As people and economies around the world reel from the impact of the novel coronavirus (COVID-19), one thing is clear: facts are at a premium. The value of trusted data has never been more in evidence than in the months since the onset of COVID-19 in China at the end of 2019, and its rapid spread around the world.
I have been struck time and time again by how much my colleagues want to contribute to finding solutions to the COVID-19 crisis. Yet, we are not qualified to develop a vaccine or to treat those who are suffering. However, economists at the University of Chicago, with their grounding in rigorous research and commitment to public policy, are uniquely positioned to offer insights into the ongoing economic challenges occasioned by this historic health crisis.
So, we decided that what BFI could contribute is a set of facts about COVID-19 that we believe can help people better understand its consequences and potential policy responses. Specifically, we aim to deliver key economic insights that are often missing from policy discussions. The economic implications of COVID-19 are significant and varied, and we address a range of questions: What is the economic benefit of social distancing? What would the impacts of universal testing for COVID-19 be for mortality rates and economic outcomes? Which sectors will be hardest hit? What do the latest stock market gyrations tell us about the expectations for growth? What can China teach us about the economic implications of widescale lockdowns? The answers to these and other important questions are addressed in the following selected facts.
This is a dynamic effort. And in this signal social and economic period, BFI will continue to develop, update, and communicate facts as part of our contribution to minimizing COVID-19’s harm to people and society.
Please visit this page regularly for updates.
Director of the Becker Friedman Institute for Economics
Milton Friedman Distinguished Service Professor of Economics
- Policy Response
The steep drop in US economic activity in recent months has been driven in large part by the fall-off in consumer spending at retail stores, restaurants, entertainment spots, and other social venues. This decline in spending has roughly correlated with government shelter-in-place (SIP) orders, and has given rise to fierce debates over “re-opening” the economy. Were the various lockdown orders worth the economic pain of slowing the spread of the virus? When, and how fast, should economies reopen?
These questions presume that SIP orders were the primary determinant in keeping consumers at home. However, using data on foot traffic at 2.25 million individual businesses across the United States (including 110 industry groupings), the authors find that while total foot traffic fell by 60 percentage points, legal restrictions explain only around 7 percentage points of this decline. In other words, people were staying home on their own, and when they did go shopping, the authors found that consumers avoided larger, high-traffic businesses. Given the richness of their data set, and described in detail in their accompanying paper, the authors are able to compare, for example, two similar establishments within a commuting zone but on opposite sides of an SIP order. In such a case, both establishments saw enormous drops in customer activity, but the one on the SIP side saw a drop that was only about one-tenth larger.
Interestingly, and further supporting the modest size of the estimated SIP effects, when some states and counties repealed their shutdown orders toward the end of the authors’ sample, the recovery in economic activity due to the repeal was equal in size to the decline at imposition. Thus, the recovery is limited not so much by policy per se as the reluctance of individuals to engage in social economic activity.
- Public Health & the Economy
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To address the gap in critical, real-time information about COVID-19’s effects on US income and poverty (official estimates will not be available until September 2021), the authors constructed new measures of income distribution and income-based poverty with a lag of only a few weeks, using high frequency data for a large, representative sample of US families and individuals. The authors relied on the Basic Monthly Current Population Survey (Monthly CPS), which includes a greatly underused global question about annual family income, and which allows them to determine the immediate impact of macroeconomic conditions and government policies.
The authors’ initial evidence indicates that, at the start of the pandemic, government policy effectively countered its effects on incomes, leading poverty to fall and low percentiles of income to rise across a range of demographic groups and geographies. Their evidence suggests that income poverty fell shortly after the start of the COVID-19 pandemic in the US. In particular, the poverty rate, calculated each month by comparing family incomes for the past twelve months to the official poverty thresholds, fell by 2.3 percentage points, from 10.9 percent in the months leading up to the pandemic (January and February) to 8.6 percent in the two most recent months (April and May). This decline in poverty occurred despite that employment rates fell by 14 percent in April—the largest one-month decline on record.
This research reveals that government programs, including the regular unemployment insurance program, the expanded UI programs, and the Economic Impact Payments (EIPs), can account for more than the entire decline in poverty that the authors find, and more than half of the decline can be explained by the EIPs alone. These programs also helped boost incomes for those further up the income distribution, but to a lesser extent.
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As more countries, states, and municipalities begin to reopen their businesses and public spaces in response to the ongoing COVID-19 pandemic, one constant refrain is the warning that we will just get back to square one, with the pandemic running its course and the death toll rising once again, as everyone will get back to normal. But will they? How far might people go in practicing precaution on their own by adjusting their social and economic behavior, without government stay-at-home orders, and how will that affect the economy and the dynamics of the pandemic?
To address this question, the authors developed a simple model based on other recent research, which includes agents (people) who are aware of infection and death risks if they continue to leave their homes to work and to shop, among other activities. Faced with these risks to their own health, they will adjust their behavior. This is a key element of economic models, and is a feature that is not part of standard epidemiological models.
