these firms comprise 0.3% of the funds disbursed. Using guidelines specified by the US Small Business Administration, we document that about half of public firms were eligible to apply, of which 13% were eventual borrowers. Within the set of eligible firms, public firm borrowers tended to be smaller, have more employees, have fewer investment opportunities, have preexisting debt balances, and be located in a county with COVID-19 cases. Implementing additional eligibility requirements may help target funds towards the most constrained firm.
This paper analyzes the epidemiological and economic effects of quarantines. We use a basic epidemiological model, a SEIR-model, that is calibrated to roughly resemble the COVID-19 pandemic, and we assume that individuals that become infected or are isolated on average lose a share of their productivity. An early quarantine postpones but does not alter the course of the pandemic at a cost that increases in the duration and the extent of the quarantine. For quarantines at later stages of the pandemic there is a trade-off between lowering the peak level of infectious people on the one hand and minimizing fatalities and economic losses on the other hand. A longer quarantine dampens the peak level of infectious people and also reduces the total number of infected persons but increases economic losses. Both the peak level of infectious individuals and the total share of the population that will have been infected are U-shaped in relation to the share of the population in quarantine, while economic costs increase in this share. In particular, a quarantine covering a moderate share of the population leads to a lower peak, fewer deaths and lower economic costs, but it implies that the peak of the pandemic occurs earlier.
Less impacted than the rest of the world, the African continent is also facing the spread of Covid 19 and the numbers of confirmed cases are rising. This paper estimates the short-term impact of COVID-19 on poverty in Africa using the World Bank’s PovcalNet dataset. Three scenario were used including low, medium and high consumption contractions of 10%, 20% and 30%. The impact is estimated based on the US$ 1.90 per day poverty line. First, the impact of COVID-19 is estimated for the whole Africa. Secondly, to account for the regional heterogeneity, the impact is estimated separately for the five regions in Africa. The results indicate that the number of poor people in Africa would increase by between 59 – 200 million due to contractions in consumption as a result of COVID-19 pandemic. In all three scenario, West Africa and East Africa are the most affected by contractions in consumption due to the COVID-19 pandemic, while North Africa is the least affected among the five regions in Africa. The findings suggest that COVID-19 pandemic is a serious threat for achieving the Sustainable Development Goals (SDGs). Therefore, governments and international organizations should increase efforts in supporting the economic activities in all countries.
Issue : 14
We study the optimal lockdown policy for a planner who controls the fatalities of a pandemic while minimizing the output costs of the lockdown. The policy depends on the fraction of infected and susceptible in the population, prescribing a severe lockdown beginning two weeks after the outbreak, covering 60% of the population after a month, and gradually withdrawing to 20% of the population after 3 months. The intensity of the optimal lockdown depends on the gradient of the fatality rate with respect to the infected, and the availability of antibody testing that yields a welfare gain of 2% of GDP. We also analyze a test-tracing and quarantine (TTQ) policy. We find that TTQ is, in general, complementary to a lockdown.
The Covid-19 pandemic has motivated a myriad of studies and proposals on how economic policy should respond to this colossal shock. But participants in this debate seldom recognize that the health shock is not entirely exogenous. Its magnitude and dynamics themselves depend on economic policies, and the explicit or implicit incentives those policies provide. To illuminate the feedback loops between medical and economic factors we develop a minimal economic model of pandemics. In the model, as in reality, individual decisions to comply (or not) with virus-related public health directives depend on economic variables and incentives, which themselves respond to current economic policy and expectations of future policies. The analysis yields several practical lessons: because policies affect the speed of virus transmission via incentives, public health measures and economic policies can complement each other, reducing the cost of attaining desired social goals; expectations of expansionary macroeconomic policies during the recovery phase can help reduce the speed of infection, and hence the size of the health shock; the credibility of announced policies is key to rule out both self-fulfilling pessimistic expectations and time inconsistency problems. The analysis also yields a critique of the current use of SIR models for policy evaluation, in the spirit of Lucas (1976).
Health care practitioners around the globe have observed that the COVID19 crisis has been associated with an unprecedented decrease in non-COVID-19 visits to emergency departments. We corroborate this observation using administrative daily data from Chile and study the potential causes for this decrease. To that end, we merge regional emergency visits with Google mobility data and show that the crisis-induced changes in mobility patterns explain a significant portion of the overall drop in nonrespiratory emergency room visits, especially for visits related to trauma and poisoning. Our results reveal that an important reason for the dramatic drop in non-COVID-19 utilization of emergency care is the lower incidence of emergencies. This result suggests that lockdown measures may have the unexpected benefit for public health of freeing up healthcare resources to confront the pandemic.
We study the impact of non-pharmaceutical policy interventions (NPIs) like “stay-at-home” orders on the spread of infectious disease. NPIs are associated with slower growth of Covid-19 cases. NPIs “spillover” into other jurisdictions. NPIs are not associated with significantly worse economic outcomes measured by job losses. 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. The tradeoff between the economy and public health today depends strongly on what is happening elsewhere. This underscores the importance of coordinated economic and public health responses.
We quantify the exposure of major financial markets to news shocks about global contagion risk accounting for local epidemic conditions. For a wide cross section of countries, we construct a novel data set comprising (i) announcements related to COVID19, and (ii) high-frequency data on epidemic news diffused through Twitter. Across several classes of financial assets, we provide novel empirical evidence about financial dynamics (i) around epidemic announcements, (ii) at a daily frequency, and (iii) at an intra-daily frequency. Formal estimations based on both contagion data and social media activity about COVID19 confirm that the market price of contagion risk is very significant. We conclude that prudential policies aimed at mitigating either global contagion or local diffusion may be extremely valuable.
We construct a quantitative model of an economy hit by an epidemic. People differ by age and skill, and choose occupations and whether to commute to work or work from home, to maximize their income and minimize their fear of infection. Occupations differ by wage, infection risk, and the productivity loss when working from home. By setting the model parameters to replicate the progression of COVID-19 in South Korea and the United Kingdom, we obtain three key results. First, government-imposed lock-downs may not present a clear trade-off between GDP and public health, as commonly believed, even though its immediate effect is to reduce GDP and infections by forcing people to work from home. A premature lifting of the lock-down raises GDP temporarily, but infections rise over the next months to a level at which many people choose to work from home, where they are less productive, driven by the fear of infection. A longer lock-down eventually mitigates the GDP loss as well as flattens the infection curve. Second, if the UK had adopted South Korean policies, its GDP loss and infections would have been substantially smaller both in the short and the long run. This is not because Korea implemented policies sooner, but because aggressive testing and tracking more effectively reduce infections and disrupt the economy less than a blanket lock-down. Finally, low-skill workers and self-employed lose the most from the epidemic and also from the government policies. However, the policy of issuing “visas” to those who have antibodies will disproportionately benefit the low-skilled, by relieving them of the fear of infection and also by allowing them to get back to work.
At the onset of COVID-19 pandemic a large number of countries introduced a range of non-pharmaceutical interventions. Whereas the policies are similar across countries, country characteristics vary substantially. We examine the e↵ectiveness of such policies using a cross-country variation in economic, geographic and public health system characteristics. The effectiveness of lockdown policies is declining with GDP per capita, population density and surface area; and increasing with health expenditure and proportion of physicians in population. The findings can be explained by incentive driven behaviors and resource constraints. Higher population density, larger geographical area, and higher employment rate may require more resources to ensure compliance with lockdown policies. On the other hand, communities with access to better health care might be less likely to voluntary practice social distancing.