Crucially and in departure from other economic models, the authors assume that the economy is composed of sectors that differ in their infection probabilities. This heterogeneity is simply illustrated, for example, by people’s choice to eat a pizza delivered to their home vs. in a restaurant, or to work at home rather than in an office (if they are among those able to work from home). This heterogeneity matters. The way people choose to “consume” public experiences—whether work, worship, or entertainment—has a profound impact on infection rates.
Broadly summarized, when the authors run their model without heterogeneity in infection risk across sectors, economic activity declines 10%. However, the introduction of heterogeneity mitigates much of that decline. Likewise, the majority of deaths are avoided after the first year, compared to the homogeneous sector version. Importantly, these results are realized without government intervention. One can think of these results as capturing some of the experiences with Sweden’s less-restrictive approach to COVID-19 management. Better, these results are indicative of the unfolding dynamics subsequent to re-opening: a modest rise in infection, a very persistent, but modest decline in economic activity, and a substantial and prolonged shift across sectors, which flexibility of labor markets needs to allow for. This is far from a return to normal, but it is a reasonably optimistic outlook nonetheless.
What explains these outcomes? The authors suggest that infections may decline due to the re-allocation of economic activity that people will make on their own, and the resulting and longer-lasting shift between sectors. For the rather benign outcome in the model and for successful sectoral shifts, it is key that workers can adjust rather quickly to the changing labor market. Food servers can become delivery drivers. Former shop clerks find employment in Amazon warehouses. Artists provide entertainment online. Jobs lost in some sectors get partly offset by recruitment in others.
The authors acknowledge that labor markets do not function as smoothly as they assume in their model. The authors stress that their results are not definitive in and of themselves; models are approximations of reality that depend greatly on the parameters applied by researchers. In this case, the authors concede that the results may appear Panglossian.
However, one need not wear rose-colored glasses to recognize that private incentives can shape behavior during a health pandemic. Most importantly, allowing the economy to succeed in shifting sectoral activities in response to these choices is key for mitigating both the economic as well as the health impact. Consideration of such incentives and sectoral shifts could be important as governments around the world consider strategies to reopen public activities.
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The spread of infectious disease has an important spatial component: When individuals from one neighborhood visit another one they can infect others or get infected. Closure of businesses and public places in a neighborhood could reduce such infection opportunities as well as the import/export of the disease from/to other neighborhoods. How should a city target closures to achieve an appropriate policy goal at the lowest possible economic cost, factoring in neighborhood spillovers and the differences among neighborhoods’ economic values?
To answer this question, the authors focus on the policy goal of reducing infections in all neighborhoods, and provide an optimization framework that delivers the optimal targeted closure policies. They then use mobile-phone data (from a period prior to lockdowns) to estimate individuals’ movements within NYC and, applying their framework, the authors reveal the following:
- Targeted closures could achieve the aforementioned policy goal at up to 85% lower economic cost than the uniform city-wide closures.
- Second, coordination among counties and states is extremely important. It may be infeasible for NYC to achieve the policy goals and curb the spread of the epidemic unless the neighboring counties (e.g., those in New Jersey) also impose appropriate economic closure measures.
- Third, the optimal policy promotes some level of economic activity in Midtown, while imposing closures in many neighborhoods of the city.
- Finally, contrary to likely intuition, the neighborhoods with larger levels of infections are not necessarily the ones targeted for the most stringent economic closure measures.
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Throughout the United States, large swathes of economic activity and social life have been paused due to the pandemic. Data based on smartphone movements reveal this abrupt shift and can be used to study—almost in real-time—how people are altering their behavior during the coronavirus pandemic. A team of economists from five different universities that includes Chicago Booth’s Jonathan Dingel has published indices derived from anonymized phone data to allow researchers to use this information.
One of the team’s indices describes a device’s exposure to other devices due to visiting the same commercial venue. This daily device exposure index (DEX) reports the average number of distinct devices that also visited any of the commercial venues visited by a device on that day. Nationwide, the DEX declined dramatically over the month of March. By late March, device exposure was about one-third the level typically observed in February.
Thanks to the smartphone data’s rich detail, device exposure can be measured on a daily basis for more than 2,000 US counties. While exposure is down by two-thirds on average, there is considerable variation in the degree of isolation across US cities. On April 3, the device exposure indices in New York City and Las Vegas were merely one-tenth their Valentine’s Day levels. By contrast, the DEX for Cheyenne, Wyo., declined by only 40%. Across metropolitan areas, the decline in device exposure was greater in cities where a larger share of jobs can be done at home.
While the correlation between reduced device exposure and a greater share of jobs that can be done at home does not establish a causal relationship, this finding illustrates just one of numerous questions that can be investigated using these exposure indices made available to the global research community by the team of economists. The data are available online at https://github.com/COVIDExposureIndices/.
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The novel coronavirus outbreak was declared a national emergency in the US beginning March 1, 2020, with states imposing various levels of lockdown measures. By April 13, there were nearly 550,000 confirmed cases in the US, with deaths approaching 22,000. While this is clearly a major health crisis, the country is also facing a deep and possibly long-lasting economic recession. One crucial question looming over both the health and economic effects is how many people have actually contracted COVID-19 and the actual mortality rate; that is, while the number of confirmed cases is known, there are likely a large number of cases that have not been confirmed and, likewise, some deaths that have not been attributed to COVID-19.
To address this crucial knowledge gap, the authors have developed a unique strategy to estimate the likely real impact of the COVID-19 pandemic on the US. This strategy is based on the variation in travel from the epicenter of an outbreak to other locations that were not previously infected. Through a series of estimates based on known infection rates and expected rates of transmission, and incorporating the likely effect of travel from an epicenter of an outbreak to other areas, the authors estimate the percentage of unreported cases. The results are striking: for example, on March 13, across major metro areas, the authors estimate that on average only 4.16% of total infections were reported across the US with an eight-day reporting lag, meaning that for every case there were 23 unreported cases. The range of results across model assumptions and time periods utilized vary between 6 to 24 unreported cases.
Finally, while the authors stress that their results are dependent on strong assumptions and reliable data, they believe their methodological strategy is a solid start that can fuel additional research.
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Shelter-in-place policies reduce social contact and risks of interpersonal COVID-19 transmission. Though the economic consequences of these policies are substantial, local non-compliance creates public health risks and may cause regional spread. Understanding the drivers of what enhance or mitigate compliance is a first order public policy concern.
Clarifying these mechanisms provides actionable insights for policy makers and public health officials responding to the COVID-19 pandemic.
In our paper, we find a significant decline in population movement after the local shelter-in-place policies were enacted. Second, an increase in local income enhances compliance. Third, tariff-induced economic dislocation and higher Trump vote shares in 2016 reduce compliance. Finally, exposure to slanted media reduces compliance, consistent with the impact of information sources that downplayed the danger of COVID-19.
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Since the purpose of social distancing is to reduce the spread of a virus, in this case COVID-19, it matters greatly whether people believe in the need to take such precautions. If people infer lower risk from the same set of facts (e.g., population density, case counts and deaths), they may impose unnecessary health risks on others. Given the political divide in the US and how individuals consume news and information, the authors of this new research examine whether political partisanship affects the risk perceptions of individuals during the ongoing COVID-19 pandemic of 2020.
The authors use a number of measures to explore the effects of political partisanship on pandemic risk perceptions and, among other revealing insights (regarding, for example, pandemic-related internet searches), they find that while a higher incidence of confirmed COVID-19 cases results in a reduction in daily distance traveled, this effect is muted in counties that favored Donald Trump in the 2016 presidential election. For example, with a doubling of the number of confirmed COVID-19 cases in a county, the percent change in average daily change in distance traveled falls by 4.75 percentage points. However, for this same doubling in cases in a county, a one standard deviation increase in Trump voter share mutes this effect by 0.5 percentage points. Similar patterns are revealed when the authors examine the change in daily visits to non-essential businesses—residents in counties that favored Trump took more non-essential trips.
Epidemiological models predict that COVID-19 will generate extraordinary demand for medical care, raising questions about whether the US healthcare system has sufficient capital (ventilators and ICU beds) and labor (doctors, nurses and other healthcare workers) to provide needed care. To gauge the surge capacity of the US healthcare workforce, the authors calculate how much additional care could be provided if clinicians increased their workloads to 60 hours per week. They use data from the 2015-2017 American Community Survey, which surveys 1% of the US population each year, and records workers’ occupation and weekly hours.
The table below shows national-level statistics, with a focus on three occupations: physicians, registered nurses, and respiratory therapists, who provide intubation and ventilation management for COVID-19 patients with breathing difficulties. The US has 237 physicians per 100,000 people, who work the equivalent of 4.3 12-hour shifts per week, and thus provide 1,022 clinician-shifts per 100,000 people per week. If physicians increased their capacity to 60 hours, or five 12-hour shifts, per week, they could provide an additional 163 clinician-shifts, or 16% more care. Registered nurses provide a baseline of 2,111 clinician-shifts per 100,000 people per week. Because they work fewer hours at baseline, they could increase their capacity by an additional 1,276 clinician-shifts per 100,000 people or 60% by working five shifts per week. Respiratory therapists’ surge capacity is proportionally similar.
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Surge capacity varies substantially by region. Physician surge capacity, measured in clinician-shifts per 100,000 people per week, is nearly twice as large in the Northeast as the Midwest or Deep South. Surge capacity for registered nurses is highest in the Midwest, and lowest in the Southwest. Respiratory therapist surge capacity is highest in the Great Plains and the South. The Southwest has relative low surge capacity for all three occupations.
Some clinicians have the training to care for COVID-19 patients. Others could be cross-trained to provide this care. Even clinicians who are not appropriate for cross-training can fill in for coworkers who have been shifted to COVID-19 care, as could retired workers who have training and experience but have higher COVID-19 mortality risk. As some states have already started doing, easing licensing restrictions can give hospitals the flexibility to better cope with this unprecedented spike in demand.
 Ferguson, Neil M., et al. March 16, 2020. “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.” London: Imperial College COVID19 Response Team.