Millions of individuals are required to work from home as part of national efforts to fight COVID-19. To evaluate the employment impact of the pandemic, an important point is whether individuals are able to work from home. This paper estimates the share of jobs that can be performed at home in 23 Latin American and Caribbean (LAC) countries as well as examines the workers' characteristics associated with such jobs. To carry out this analysis, this paper uses rich harmonised household surveys and presents two measures of teleworkability. The first measure of the feasibility of working from home is borrowed from Dingel and Neiman (2020), while the second closely follows the methodology of Saltiel (2020). We use the second measure as our benchmark, as it is based on a more representative task content of occupations for LAC countries. We find that the share of individuals who are able to work from home varies from 7% in Guatemala to 16% in the Bahamas. We document considerable variation in the potential to work from home across occupations, industries, regions and workers' socioeconomic characteristics. Our results show that some individuals are better positioned to cope with the current situation than others. This highlights the need to assist the most vulnerable workers in the context of the global pandemic.
Issue : 13
We extend the baseline Susceptible-Exposed-Infectious-Recovered (SEIR) infectious disease epidemiology model to understand the role of testing and case-dependent quarantine. 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.
We argue that occupations are a key explanatory variable for understanding the early transmission of COVID-19 in New York City, finding that they play a larger role than other key demographics such as race or income. Moreover, we find no evidence that commuting patterns are significant after controlling for occupations. However, racial disparities still persist for Blacks and Hispanics compared to Whites, although their magnitudes are economically small. We perform a daily analysis over a range of one month to evaluate how different channels interact with the progression of the pandemic and the stay-at-home order. While the coefficient magnitudes of many occupations and demographics decrease, we find evidence consistent with higher intra-household contagion over time. Finally, our results also suggest that crowded spaces play a more important role than population density in the spread of COVID-19.
This article presents a spatial analysis of COVID-19 mortality rates across England and Wales in the early part of the pandemic (3/January/2020 to 27/March/2020). It assesses whether cultural and/or economic discrimination enhanced the vulnerability of groups of people and places. We used data on lung cancer deaths in non-pandemic times as an instrument for mortality rates during the pandemic and explored the relationship between COVID-19 mortalityrates and the Brexit vote. Results suggest that the effects of cultural discrimination on COVID-19-related mortality is five times greater than the economic effect, and deaths were 19 percent higher in pro-Brexit areas
Older adults and those with underlying medical conditions seem especially vulnerable to the COVID-19 pandemic. The U.S. government’s efforts to contain the infection, on the other hand, have a disproportionate impact on the working age population. To be able to capture the impact of the pandemic and the resulting mitigation efforts on a population that is heterogeneous by age, income and health status, we use an overlapping generations model that mimics the U.S. economy along those dimensions in 2020. We introduce an unexpected COVID-19 shock in the economy and examine the resulting impact on aggregate output, labor supply, savings, and consumption behavior of the different agents. We find that mitigation efforts that target certain age and health groups result in significantly smaller disruptions in the economy. Going forward, introducing subsidies to those with underlying health conditions and/or the elderly to self isolate might prove to be a useful path in opening up the economy.
We investigate the effects of the COVID-19-induced shock in financial markets on aggregate venue selection/market share and market quality. We find that the shock is linked with an economically significant loss of market share by dark pools to lit exchanges. In line with theory, the loss appears linked to an increase in lit market volatility and a search for immediacy by traders active in stocks with dark trading access. The market quality implications of the reduction in dark trading are mixed: while it tempers COVID-19- linked liquidity decline in the lit market, it exacerbates the loss of informational efficiency.
This study investigates the effectiveness of lockdown and testing in curbing the transmission of Covid-19 infection. Using a combination of data from the European Centre for Disease Prevention, Roser et al. (2020) and Hale et al. (2020), we carried cross-country analysis covering 69 countries across the 5 continents. To take care of the fact that the number of Covid-19 cases strongly depend on its own lag values, we used two-step system GMM for the estimations. Unlike prior studies that measure lockdown in terms of a fixed intervention date, we relied on the stringency index from Hale et al. (2020) that accommodates for the gradual lockdown measures in different countries. We found that an exogenous lockdown significantly affects the number of confirmed cases after 7 days of its implementation and its lag effects are intact even after 21 days of its implementation. A one unit change in the lockdown index decreases the total number of confirmed cases by 0.19 percent. Testing has no significant effects for at least 14 days after its implementation. However, after 21 days of its implementation, its effects become significant with −0.03 to −0.05 elasticity value.
The paper uses Google mobility data to identify the determinants of social distancing during the 2020 COVID-19 outbreak. We find for the U.S. that much of the decrease in mobility is voluntary driven by the number of COVID-19 cases and proxying for greater awareness of risk. Non-Pharmaceutical Interventions (NPI) such as closing nonessential business, sheltering in place, school closings are also effective, although with a total contribution dwarfed by the voluntary. This suggests that much social distancing will happen regardless of the presence of NPIs and that restrictions may often function more like a coordinating device among increasingly predisposed individuals than repressive measures per se. These results are consistent across countries income groups with only the poorest (LICs) showing limited effect of NPIs , and no voluntary component, consistent with resistance to abandon sources of livelihood. We also confirm the direct impact of the voluntary component on economic activity by showing that the majority of the fall in restaurant reservations in the U.S., and movie spending in Sweden occurred before the imposition of any NPIs. Widespread voluntary de-mobilization implies that releasing constraints may not yield a V shaped recovery if the reduction in COVID risk not credible.
Early indicators suggest that startup activity is heavily disrupted by the COVID-19 pandemic and the associated lockdown. At the same time, empirical evidence has shown that such disturbances may have long-lasting effects on aggregate employment. This paper presents a calculator which can be used to compute these effects under different scenarios regarding (i) the number of startups, (ii) the growth potential of startups, and (iii) the survival rate of young firms. We find that employment losses can be substantial and last for more than a decade, even when the assumed slump in startup activity is only short-lived.
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We test the theory that seller reputation moderates the effect of demand shocks on a seller's propensity to price gouge. From mid January to mid March 2020, 3M masks were priced 2.72 times higher than Amazon sold them in 2019. However, the difference (in price ratios) between a post-COVID-19 entrant and an established seller is estimated to be about 1.6 at times of maximum scarcity, that is, post-COVID-19 entrants price at approximately twice the level of established sellers. Similar results are obtained for Purell hand sanitizer. We also consider cumulative reviews as a measure of what a seller has to lose from damaging its reputation and, again, obtain similar results. Finally, we explore policy implications of our results.
This paper provides a critical review of models of the spread of the coronavirus (SARS-CoV 2) that have been influential in recent policy discussions. It notes potentially important features of the real-world environment that the standard models do not incorporate and discusses reasons why estimating critical parameters is difficult. These limitations may bias forecasts and lead forecasters to overstate confidence in their predictions. They also provide social scientists with opportunities to advance the literature and enable improved policies. This paper also discusses how optimal policies might depend on what is learned from new data and models.
What are the characteristics of workers in jobs likely to be initially affected by broad social distancing and later by narrower policy tailored to jobs with low risk of disease transmission? We use O/NET to construct a measure of the likelihood that jobs can be conducted from home (a variant of Dingel and Neiman, 2020) and a measure of low physical proximity to others at work. We validate the measures by showing how they relate to similar measures constructed using time use data from ATUS. Our main finding is that workers in low-workfrom-home or high-physical-proximity jobs are more economically vulnerable across various measures constructed from the CPS and PSID: they are less educated, of lower income, have fewer liquid assets relative to income, and are more likely renters. We further substantiate the measures with behavior during the epidemic. First, we show that MSAs with less pre-virus employment in work-fromhome jobs experienced smaller declines in the incidence of 'staying-athome', as measured using SafeGraph cell phone data. Second, we show that both occupations and types of workers predicted to be employed in low work-from-home jobs experienced greater declines in employment according to the March 2020 CPS. For example, non-college educated workers experienced a 4ppt larger decline in employment relative to those with a college degree.