 The authors choose 60 hours because this is the average amount that physicians report working per week during the ages when they are in training. This training is notorious for requiring long hours, but these hours are apparently manageable for a period of months or a few years.
 The authors restrict their analysis to those working in hospitals and physicians’ offices, as these industries are most relevant for COVID-19 care.
 Data on additional occupations are shown in the Appendix.
 E.g., https://malegislature.gov/Bills/191/S2615 and http://www.op.nysed.gov/COVID-19Volunteers.html
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As the United States and the rest of the world grapple with COVID-19, the most reliable policy response seems to be social distancing, which itself imposes substantial costs on economies and people’s well-being. Indeed, people have begun to question whether the costs of social distancing exceed its benefits and are therefore too great. Here, we estimate the economic benefits of social distancing due to reducing mortality rates.
Panel A is derived from Ferguson et al. (2020) and reports the projected daily deaths in the United States due to COVID-19, including the impacts of overcrowding. It is apparent that the moderate social distancing scenario (roughly consistent with current US policy) is projected to greatly reduce the number of deaths, relative to the “no policy” scenario. The period where the daily number of deaths under the distancing scenario exceeds the “no policy” number of deaths is because of the lower rates of immunity in the population due to distancing. After September 1, the number of daily deaths in the two scenarios is equal. In total, these projections indicate that the moderate social distancing scenario will save 1.1 million lives by avoiding new infections and an additional 600,000 lives by avoiding overcrowding of hospital intensive care units.
Panel B shows the monetized benefits of saving these lives, which total $7.9 trillion, or roughly $60,000 per US household. About 90% of the monetized benefits are projected to accrue to people age 50 or older. Importantly, the benefits we compute are in the trillions of dollars because they capture the total value Americans place on remaining alive: not just the income they earn, but also the value they place on leisure, spending time with friends and family, and all other activities. Even so, the $7.9 trillion is likely an underestimate, because it does not account for social distancing’s impact on reducing uncertainty about mortality impacts, the potential for reducing morbidity rates, and improving quality of medical care for non-COVID-19 medical problems
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We extend the baseline SEIR infectious disease epidemiology model to understand the potential role of broad based testing in guiding government quarantine policies. Reducing quarantine measures by themselves would increase mortality. However, our framework demonstrates that randomly testing asymptomatic individuals and then targeting quarantine measures to those who test positive to COVID-19 can deliver the same mortality as severe quarantine measures, with a less devastating impact on the economy.
- Global Economy
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The COVID-19 pandemic initially led governments to shut down a few sectors, for example the service, hospitality, and travel industry. Huber’s 2018 study highlights that such disruptions can harm the entire economy, even if they initially only affect a few companies. To make this point, Huber shows that Commerzbank, one of Germany’s largest banks, cut lending to its German borrowers during the 2008-09 financial crisis. The lending disruption reduced the growth of companies that relied directly on loans from Commerzbank.
Importantly, the disruption also affected companies and employees that had no direct relationship with Commerzbank. Indirectly affected companies experienced spillover effects due to both a general decline in demand and a temporary lack of innovation at directly affected companies. When Commerzbank’s customers made job cuts, overall household consumption fell, which then affected revenue and employment at other companies. Further, declining research-and-development activities at directly affected companies spilled over to other companies, thus slowing overall productivity growth. The employment of indirectly affected companies remained low even beyond the duration of the initial lending disruption.
These findings may apply to the current economic shock due to the COVID-19 pandemic. For example, if directly disrupted companies fire workers, those workers will spend less, which will spill over to negatively affect other firms. Moreover, the economic harm of the current crisis may last longer than the actual disruption due to COVID-19.
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The Chinese government ended the 76-day lockdown of Wuhan on April 8, 2020. Outside Wuhan, many local governments had already eased restrictions on movement and shifted their focus to reviving the economy. In this work, the authors document the post-lockdown economic recovery in China. The main findings are summarized as follows:
- Official statistics suggest a quick recovery in manufacturing, which is corroborated in non-official data on city-to-city truck flows (see Figure 1) and air pollution emissions (see Figure 2).
- Electricity consumption, retail sales and catering income suggest a much more persistent output decline in services. Business registration data also show less firm entry in services.
- There is huge cross-region heterogeneity, with the southeast region experiencing the strongest initial recovery, according to the authors’ data.
- Small businesses were hit hard, with February sales down 35% from 2019, and they grew slowly in March. April will be the key month to determine the recovery speed.
Building on previous work to determine how many US jobs can be performed at home, the authors produce new estimates for 86 other countries. Their analysis reveals a clear positive relationship between income levels and the shares of jobs that can be done from home. For example, while fewer than 25 percent of jobs in Mexico and Turkey could be performed at home, this share exceeds 40 percent in Sweden and the United Kingdom. The striking pattern suggests that developing economies and emerging markets may face an even greater challenge in continuing to work during periods of stringent social distancing.
The authors conduct their analysis by merging their classification of whether each 6-digit SOC (Standard Occupation Classification) can be done at home based on the US O*NET surveys with the 2008 edition of the international standard classification of occupations (ISCO) at the 2-digit level.
The figure plots the author’s measure of the share of jobs that can be done at home in each country against its per capita income. They compute the jobs share using the most recent employment data available from the International Labour Organization (ILO) after restricting attention to countries that report employment data for 2015 or later. The income measure is GDP per capita (at current prices and translated into international dollars using PPP exchange rates) in 2019, obtained from the International Monetary Fund. They note that their classification assesses the ability to perform a particular occupation from home based on US data and that the nature of an occupation likely varies across economies with different income levels.
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With Americans largely self-isolating amid concerns about COVID-19, some of the hardest-hit areas are already seeing electricity demand begin to weaken. It is useful to review what has happened to power demand in Italy, which some say is about 11 days ahead of the US trajectory of the virus. Compiling regional grid data and adjusting for weather changes reveals that power demand has plunged in Italy since the middle of February.
On Friday, February 21, life was largely going about as normal in Italy. The following day, the Italian government began to institute quarantine measures. By Monday, power demand began to slow. Since a national lock-down on March 10, national power demand had fallen over 28% compared to demand just prior to the quarantine measures.
Power demand could be a real-time indicator of the more widespread impacts on the Italian economy. Also, what is happening in Italy could point to what the United States could expect in the coming weeks as states issue tighter restrictions on daily life. When there is a sharp shock to the economy, other indicators like employment may lag in reflecting the impact. This is because laying off workers is often seen as a last resort as companies start by taking other measures like ramping down production or adjusting maintenance schedules. Conversely, electricity demand shows the more immediate change and is a broad measure of economic activity. This was on display during the last recession in the United States. US power demand began to fall a month before the official start date of the recession according to the National Bureau of Economic Research—a date that was determined after an additional year of data had been collected. As policymakers today are considering which countermeasures may be in order to buffer the economic effects of coronavirus, a real-time indicator of the economy’s strength is of the utmost importance.
- US Financial Markets
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During financial crises like in 2008, US Treasuries are typically viewed as the most liquid and safe assets in the world, reflected by their rising prices when markets rush to these relatively secure assets. However, this did not occur in March 2020 during the COVID-19 pandemic. True to script, stock prices fell dramatically, the VIX index of implied stock return volatility spiked, credit spreads widened, and the dollar appreciated. In sharp contrast to previous crisis episodes, though, prices of long-term Treasury securities fell sharply.
What happened? The authors review empirical evidence of investor flows and build a model to shed light on the mechanism behind this episode. Their model introduces repo financing as a key part of dealers’ intermediation activities, through which levered investors obtain funding from dealers who are subject to a balance sheet constraint–the Supplementary Leverage Ratio (SLR)–due to regulation reforms since the 2007–09 crisis. Consistent with their model, the spread between the Treasury yield and overnight-index swap rate (OIS) and the spread between dealers’ reverse repo and repo rates are both highly positive in the COVID-19 crisis, and both greatly negative in the 2007–09 financial crisis.
The observed movements in Treasury yields in March 2020 can be rationalized as a consequence of selling pressure that originated from large holders of US Treasuries interacting with intermediation frictions, including regulatory constraints such as the SLR. Evidently, the current institutional environment in the Treasury market is such that it cannot absorb large selling pressure without substantial price dislocations, or intervention by the Federal Reserve as the market maker of last resort. The safe asset status of US Treasuries’ should not be taken for granted.
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The authors use data from the aggregate equity market and dividend futures to quantify how investors’ expectations about economic growth across horizons evolve in response to the coronavirus outbreak and subsequent policy responses. Dividend futures, which are claims to dividends on the aggregate stock market in a particular year, can be used to directly compute a lower bound on growth expectations across maturities or to estimate expected growth using a simple forecasting model. As of June 8, the authors’ forecast of annual growth in dividends is down 9% in the US and 14% in the EU, and their forecast of GDP growth is down by 2.0% in the US and 3.1% in the EU. As a word of caution, the authors emphasize that these estimates are based on a forecasting model estimated using historical data. In turbulent and unprecedented times, there is a risk that the historical relation between growth and asset prices breaks down, meaning these estimates come with uncertainty.
The lower bound on the change in expected dividends is -18% in the US and -25% in the EU on the 2-year horizon. The lower bound is model-free and completely forward looking. There are signs of catch-up growth from year 4 to year 10. News about economic relief programs on March 26 boosts the stock market and long-term growth but did little to increase short-term growth expectations. Expected dividend growth has improved since April 1 in both the US and the EU.
As of June 8, the expected return on the market has returned to the pre-crisis level. On June 8, the S&P 500 trades at $3232, which is $64 lower than the average price between January 1 and February 19. This drop can largely be explained by the first 7 years of dividends, as they are down by a total of $72. As such, the distant-future dividends, the dividends beyond year 7, must have approximately the same value as before the crisis. If expected long-run dividends are the same as before the crisis, expected returns on the long- run dividends must therefore also be the same as before the crisis. However, interest rates have dropped substantially, which means the expected return in excess of the interest rates is higher than before the crisis.