The Covid-19 pandemic has disrupted working life in many ways, the negative consequences of which may be distributed unevenly under lockdown regulations. In this paper, we construct a new set of pandemic-related indices from the Occupational Information Network (O*NET) using factor analysis. The indices capture two key dimensions of job task requirements: (i) the extent to which jobs can be adaptable to work from home; and (ii) the degree of infection risk at workplace. The interaction of these two dimensions help identify which groups of workers are more vulnerable to income losses, and which groups of occupations pose more risk to public health. This information is crucial for both designing appropriate supporting programs and finding a strategy to reopen the economy while controlling the spread of the virus. In our application, we map the indices to the labor force survey of a developing country, Thailand, to analyze these new labor market risks. We document differences in job characteristics across income groups, at both individual and household levels. First, low income individuals tend to work in occupations that require less physical interaction (lower risk of infection) but are less adaptable to work from home (higher risk of income/job loss) than high income people. Second, the positive occupational sorting among low-income couples amplifies these differences at the household level. Consequently, low-income families tend to face a disproportionately larger risk of income/job loss from lockdown measures. In addition, the different exposure to infection and income risks between income groups can play an important role in shaping up the timing and optimal strategies to unlock the economy.
Citizens’ compliance with measures enacted by health authorities can have an important effect on the state of public health, particularly during epidemics. How much can political leaders influence compliance with such measures? In this paper, we analyze this question in the context of Brazil, where the president Jair Bolsonaro disrespected the recommendations and measures implemented by health authorities during a country-wide pro-government demonstration that took place amid the COVID-19 outbreak. We conclude that Bolsonaro’s behavior increased the pace of COVID-19 diffusion. In particular, after the day of the manifestations, the daily number of new COVID-19 is 19% higher in cities that concentrate Bolsonaro’s voters as compared to cities that concentrate opposition voters. In particular, after the day of the manifestations, the daily number of new COVID-19 is 19% higher in cities that concentrate Bolsonaro’s voters as compared to cities that concentrate opposition voters. The impact is verified even in cities where no demonstration took place, which indicates that the quicker spread of COVID-19 was not only due to people agglomerating during the manifestation, but also due to the changed behavior of Bolsonaro’s supporters regarding social distancing measures. We directly test this later mechanism exploring an index of social isolation and find that citizens’ compliance with social distancing decreased among pro-Bolsonaro cities after the demonstrations.
This paper provides evidence on the impact of major epidemics from the past two decades on income distribution. Our results justify the concern that the current pandemic could end up exerting a significant impact on inequality: past events of this kind, even though much smaller in scale, have led to increases in the Gini coefficient, raised the income shares of higher income deciles, and lowered the employment-to-population ratio for those with basic education compared to those with higher education. We provide some evidence that the distributional consequences from the current pandemic may be larger than those flowing from the historical pandemics in our sample.
We draw on mobile application data from 74 countries to document the effects of the COVID-19 pandemic on the adoption of digital finance and fintech. We estimate that the spread of COVID-19 and related government lockdowns have led to between a 24 and 32 percent increase in the relative rate of daily downloads of finance mobile applications in the sample countries. In absolute terms, this equates to an average daily increase of roughly 5.2 to 6.3 million application downloads and an aggregate increase of about 316 million app downloads since the pandemic’s outbreak, taking into account prior trends. Most regions across the world exhibit notable increases in absolute, relative, and per capita terms. Preliminary analysis of country-level characteristics suggest that market size and demographics, rather than level of economic development and ex-ante adoption rates, drive differential trends.
Tests are crucial to know about the number of people who have fallen ill with COVID-19 and to understand in real-time whether the dynamics of the pandemic is accelerating or decelerating. But tests are a scarce resource in many countries. The key but still open question is thus how to allocate tests across sub-national levels. We provide a data-driven and operational criterion to allocate tests efficiently across regions or provinces, with the view to maximize detection of people who have been infected. We apply our criterion to Italian regions and compute the shares of tests that should go to each region, which are shown to differ significantly from the actual distribution
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We analyze the externalities that arise when social and economic interactions transmit infectious diseases such as COVID-19. Public health measures are essential because individually rational agents do not internalize that they impose infection externalities upon others. 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 of 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: individually rational susceptible agents act cautiously to atten the curve of infections, but the disease is not overcome until herd immunity is acquired, with a deep recession and slow recovery lasting several years. By contrast, the socially optimal approach in our model isolates the infected and quickly contains the disease, producing a much milder recession. If the infected and susceptible cannot be targeted independently, then containment is far costlier: it remains optimal for standard statistical values of life but not if only the economic losses from lost lives are counted.
The COVID-19 pandemic and the subsequent lockdown brought about a massive slowdown of the economy and an unparalleled stock market crash. Using US data, this paper explores how firms with high Environmental and Social (ES) ratings fare during the first quarter of 2020 compared to other firms. We show that stocks with high ES ratings have significantly higher returns, lower return volatilities, and higher trading volumes than other stocks. Firms with high ES ratings and high advertising expenditures perform especially well during the crash. This paper highlights the importance of ES policies in making firms more resilient during a time of crisis.
This paper studies the effectiveness of big data technology in mitigating the economic and health impacts of the COVID-19 outbreak. I exploit the staggered implementation of contact-tracing apps called "health code" in 322 Chinese cities during the COVID-19 pandemic. Using high-frequency variations in population movements and greenhouse gas emissions across cities before and after the introduction of health code, I disentangle the effect of big data technology from confounding factors such as public sentiments and government responses. I find that big data technology significantly improves the tradeoff between human toll and economic costs. Cities adopting health code experience a significant increase in economic activities without suffering from higher infection rates. Overall, big data technology creates an economic value of 0.5%-0.75% of GDP during the COVID-19 outbreak in China.
I develop an extension of a canonical epidemiology model in which the policy in place determines the probability of transmission of an epidemic disease. I use the model to evaluate the effects of isolating symptomatic individuals, of increasing social distancing and of tests of different quality: a poor quality test that can only discriminate between healthy and infected individuals (such as polymerase chain reaction 'PCR' or Rapid Diagnostic Test), and a high quality test that is able to discriminate between immune and vulnerable healthy, and infected individuals (such as a serology test like Neutralization Assay). I find that isolating symptomatic individuals has a large effect at delaying and reducing the pick of infections. The combination of this policy with the poor quality test represents only a negligible improvement, whereas with the high quality test there is an additional delaying and reduction in the pick of infections. Social distancing alone cannot achieve similar effects without incurring in enormous output losses. I explore the combined effect of social distancing at early stages of the epidemic with a following period of tests and find that the best outcome is obtained with a light reduction of human interaction for about three months together with a subsequent test of the population over 40 days.
Public response to rising deaths from COVID-19 was immediate and, in many cases, drastic, leading to substantial economic and institutional costs. In this paper, I focus on mortality from COVID-19. Using crosscountry evidence and controlling for a variety of contributing factors, I find that increasing the number of hospital beds has a significant and quite substantial impact on mortality rates. Hospital beds likely capture the capacity of ICU, laboratories, and other hospital-related equipment. Facing a potential second or third wave of infection following an exit from lockdown policies, countries short on medical infrastructures should increase them immediately.