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Financial markets have fluctuated significantly as the COVID-19 epidemic has progressed.These fluctuations likely reflect both the anticipation of a steep drop in corporate earnings, as well as a reassessment of the risk of business investment. It is important to separate these two factors because upward revisions in risk perceptions can themselves reduce investment, deepening and prolonging the recession.
To understand movements in risk perceptions relevant for the macroeconomy in near real-time, the authors employ the “price of volatile stocks” (PVSt)1, which is the book-to-market ratio of low-volatility stocks minus the book-to-market ratio of high-volatility stocks. In previous work, the authors showed that PVSt is low when perceived risk directly measured from surveys and option prices is high. Further, using time-series data from 1970 to 2016, the authors showed that when perceived risk is high according to PVSt, future real investment tends to be lower because the cost of capital is higher for risky firms.
Figure 1 shows a daily time series of the authors’ measure of perceived risk, PVSt, from 1970 and through April 2020. It shows the price of volatile stocks fell sharply – and hence perceived risk rose sharply – as news about COVID-19 was hitting US markets and households in March 2020. PVSt reached its low for the year on April 3, 2020, when it was down 2.6 standard deviations from its level at the start of 2020. While this decline is large, it is comparable to movements in risk perceptions in prior recessions, particularly the downturn following the dotcom bubble in the early 2000s. It is also much smaller than the move in risk perceptions during the financial crisis of 2008-2009. Estimates for the period 1970-2016 indicate that a move in risk perceptions of the size experienced from the beginning of the year until this trough has typically been associated with a drop in the natural real risk-free rate of 3.3 percentage points, and a decline in the ratio of economy-wide capital expenditures to total assets of ratios of 0.91 percentage points (relative to a pre-2016 standard deviation of 1.16%).
Figure 2 provides a close-up view of PVSt and the aggregate stock market during the COVID-19 pandemic (February 14, 2020 through April 30, 2020). The figure shows that PVSt is useful for interpreting individual events during the COVID-19 crisis and often contains information that is distinct from the aggregate stock market. One thing that stands out from this figure is that the steep drop in the aggregate stock market at the end of February left PVSt almost completely untouched, implying that perceptions of risk had not changed significantly. In other words, the evolution of PVSt at the onset of the crisis suggests that investors initially believed there would be a short-term decline in earnings, but did not believe there would be an amplification effect from heightened risk perceptions to the aggregate economy. However, PVSt and the aggregate market began to drop in tandem around March 11, the day the WHO declared COVID-19 a pandemic and wide-spread international travel restrictions were imposed. One possible interpretation for this decoupling and recoupling is that COVID-19 initially appeared to affect only the short-term cash flows of internationally connected firms, whereas the spread of the virus and the associated policy measures imposed in mid-March affected the risk outlook for a much broader swath of the economy. These trends were in turn reflected in the prices of volatile stocks.
Another striking feature of Figure 2 is the large increase in PVSt that began on April 21, 2020, the day that the United States Senate passed the Paycheck Protection Program and Health Care Enhancement Act. The bill provided nearly $500 billion in additional funding to support the CARES Act, much of which was geared towards aiding small and medium-sized businesses. PVSt increased nearly 0.66 standard deviations between the time that the bill was passed in the Senate and when it was signed into law by President Trump on April 24. Interestingly, the market-to-book ratio of the aggregate stock market increased only 0.17 standard deviations over the same time period. The differential response of PVSt and the aggregate stock market to the passing of the bill is consistent with the authors’ previous interpretation that PVSt reflects perceptions of risk that are relevant for privately owned firms, which tend to be smaller and riskier than the larger, less volatile publicly traded firms that dominate the aggregate stock market.
1 As developed in Pflueger, C., E. Siriwardane, and A. Sunderam (2020). “Financial market risk perceptions and the macroeconomy.” Quarterly Journal of Economics, forthcoming.
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The stock prices of life insurance companies declined sharply during the onset of the COVID-19 crisis. To illustrate this, the figure reports the drawdown, defined as the percent decline from the maximum to the minimum of the cumulative return index, from January 2 to April 2, 2020. The drawdown of a portfolio of variable annuity insurers is -51% during this period. This is a substantially larger drawdown than the S&P500 (-34%), the financial sector more broadly (-43%), and rivals the airline industry (-62%). Some of the most affected companies experienced a drawdown of -65% or more (e.g., AIG, Brighthouse, and Lincoln). While this apparent fragility may be concerning in general, the solvency of life insurance companies that safeguard a large share of long-term savings and insure health/mortality risks is particularly important during a pandemic.
It may be tempting to conclude that life insurers experienced large losses due to the high death toll of the coronavirus, but this is not necessarily the case, as annuities represent a large fraction of insurers’ liabilities and insurers and, in fact, profit from those contracts if the policyholders die unexpectedly early. Instead, the fragility is the result of various insurance products with that come with minimum return guarantees. The traditional role of life insurers is to insure idiosyncratic risk through products like life annuities, life insurance, and health insurance. With the secular decline of defined benefit pension plans and Social Security around the world, life insurers are increasingly taking on the role of insuring market risk through minimum return guarantees. In the US, life insurers sell retail financial products called variable annuities that package mutual funds with minimum return guarantees over long horizons. Variable annuities have become the largest category of life insurer liabilities, larger than traditional annuities and life insurance.
From the insurers’ perspective, minimum return guarantees are difficult to price and hedge because traded options have shorter maturity. Imperfect hedging leads to risk mismatch that stresses risk-based capital when the valuation of existing liabilities increases with a falling stock market, falling interest rates, or rising volatility.
The fragility is not new to the current crisis. During the 2008 financial crisis, many insurers including Aegon, Allianz, AXA, Delaware Life, John Hancock, and Voya suffered large increases in variable annuity liabilities ranging from 27% to 125% of total equity. Hartford was bailed out by the Troubled Asset Relief Program in June 2009 because of significant losses on their variable annuity business. Risk mismatch between general account assets and minimum return guarantees leads to negative duration and negative convexity for the overall balance sheet and poses a challenge for life insurers in the low interest rate environment after the financial crisis. As a consequence, the stock returns of US life insurers have significant negative exposure to long-term bond returns after the financial crisis.
The persistent low-rate environment in combination with declining interest rates, widening credit spreads, and increased volatility will be a challenge to the balance sheet of life insurers in the foreseeable future.
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As the novel coronavirus (COVID-19) spread around the world, equities plummeted and market volatility rocketed upwards. In the United States, recent volatility levels rival or surpass those last seen in October 1987 and December 2008 and during the Great Depression, raising two key questions: 1) what is the role of COVID-19 developments in driving market volatility, and 2) how does this episode compare with historical pandemics, including the devastating Spanish Flu of 1918-20?
Employing automated and human readings of newspaper articles dating to 1985, the authors find no other infectious disease outbreak that had more than a minimal effect on US stock market volatility. Reviewing newspapers back to 1900, the authors find no contemporary newspaper account that attributes a large daily market move to pandemic-related developments, including the devastating Spanish Flu pandemic, which killed an estimated 2% of the world’s population. In striking contrast, news related to COVID-19 developments is overwhelmingly the dominant driver of large daily US stock market moves since February 24, 2020.
While the severity of COVID-19 explains some of the market’s volatile response, the authors find this answer incomplete, especially since similar—or worse—fatality rates 100 years ago had comparatively modest effects on markets. The authors offer three additional explanations:
- Information about pandemics is richer and is relayed much more rapidly today.
- The modern economy is more interconnected, including the commonplace nature of long-distance travel, geographically expansive supply chains, and the ubiquity of just-in-time inventory systems that are highly vulnerable to supply disruptions
- And behavioral and policy reactions meant to contain spread of the novel coronavirus, including adoption of social distancing, are more widespread and extensive than past efforts, and have a more potent effect on the economy.
When the market is calm, the term structure of the Treasury yield curve tends to be upward sloping, as investors expect to be paid more when lending in the longer-term. But on March 9, when the first market-wide halt was triggered by the coronavirus outbreak, the term structure was greatly flattened as investors responded to stock market turmoil by turning to long-term government bonds. During the second and third market-wide halts on March 12 and March 16, as the liquidity crisis was looming, investors started scrambling for cash, i.e., the government debt with the shortest maturity. As a result, short-term Treasury Bills (T-Bills) that can be quickly converted to cash became highly favored by investors, raising their prices relative to long-term treasuries and bending the entire yield curve upward sloping again. This flight to T-Bills also explains the recent striking fall of stocks, commodities, and long-term bonds in the same time.
The situation worsened even more on March 18 when the stock market halted for the fourth time in this sequel, strengthening the upward yield curve. However, the upward slope in this dire situation is driven by the surging demand of US currency from market participants–ranging from companies, funds, or sovereigns–potentially to pay off their US dollar denominated debts and other contractual obligations. This dramatic increase in demand for US currency is reflected in Figure 2, which plots the soaring dollar index (DXY) against other major currencies. Note, USD/JPY rises too, even though Japan has been widely appraised for its success of containing the virus during this time. This is behind the Federal Reserve’s recent aggressive expansion of its dollar swap lines with several major central banks.
 We have taken the 3-month OIS spread out from the entire yield curve to eliminate any mechanical level shift caused by the (expected or realized) federal funds rate movement on that day. (Indeed, the federal funds rate was cut on March 15.) Also, the upward sloping is not due to rising expectation of inflation; during this period the breakeven inflation rate (a market-based measure of expected inflation, the spread between nominal bonds and inflation-linked bonds say TIPS) goes down slightly.
- Impact on US Firms
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The world entered into the COVID crisis in the midst of an unexplained 15-year-long productivity growth slowdown, and the current decline of the world economy raises critical questions about the further trajectory of productivity growth. The authors consider the channels through which the crisis might shift the growth rates of productivity and output, whether up or down.
The authors note that measured productivity is likely to fall in the short run as workers are kept on companies’ payrolls while output declines. However, their concern is a more complete measure of productivity, or one that goes beyond traditional inputs like capital and labor to include any residual growth in output (what economists call total factor productivity, or TFP). Broadly summarized here, the authors describe three components of economy-wide TFP and possible impacts of the pandemic:
- Within-firm productivity growth. Firms build trust among customers and knowledge capital among employees, and both are in danger as the pandemic persists and customer needs go unmet or employees are lost. In addition, higher taxes and/or inflation in the future, as well as trade restrictions, could hamper a company’s recovery.