The COVID-19 pandemic has rattled the global economy and has required governments to undertake massive fiscal stimulus to prevent
the economic fallout of social distancing policies. In this paper, we compare the fiscal response of governments from around the world and its main determinants. We find sovereign credit ratings as one of the most critical factors determining their choice. First, the countries with one level worse rating announced 0.3 percentage points lower fiscal stimulus (as a percentage of their GDP). Second, these countries also delayed their fiscal stimulus by an average of 1.7 days. We identify 22 most vulnerable countries, based on their rating and stringency, and find that a stimulus equal to 1 percent of their GDP adds up to USD 87 billion. In order to fight the pandemic, long term loans from multilateral institutions can help these stimulus starved economies.
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We investigate the link between the Great Influenza Pandemic of 1918 and regional economic growth in Italy. The pandemic caused 600,000 deaths in Italy, a death rate of about 1.2%. We find that going from regions with the lowest mortality to the ones with the highest mortality is associated with a decline in GDP per capita growth of about 6.5%, an effect that dissipated within three years. We find limited evidence of mechanisms that may uncover long-term effects of the pandemic, such as human capital accumulation and industrialization. The severity of the pandemic in the less developed regions of the country, along with the limited implementation or largely ineffective measures of central and local interventions by public authorities, point to our estimates as indicative of an upper bound of the transitory adverse effect of pandemics on local economic growth.
The paper presents early research on problems that make the use of the basic SIR-model of epidemiology difficult for short- and medium-run policy. The model essentially generalizes the simple exponential model in two respects. First, it considers the structure of the infectious population in more detail and introduces the concept of the "cohort composition kernel" that generalizes the aggregate transmission function and renders the transmission model non-recursive. Second, it shows how policy measures such as testing, social distancing, or quarantine rules can affect this kernel and how this can provide estimates for the impact and lag of nonpharmaceutical policy interventions.
As a response to the COVID-19 pandemic, governments globally closed down major parts of their economies potentially plunging a vast majority of their firms into a liquidity crisis. Using a novel dataset of daily credit line drawdowns at the firm-loan-level, we study in a descriptive exercise the resulting “dash for cash” among firms and how the stock market priced firms differentially based on liquidity. In particular, we show that the U.S. stock market rewarded firms with access to liquidity through either cash or committed lines of credit from banks. AAAA rated firms, i.e., high-quality investment-grade firms, issued bonds in public capital markets, particularly after the Federal Reserve Bank initiated its corporate bond buying program. In contrast, bond issuances of BBB-rated firms, i.e., the lowest-rated investment-grade firms, remained mostly flat; instead, these firms rushed to convert their credit line commitments from banks into cash accounting for about half of all the credit line drawdowns. We document that consistent with the risk of becoming a fallen angel, this “dash for cash” has been driven by the lowest-quality BBB-rated firms. The risk of such precautionary drawdowns of credit lines remains an important consideration for stress-test based assessments of banking sector capital adequacy
To predict the effects of the 2020 U.S. ‘CARES’ act on consumption, we extend a model that matches responses of households to past consumption stimulus packages. The extension allows us to account for two novel features of the coronavirus crisis. First, during the lockdown, many types of spending are undesirable or impossible. Second, some of the jobs that disappear during the lockdown will not reappear when it is lifted. We estimate that, if the lockdown is short-lived, the combination of expanded unemployment insurance benefits and stimulus payments should be sufficient to allow a swift recovery in consumer spending to its pre-crisis levels. If the lockdown lasts longer, an extension of enhanced unemployment benefits will likely be necessary if consumption spending is to recover.
We study the epidemiology of Covid-19 using an overlapping-generations method in which successive cohorts of infectives are temporarily contagious. We use this method to estimate R0 (natural rate of contagion) and Re (effective rate of contagion) for Covid-19 in various countries and over time using data on confirmed morbidity and estimates of unconfirmed morbidity. We use these estimates to study the effect of mitigation policy on Re. We show that even in the absence of mitigation policy Re tends to decrease by week 3 of the epidemic due to endogenous social distancing. Several methods for estimating the treatment effect of mitigation policy on R suggest that mitigation policy accelerates the decrease in Re. A "Chinese crystal ball" method is proposed for projecting and simulating contagion with an empirical illustration for Israel.
The outbreak of Coronavirus named COVID-19 world has disrupted the Chinese economy and is spreading globally. The evolution of the disease and its economic impact is highly uncertain which makes it difficult for policymakers to formulate an appropriate macroeconomic policy response. In order to better understand possible economic outcomes, this paper explores seven different scenarios of how COVID-19 might evolve in the coming year using a modelling technique developed by Lee and McKibbin (2003) and extended by McKibbin and Sidorenko (2006). It examines the impacts of different scenarios on macroeconomic outcomes and financial markets in a global hybrid DSGE/CGE general equilibrium model. The scenarios in this paper demonstrate that even a contained outbreak could significantly impact the global economy in the short run. These scenarios demonstrate the scale of costs that might be avoided by greater investment in public health systems in all economies but particularly in less developed economies where health care systems are less developed and population density is high.
Issue : 9
We use a conventional dynamic economic model to integrate individual optimization, equilibrium interactions, and policy analysis into the canonical epidemiological model. Our tractable framework allows us to represent both equilibrium and optimal allocations as a set of differential equations that can jointly be solved with the epidemiological model in a unified fashion. Quantitatively, the laissez-faire equilibrium accounts for the decline in social activity we measure in US micro-data from SafeGraph. Relative to that, we highlight three key features of the optimal policy: it imposes immediate, discontinuous social distancing; it keeps social distancing in place for a long time or until treatment is found; and it is never extremely restrictive, keeping the effective reproduction number mildly above the share of the population susceptible to the disease.
We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic "unemployment," we show that out-ofsample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.
In this study, I discuss the role of international air traffic in spreading the new corona virus COVID-19 around the world, with a focus on travel restrictions. I build on a sample of 34 mostly European countries reporting international flights to 154 destination countries. This dataset is combined with information on daily reported cases of COVID-19 infections in these countries. I find that more connected countries registered first infection cases significantly earlier than less connected countries. This effect was reinforced by direct flight connections to China. I also show that severe travel restrictions were implemented relatively late in most countries. For a group of 120 countries included in the sample of analysis, three out of four countries already had more than 50 confirmed cases when travel restrictions were implemented. In contrast, very early implementations of air travel restrictions were associated with a delayed onset of infections. As a takeaway for future outbreaks of infectious diseases, the results suggests that the early implementation of travel restrictions could be key in slowing down the spread of infections around the world. The design of a global emergency stop in international travel requires a high level of coordination at a multilateral level in order to preserve supply chains as much as possible.
The British government has been debating how and when to escape from the lockdown without provoking a resurgence of the Covid-19 disease. There is a growing recognition of the damage the lockdown is causing to economic and social life, including deaths and illness amongst the non-infected population. This paper presents a simple cost-benefit analysis based on optimal control theory and incorporating the SIR model of disease propagation. It concludes by presenting some simulations informed by the theoretical discussion. The main conclusions are: (1) the lockdown should be continued for some weeks, and (2) if there is an inexpensive way of reducing the net reproductive rate of the disease to r = 1, this policy should be adopted within a few weeks of exiting lockdown. It is not cost-effective to linger in intermediate stages with more expensive policies designed to keep r well below unity with the hope eradicating the disease.
We study the impact of working from home on (i) infection risk in German regions and (ii) output using an input-output (IO) model of the German economy. We find that working from home is very effective in reducing infection risk: regions whose industry structure allows for a larger fraction of work to be done from home experienced much fewer Covid-19 cases and fatalities. Moreover, confinement is significantly more costly in terms of induced output loss in regions where the share of workers who can work from home is lower. When phasing out confinement, home office should be maintained as long as possible, to allow those workers who cannot work from home to go back to work, while keeping infection risk minimal. Finally, systemic industries (with high multipliers and/or high value added per worker) should be given priority, especially those where home office is not possible.
The outbreak of COVID-19 has significantly disrupted the economy. This paper attempts to quantify the macroeconomic impact of costly and deadly disasters in recent US history, and to translate these estimates into an analysis of the likely impact of COVID-19. A costly disaster series is constructed over the sample 1980:1-2020:04 and the dynamic impact of a disaster shock on economic activity and on uncertainty is studied using a VAR. While past natural disasters are local in nature and come and go quickly, COVID-19 is a global, multiperiod event. We therefore study the dynamic responses to a sequence of large disaster shocks. Even in a fairly conservative case where COVID-19 is a 5-month shock with its magnitude calibrated by the cost of March 2020 Coronavirus relief packages, the shock is forecast to lead to a cumulative loss in industrial production of 20% and in service sector employment of nearly 39% or 55 million jobs over the next 12 months. For each month that a shock of a given magnitude is prolonged from the base case, heightened macro uncertainty persists for another month.
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The largest economic cost of the COVID-19 pandemic could arise from changes in behavior long after the immediate health crisis is resolved. A potential source of such a long-lived change is scarring of beliefs, a persistent change in the perceived probability of an extreme, negative shock in the future. We show how to quantify the extent of such belief changes and determine their impact on future economic outcomes. We find that the long-run costs for the U.S. economy from this channel is many times higher than the estimates of the short-run losses in output. This suggests that, even if a vaccine cures everyone in a year, the COVID-19 crisis will leave its mark on the US economy for many years to come.
Motivated by reports in the media suggesting unequal access to Covid-19 testing across incomes, we analyze zip-code level data on the number of Covid-19 tests, test results, and income per capita in New York City. We find that the number of tests administered is evenly distributed across income levels. In particular, the test distribution across income levels is significantly more egalitarian than the distribution of income itself: The ten percent of the city’s population living in the richest zip codes received 11 percent of the Covid-19 tests and 29 percent of the city’s income. The ten percent of the city’s population living in the poorest zip codes received 10 percent of the tests but only 4 percent of the city’s income. At the same time, we find significant disparity in the fraction of tests that come back negative for the Covid-19 disease across income levels: moving from the poorest zip codes to the richest zip codes is associated with an increase in the fraction of negative Covid-19 test results from 38 to 65 percent.
This paper argues for the regular testing of members of at-risk groups more likely to be exposed to SARS-CoV-2 as a strategy for reducing the spread of Covid-19 and enabling the resumption of economic activity. We call this ‘stratified periodic testing’. It is ‘stratified’ as it is based on at-risk groups, and ‘periodic’ as everyone in the group is tested at regular intervals. We argue that this is a better use of scarce testing resources than ‘universal random testing’, as recently proposed by Paul Romer. We find that universal testing would require checking over 21 percent of the population every day to reduce the effective reproduction number of the epidemic, R’, down to 0.75 (as opposed to 7 percent as argued by Romer). We obtain this rate of testing using a corrected method for calculating the impact of an infectious person on others, where testing and isolation takes place, and where there is self-isolation of symptomatic cases. We also find that any delay between testing and the result being known significantly increases the effective reproduction number and that one day’s delay is equivalent to having a test that is 30 percent less accurate.
We study how the share of employment that can work from home changes with country income levels. We document that in urban areas, this share is only about 20% in poor countries, compared to close to 40% in rich ones. This result is driven by the self-employed workers: in poor countries their share of employment is large and their occupational composition not conducive to work from home. At the level of the entire country, the share of employment that can work from home in poor countries compared to rich countries depends on farmers' ability to work from home. This finding is due to the high agricultural employment share in poor countries.
This paper analyses the extent to which the Italian welfare system provides monetary compensation for those who lost their earnings due to the lockdown imposed by the government in order to contain the Covid-19 pandemic in March 2020. In assessing first-order effects of the businesses temporarily shut down and the government’s policy measures on household income, counterfactual scenarios are simulated with EUROMOD, the EU-wide microsimulation model, integrated with information on the workers who the lockdown is more likely to affect. This paper provides timely evidence on the differing degrees of relative and absolute resilience of the household incomes of the individuals affected by the lockdown. These arise from the variations in the protection offered by the tax-benefit system, coupled with personal and household circumstances of the individuals at risk of income loss.
Policies that curtail social and economic activities during a pandemic are predominantly decided upon at the national level, but have international ramifications. In this paper we examine what type of inefficiencies this may create and how cooperation across countries may improve outcomes. We find that inefficiencies arise even among completely identical countries. We show that countries are likely to choose excessively lenient policies from the perspective of world welfare in later stages of the pandemic. This provides a rationale for setting minimum containment standards internationally. By contrast, in early and intermediate stages of the pandemic, national containment policies may also be excessively strict. Whether or not this is the case depends on a country's degree of economic integration relative to (outward and inward) mobility of people. Analyzing the stringency of containment policies during the current epidemic confirms that countries with higher economic integration adopt stringent containment policies more quickly whereas countries subject to high mobility do so later.
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This paper develops and implements a method to monetize the impact of moderate social distancing on deaths from Covid-19. Using the Ferguson et al. (2020) simulation model of Covid-19’s spread and mortality impacts in the United States, we project that three to four months of moderate distancing beginning in late March 2020 would save 1.7 million lives by October 1. Of the lives saved, 630,000 are due to avoided overwhelming of hospital intensive care units. Using the projected age-specific reductions in death and age-varying estimates of the United States Government’s value of a statistical life, we find that the mortality benefits of social distancing are about $8 trillion or $60,000 per US household. Roughly nine-tenths of the monetized benefits are projected to accrue to people age 50 or older. Overall, the analysis suggests that social distancing initiatives and policies in response to the Covid-19 epidemic have substantial economic benefits.
In a pandemic recession an extraordinary monetary policy – helicopter money – can be considered. If we define helicopter money as a monetization of irredeemable fiscal transfers to citizens that produces losses in the central bank balance sheet, and an independent
central bank acts as a long-sighted policymaker, an optimal helicopter monetary policy can be identified. Yet, if the government in charge is made up of career-concerned politicians and citizens are heterogenous, the policy mix will produce distributional effects, and conflicts between politicians and central bankers will be likely. Political pressures will arise and the optimal helicopter money option will be less likely. The framework is applied in a discussion of the economics and politics of issuing COVID-19 perpetual bonds with the European Central Bank as the buyer.
This paper introduces a dynamic panel SIR (DP-SIR) model to investigate the impact of non-pharmaceutical interventions (NPIs) on the COVID-19 transmission dynamics with panel data from 9 countries across the globe. By constructing scenarios with different combinations of NPIs, our empirical findings suggest that countries may avoid the lockdown policy with imposing school closure, mask wearing and centralized quarantine to reach similar outcomes on controlling the COVID-19 infection. Our results also suggest that, as of April 4th, 2020, certain countries such as the U.S. and Singapore may require additional measures of NPIs in order to control disease transmissions more effectively, while other countries may cautiously consider to gradually lift some NPIs to mitigate the costs to the overall economy.
We embed a lockdown choice in a simplified epidemiological model and derive formulas for the optimal lockdown intensity and duration. The optimal policy reflects the rate of time preference, epidemiological factors, the hazard rate of vaccine discovery, learning effects in the health care sector, and the severity of output losses due to a lockdown. In our baseline specification a Covid-19 shock as currently experienced by the US optimally triggers a reduction in economic activity by two thirds, for about 50 days, or approximately 9.5 percent of annual GDP.
This paper uses transaction-level customer data from the largest bank in Denmark to estimate consumer responses to the COVID-19 pandemic and the partial shutdown of the economy. We find that aggregate card spending has dropped sharply by around 25% following the shutdown. The drop is mostly concentrated on goods and services whose supply is directly restricted by the shutdown, suggesting a limited role for spillovers to non-restricted sectors through demand in the short term. The spending drop is somewhat larger for individuals more exposed to the economic risks and health risks introduced by the COVID-19 crisis; however, pre-crisis spending shares in the restricted sectors is a much stronger correlate of spending responses.
Are lockdown policies effective at inducing physical distancing to counter the spread of COVID-19? Can less restrictive measures that rely on voluntary community action achieve a similar effect? Using data from 40 million mobile devices, we find that a lockdown increases the percentage of people who stay at home by 8% across US counties. Grouping states with similar outbreak trajectories together and using an instrumental variables approach, we show that time spent at home can increase by as much as 39%. Moreover, we show that individuals engage in limited physical distancing even in the absence of such policies, once the virus takes hold in their area. Our analysis suggests that non-causal estimates of lockdown policies’ effects can yield biased results. We show that counties where people have less distrust in science, are more highly educated, or have higher incomes see a substantially higher uptake of voluntary physical distancing. This suggests that the targeted promotion of distancing among less responsive groups may be as effective as across-the-board lockdowns, while also being less damaging to the economy.
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This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the response to the novel Coronavirus in the United States. The WEI shows a strong and sudden decline in economic activity starting in the week ending March 21, 2020. In the most recent week ending April 4, the WEI indicates economic activity has fallen further to -8.89% scaled to 4 quarter growth in GDP.
Many countries are shutting non-essential sectors of the economy to slow the spread of Covid-19. Older individuals have most to gain from slowing virus diffusion. Younger workers in sectors that are shuttered have the most to lose. In this paper we extend a standard epidemiological model of disease progression to include heterogeneity by age, and multiple sources of disease transmission. We then incorporate the epidemiological block into a multisector economic model in which workers differ by sector (basic and luxury) as well as by health status. We study optimal mitigation policies of a utilitarian government that can redistribute resources across individuals, but where such redistribution is costly. We show that optimal redistribution and mitigation policies interact, and reflect a compromise between the strongly diverging preferred policy paths across the subgroups of the population
We provide quantitative predictions of first-order supply and demand shocks for the US economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyze the supply shock, we classify industries as essential or non-essential and construct a Remote Labor Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 22% of the US economy's GDP, jeopardise 24% of jobs and reduce total wage income by 17%. At the industry level, sectors such as transport are likely to have output constrained by demand shocks, while sectors relating to manufacturing, mining and services are more likely to be constrained by supply shocks. Entertainment, restaurants and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from adverse supply and demandside shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks – we expect them to be substantially amplified by feedback effects in the production network.
In this paper, I examine the feasibility of working from home in developing countries. I take advantage of worker-level data from the STEP survey, which collects comparable information on employment outcomes across ten countries. I use information on workers' tasks to define the feasibility of working from home following Dingel and Neiman (2020). I extend the nascent literature on this topic by providing comparable cross-country evidence on the feasibility of telework. Only 13% of workers in STEP countries could work from home, yet this share ranges from 5.5% in Ghana to 23% in Yunnan (China). The feasibility of working from home is positively correlated with high-paying occupations. Educational attainment, formal employment status and household wealth are positively associated with the possibility of working from home, reflecting the vulnerability of various groups of workers. These relationships remain significant within narrowly defined occupations, yet exhibit heterogeneity across countries. I remark on the importance of rapidly identifying vulnerable workers to design adequate policies to combat the negative employment impacts of COVID-19.
Are pandemics systemically important to modern-day financial markets? This study uses the COVID-19 pandemic as a natural experiment for testing how large-scale pandemics affect the financial markets. Using hand-collected data at the firm level, I find that managers systematically underestimated their exposure to pandemics in their SEC-mandated risk factors, and the vast majority of firms decreased in value at the pandemic's onset. I also find that the pandemic triggered unprecedented changes in U.S. employment levels and the values of bonds, commodities, and currencies. These types of findings suggest that pandemics are systemically important to the financial markets. Overall, this study provides some of the first large-scale evidence on how pandemics affect the financial markets.
We combine high-quality vital statistics data with annual income data at the municipality level to study the economic aftermath of the 1918-inuenza epidemic in Denmark. Controlling for pre-epidemic trends, we find that more severely affected municipalities experienced short-run declines in income, suggesting that the epidemic led to a V-shaped recession, with relatively moderate, negative effects and a full recovery after 2-3 years. Month-by-industry unemployment data shows that unemployment rates were high during the epidemic, but decreased again only a couple of months after it receded. This evidence also indicates that part of the economic downturn in 1918 predates the epidemic.
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Since the outbreak of the Covid-19 pandemic economists have turned to the SIR model and its subsequent variants for the study of the pandemic's economic impact. But the SIR model is lacking the optimising behaviour of economic models, in which agents can influence future transitions with their present actions. We borrow ideas and modelling techniques from the Mortensen-Pissarides (1994) search and matching model and show that there is a well-defined solution in line with the original claims of Kermack and McKendrick (1927) but in which incentives play a role in determining the transitions. There are also externalities that justify government intervention in the form of imposing more restrictions on actions outside the home than a decentralised equilibrium would yield.
In this paper we argue that endogenous shifts in private consumption behaviour 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 the 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 reallocation of economic activity: the curve not only just flattens, it gets reversed.
We provide perspective on the possible global economic and financial effects from COVID-19 by examining the handful of similar major health crises in the 21st century. We estimate the effects of these disease shock episodes on GDP growth, fiscal policy, expectations, financial markets, and corporate activity. Simple time-series models of GDP growth indicate that real GDP is 2.6% lower on average across 210 countries in the year of the official declaration of the outbreak and is still 3% below its pre-shock level five years later. The negative effect on GDP is felt less in countries with more aggressive first-year responses in government spending. Consensus forecast data suggests a pessimistic view on real GDP initially that lasts for two months, an effect that is larger for emerging market economies. Stock market responses indicate an immediate negative reaction. Finally, using firm-level data, we find a fall in corporate profitability and employment, and an increase in debt, the last of which is further reflected in higher sovereign CDS spreads.
As an alternative to structural estimates of the SIR model, this note presents reduced-form time series forecasts of the growth in Covid-19 cases s and fatalities d for several countries where a slowdown has set in. Once a daily threshold of d >100 was crossed, daily growth in the initial unchecked phase was around Δln(d) ∈ [0.2,0.3]. For several countries, growth in fatalities as well as registered cases now shows a sustained decline; Italy and Spain report Δln(d) ≈ 0.03. I present updated ETS forecasts of the endpoint to the current epidemic for the countries in the sample, along with predicted fatalities. As a robustness check, forecasts from 31 March alongside later realizations are included. Results are preliminary and subject to daily revision as the situation is still evolving rapidly. The relative success of the method suggests univariate forecasts as a quick way of assessing resource needs and timelines where the epidemic is still ongoing.
We conducted a repeated survey on risk taking behavior across a panel of subjects in Wuhan, China – ground zero of the Coronavirus pandemic – before and after the outbreak began. Our baseline survey was administered on October 16th, 2019 among graduate students in Wuhan prior to the COVID-19 outbreak. 47% of the students in our sample returned home to other provinces in China for semester break in early January before the province of Hubei and the city of Wuhan was locked down with strict quarantine orders on 23 January 2020. We administered a follow up survey to the same subjects, capturing their geolocation information on 28 February. We use variation in exposure across different Chinese cities and provinces to measure the impact of the Coronavirus pandemic on subjects’ willingness to take risk. We find that subjects’ allocations of wealth to hypothetical risky investments decrease monotonically based on the strength of their exposure to the pandemic. However, subjects uniformly report substantially lower general preferences for risk regardless of their exposure. Higher levels of exposure leads subjects to reduce beliefs in their own luck and sense of control and in turn, form more pessimistic beliefs on the economy and social conditions. We provide evidence that short-term changes in risk taking may stem more so from changes in beliefs and optimism than from general risk preferences. Our results suggest that more closely held formative experiences have large, negative, and acute effects on economic preferences during a crisis.
We recommend the immediate universal adoption of cloth facemasks, including homemade masks, and accompanying policies to increase the supply of medical masks for health workers. Universal adoption will likely slow the spread of the Covid-19 virus by reducing transmission from asymptomatic individuals. We provide strongly suggestive evidence from cross-country data that facemask use slows the growth rate of cases and deaths. This complements extant scientific data on mask usage. Our analysis suggests each cloth facemask generates thousands of dollars in value from reduced mortality risk. Each medical mask, when used by a healthcare worker, may generate millions of dollars in value, and policies to encourage greater production prioritised for health workers are urgently needed.
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We exploit unexpected changes in the trajectory of pandemics to quantify their effects on aggregate and firm-level stock returns. We find that an unanticipated doubling of predicted infections during the Covid-19 and SARS outbreaks forecasts aggregate equity market value declines of 4% to 11%. Firm returns are sensitive to this information even after accounting for their co-movement with the market, and vary widely both within and across sectors. Our results imply a decline in returns' reaction to new infections as the trajectory of the pandemic becomes clearer.
We study the response of an economy to an unexpected epidemic and we compare the decentralised equilibrium with the efficient allocation. Households mitigate the spread of the disease by reducing consumption and hours worked. A social planner worries about two externalities: an infection externality and a healthcare congestion externality. Private agents’ mitigation incentives are too weak, especially at early stages while the planner implements drastic and front-loaded mitigation policies. In our calibration, assuming a CFR of 1% and an initial infection rate of 0.1%, private mitigation leads to a 10% drop in consumption and reduces the cumulative death rate from 2.5% of the initially susceptible population to about 2%. The planner reduces the death rate to 0.2% at the cost of an initial drop in consumption of around 40%.
The standard SEIR model based on a parameterisation consistent with the international evidence cannot explain the very high Covid-19-related mortality in Lombardy. This paper proposes an extension of the standard SEIR model that is capable of solving the puzzle. The SEIR model features exogenous mortality: once susceptible individuals are first exposed, and then infected, they succumb with a given probability. The extended model inlcudes a hospitlisation process and the possibility that hospitalised patients, who need to resort to an intensive care unit, cannot find availability because the ICU is saturated. This constraint creates an additional increase in mortality, which is endogenous to the diffusion of the disease. The SEIHCR model (H stands for hospitalisation and C stands for constraint) is capable of explaining the dynamics of Covid-19-related mortality in Lombardy with a paramerisation consistent with the international evidence.
The economic downturn caused by the current Covid-19 outbreak has substantial implications for gender equality, both during the downturn and the subsequent recovery. Compared to 'regular' recessions, which affect men’s employment more severely than women’s employment, the employment drop related to social distancing measures has a large impact on sectors with high female employment shares. In addition, closures of schools and daycare centers have massively increased child care needs, which has a particularly large impact on working mothers. The effects of the crisis on working mothers are likely to be persistent, due to high returns to experience in the labour market. Beyond the immediate crisis, there are opposing forces which may ultimately promote gender equality in the labour market. First, businesses are rapidly adopting flexible work arrangements, which are likely to persist. Second, there are also many fathers who now have to take primary responsibility for child care, which may erode social norms that currently lead to a lopsided distribution of the division of labour in house work and child care.
We combine GPS data on changes in average distance travelled by individuals at the county level with Covid-19 case data and other demographic information to estimate how individual mobility is affected by local disease prevalence and restriction orders to stay at home. We find that a rise of local infection rate from 0% to 0.003%4 is associated with a reduction in mobility by 2.31%. An official stay-at-home restriction order corresponds to reducing mobility by 7.87%. Counties with larger shares of population over age 65, lower share of votes for the Republican Party in the 2016 presidential election, and higher population density are more responsive to disease prevalence and restriction orders.
Social distancing is vital to mitigate the spread of the novel coronavirus. We use geolocation data to document that political beliefs present a significant limitation to the effectiveness of state-level social distancing orders. Residents in Republican counties are less likely to completely stay at home after a state order has been implemented relative to those in Democratic counties. We also find that Democrats are less likely to respond to a state-level order when it is issued by a Republican governor relative to one issued by a Democratic governor. These results are robust to controlling for other factors including time, geography, local Covid-19 cases and deaths, and other social distancing orders. We conclude that bipartisan support is essential to maximise the effectiveness of social distancing orders.
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This note lays out the basic Susceptible-Infected-Recovered (SIR) epidemiological model of contagion, with a target audience of economists who want a framework for understanding the effects of social distancing and containment policies on the evolution of contagion and interactions with the economy. A key parameter, the asymptomatic rate (the fraction of the infected that are not tested under current guidelines), is not well estimated in the literature because tests for the coronavirus have been targeted at the sick and vulnerable, however it could be estimated by random sampling of the population. In this simple model, different policies that yield the same transmission rate β have the same health outcomes but can have very different economic costs. Thus, one way to frame the economics of shutdown policy is as finding the most efficient policies to achieve a given β, then determining the path of β that trades off the economic cost against the cost of excess lives lost by overwhelming the health care system.
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 neighbourhoods were less likely to be tested; but the likelihood that a test was positive was larger in those neighbourhoods, as well as in neighbourhoods 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 neighbourhoods 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.
In this paper, we conduct a comprehensive review of different economic policy measures adopted by 166 countries as a response to the COVID-19 pandemic and create a large database including fiscal, monetary, and exchange rate measures. Furthermore, using principle component analysis (PCA), we construct a COVID-19 Economic Stimulus Index (CESI) that combines all adopted policy measures. This index standardises economic responses taken by governments and allows us to study cross-country differences in policies. Finally, using simple cross-country OLS regressions we report that the median age of the population, the number of hospital beds per-capita, GDP per-capita, and the number of total cases are all significantly associated with the extent of countries’ economic policy responses.
What is the impact of anxiety on vote choice? Building on a well-documented phenomenon in finance, we posit that voters will exhibit a “flight to safety” by turning toward establishment candidates. We test this theory in the context of the Democratic primary election of 2020 by examining changes in the vote shares of Bernie Sanders, a candidate promising disruptive change. We use the outbreak of the novel coronavirus across both space and time to identify a causal effect of the outbreak on voting. By comparing counties with and without reported cases in their local media market, before and after the outbreak of the virus, we show that COVID-19 resulted in diminished support for Sanders as compared to his support in the 2016 election, and interpret this to be the result of COVID-induced anxiety altering vote choice. We test alternative mechanisms, such as differential changes in turnout by age groups more and less supportive of Sanders, selection effects in which areas less supportive of Sanders were more exposed, and the coincident timing of
the outbreak with the Democratic party rallying around Biden. We find little support for these alternative pathways, bolstering our claim that the results are consistent with a political flight to safety. Our findings suggest an as-yet underappreciated preference for “safe” candidates in times of social anxiety.
The health crisis caused by the outbreak of the Covid-19 virus has led many countries to implement drastic measures of social distancing. By reducing the quantity of labour, social distancing in turn leads to a drop in output which is difficult to quantify without taking into account relationships between sectors. Starting from a standard model of production networks, we analyse the sectoral effects of the shock in the case of France. We estimate that six weeks of social distancing brings GDP down by 5.6%. Apart from sectors directly impacted by social distancing measures, those whose value-added decreases the most are upstream sectors, i.e. sectors most distant from final demand. The same exercise is carried out for other European countries, taking into account national differences in sectoral composition and propensity to telework. Finally, we analyse the economic impact of phasing out social distancing by sector, region or age group.
We measure the economic risk of Covid-19 at a geo-spatially detailed resolution. In addition to data about the current prevalence of confirmed cases, we use data from 2014-2018 to compute measures for exposure, vulnerability, and resilience of the local economy to the shock of the epidemic. Using a battery of proxies for these four concepts, we calculate the hazard and the principal components of exposure and vulnerability to it, and of the economy’s resilience (i.e., its ability to recover rapidly from the shock). We find that
the economic risk of this pandemic is particularly high in most of Africa, the Indian subcontinent, the Persian Gulf, and Southeast Asia. These results are consistent when comparing an ad hoc equal weighting algorithm for the four components of the index, an algorithm that assumes equal hazard for all countries, and one based on an estimated weights using previous aggregated disability-adjusted life years losses associated with communicable diseases.
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Facing visible strain in dollar funding markets during the Covid-19 pandemic, the Fed lowered the rate on the swap lines it had with five other central banks, and opened new ones in nine other currencies. Some of these were used, some not. We use this variation to show the impact of the swap lines on CIP deviations across currencies. The results confirm the analysis in
Bahaj and Reis (2019): the swap lines put a ceiling on CIP rates only around the time of an auction.
Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence on the role of human interactions across different lines
of business and about which will be the most limited by social distancing. In this paper we provide theory-based measures of the reliance of US businesses on human interaction, detailed by industry and geographic location. We find that 49 million workers work in occupations that rely heavily on faceto-face communication or require close physical proximity to other workers. Our model suggests that when businesses are forced to reduce worker contacts by half, they need a 12% wage subsidy to compensate for the disruption in communication. Retail, hotels and restaurants, arts and entertainment and
schools are the most affected sectors. Our results can help target fiscal assistance to businesses that are most disrupted by social distancing.
It is well-known that group testing is an efficient strategy to screen for the presence of a virus. It consists of pooling n individual samples with a single test using RT-PCR. If no individual in the group is infected, the group test is negative. Thus, a single test may reveal this crucial information. We show how group testing can be optimised in three applications to multiply the power of tests against Covid-19: Estimating virus prevalence to measure the evolution of the pandemic, bringing negative groups back to work to exit the current lockdown, and testing for individual infectious status to treat sick people. For an infection level around 2%, group testing could multiply the power of testing by a factor of 20. The implementation of this strategy in the short run requires limited investments and could bypass the current immense shortage of testing capacity.
This study quantifies the economic effect of a possible lockdown of Tokyo to prevent the spread of Covid-19. The negative effect of the lockdown may propagate to other regions through supply chains because of a shortage of supply and demand. Applying an agent-based model to the actual supply chains of nearly 1.6 million firms in Japan, we simulate what would happen to production activities outside Tokyo when production activities that are not essential to citizens’ survival in Tokyo were shut down for a certain period. We find that when Tokyo is locked down for a month, the indirect effect on
other regions would be twice as large as the direct effect on Tokyo, leading to a total production loss of 27 trillion yen in Japan, or 5.3% of its annual GDP. Although the shut down in Tokyo accounts for 21% of the total production in
Japan, the lockdown would result in a reduction in the daily production in Japan of 86% in a month.
How many jobs can be carried out without putting workers at risk of contracting Covid-19? And how many of these jobs can be activated as soon as the most severe restrictions to mobility will be lifted? To which extent do these jobs belong to the chain involved in the war against Covid-19? In this paper, we aim to provide preliminary answers to these questions drawing on the case of Italy, the first Western country to be hit by the pandemic.
I use a dynamic stochastic general equilibrium model to study the effects of the 2019-20 coronavirus pandemic in the US. The pandemic is modelled as a large negative shock to the utility of consumption of contact-intensive services. General equilibrium forces propagate this negative shock to the nonservices and financial sectors, triggering a deep recession. I use a calibrated version of the model to analyse different types of fiscal policies: (i) government purchases, (ii) income tax cuts, (iii) unemployment insurance benefits, (iv) unconditional transfers, and (v) liquidity assistance to service firms. I find
that UI benefits are the most effective tool to stabilize income for borrowers, who are hit the hardest, while savers favour unconditional transfers. Liquidity assistance programs are effective if the policy objective is to stabilize employment in the affected sector.
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How do major pandemics affect economic activity in the medium to longer term? Is it consistent with what economic theory prescribes? Since these are rare events, historical evidence over many centuries is required. We study rates of return on assets using a dataset stretching back to the 14th century, focusing on 15 major pandemics where more than 100,000 people died. In addition, we include major armed conflicts that resulted in a similarly large death toll. Significant macroeconomic after-effects of the pandemics persist for about 40 years, with real rates of return substantially depressed. In contrast, we find that wars have no such effect, indeed the opposite. This is consistent with the destruction of capital that happens in wars, but not in pandemics. Using more sparse data, we find real wages somewhat elevated following pandemics. The findings are consistent with pandemics inducing labor scarcity and/or a shift to greater precautionary savings.
We classify the feasibility of working at home for all occupations. About 34% of US jobs, accounting for 44% of overall wages, can plausibly be performed at home. This share varies considerably across cities and industries. We hope our results are useful for understanding the economic impact of social distancing and formulating policy responses. Our classifications could be easiy applied to other countries.
We investigate the impact of the German public shutdown from 13 March 2020 on the spread of Covid-19. In a simple model, we search for a trend break in cumulated confirmed Covid-19 cases as reported by the Johns Hopkins University (2020). We identify a trend break on 20 March that is in line with the expected lagged impact of the German policies and which reduces the growth rate by 48.2%. While the growth rate has almost halved, the number of cases is still doubling every 5.35 days.
No previous infectious disease outbreak, including the Spanish flu, has impacted the stock market as powerfully as the Covid-19 pandemic. We use text-based methods to develop this point with respect to large daily stock market moves back to 1900 and with respect to overall stock market volatility back to 1985. We also argue that policy responses to the Covid-19 pandemic provide the most compelling explanation for its unprecedented stock market
I estimate the susceptible-infected-recovered (SIR) epidemic model for the novel coronavirus disease (Covid-19). The transmission rate is heterogeneous across countries and far exceeds the recovery rate, which enables a fast spread.
In the benchmark model, 24.4% of the population may be simultaneously infected at the peak, potentially overwhelming the healthcare system. The peak reduces to 5.6% under the optimal mitigation policy that controls the timing and intensity of social distancing. A stylised asset pricing model suggests that the stock price temporarily decreases by 50% in the benchmark case but shows a W-shaped, moderate but longer bear market under the optimal policy.
The lack of anticipation of a worldwide disruptive event such as the spread of Covid-19, combined with the breakdown of market mechanisms for the most essential products needed to fight the disease, has left the governments of many countries unsure of how to react and has often constrained their ability to make strategic choices. The humanitarian goal of saving as many lives as possible has come, in some countries, at the cost of confining the entire population, considered the only option available given the circumstances. The economic cost of such a solution, which brings the economies of these countries to a standstill and disrupts global value chains, is likely to be followed by several years of economic depression that will dwarf the cost of the 2008 financial and economic crisis. In the light of this experience, this paper revisits some
of the implicit assumptions underlying the design of our economic systems and discusses some of the dilemmas and trade-offs faced during this stressful period. The lessons learned could help us better anticipate or deal with future black swan events.