- Between-firm reallocation (e.g., unproductive firms close and labor and capital shifts to other firms). Small firms are likely to suffer most going forward and are more likely to close permanently. If these smaller firms are more innovative on average, economy-wide productivity growth could slow. Other firms, often larger, will exist primarily through government programs, some of which would otherwise have closed. These “zombie” firms might prevent other, more productive, firms from entering the market.
- Productivity generation created by the pure shifts of activities across sectors. Some sectors, like hotel and travel, may experience persistent drops in activity, while others, like healthcare and IT, may grow over time. The resultant reallocation of resources will have consequences for aggregate productivity, to the extent these sectors differ in productivity and expected productivity growth, and these differences will also occur across countries.
The authors acknowledge that long-term and, possibly, irreversible economic damage may occur from the COVID pandemic, and they urge policymakers to look beyond policies that protect existing businesses, and to enact policies that encourage productivity growth. Globalization, labor mobility, and small firms may all still fall victim to the crisis if the world does not succeed in reopening borders, refraining from trade and currency wars, and focusing on policies to boost productivity. On the upside, the broad adoption of new technologies – such as IT skills during the epidemic – and strong reallocation pressures may provide an independent boost on productivity as we come out of the crisis.
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Small businesses account for nearly 50 percent of US workers, and this new survey of nearly 6,000 firms reveals the financial fragility of many of those businesses and signals a cautionary note for policymakers, as most respondents expect the crisis to extend beyond the spring and well into the summer.
The late-March 2020 survey focused on assessing small businesses’ current financial status, the extent of temporary closures and laid-off employees, duration expectations and the impact on decision-making, and whether businesses planned to apply for CARES Act funding and how such a decision could impact closures and lay-offs. Broadly, the survey revealed the following:
- Disruption to US small businesses is severe, with 43% of the respondents temporarily closed. Employee reductions stood at 40% across all respondents. Regionally, mid-Atlantic states, including New York City, reported closures of 54% and layoffs of 47%. Industry responses varied widely, with service sector firms reporting employment declines over 50 percent.
- Many US small businesses are standing on financially shaky ground, with the median firm with expenses over $10,000 per month retaining only enough cash to last for two weeks. For 75% of respondents, there was only enough cash to cover expenses for two months or less.
- US small businesses are widely uncertain about when the crisis will end, with half expecting the crisis to persist into mid-summer, meaning that many firms expect this economic challenge to persist well beyond their available cash levels.
For policymakers, the following results are particularly salient:
- More than 13% of respondents did not plan to seek CARES Act funding because of application hassle, distrust that loans will be forgiven, and eligibility complexity.
- If the crisis extends beyond four months, many firms—especially many in the service industries—do not expect to remain viable.
- Extrapolating the 72 percent of businesses that would apply for CARES Act funding, and assuming all businesses would request maximum loans (2.5 months of expenses), the total volume of loans from all US businesses would approach about $410 billion, beyond the $349 allocated in the CARES Act at the time of the survey.
In a new study, the authors use de-identified data from a non-profit Fintech to study how US household spending responded to the COVID-19 crisis. Households dramatically changed their spending as COVID-19 spread. As cases began to spread in late February, spending increased sharply, indicative of households stockpiling goods in anticipation of a higher level of home-production, an inability to visit retailers, or shortages. Total spending rose by approximately half between February 26 and March 11, when a national emergency was declared and as cases grew throughout the country. There is also an increase in credit card spending, which could indicate borrowing to stockpile goods. Between the imposition of a national emergency and many states and cities issuing shelter-in-place orders starting on March 17, there are elevated levels of grocery spending. These patterns continue through the month of March.
The authors use the rich dataset to characterize heterogeneity across spending categories, demographics, income groups and partisan affiliation. There are very sharp drops in restaurants, retail, air travel, and public transport in mid to late March. The decrease in spending was not consistent across all categories, e.g., grocery spending increased, as did food deliveries. Despite increases in some categories, total spending dropped by approximately 50%.
Men stockpile slightly less, and families with children stockpile more than other households. Younger households stockpile later than other households. There is little heterogeneity across income—although our sample is skewed toward lower income individuals. Cell phone records indicate differences in social distancing between political groups—individuals in states with more Trump voters were much more likely to move around in mid and late March. Republicans stockpiled more than Democrats, purchasing more on groceries in late February and early March. Republicans were spending more in retail shops and at restaurants in late March, which may reflect differences in beliefs about the epidemic’s threat, or differential risk exposure to the virus.
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Government officials around the world have ordered businesses shut and families to stay in their homes except for essential activities. This fact estimates the opportunity costs of lockdown relative to a normally functioning economy.
National income accountants have found that adding a nonwork day to the year reduces the year’s real GDP by about 0.1 percent. Adding a nonwork day to a quarter would therefore reduce the quarter’s unadjusted real GDP by about 0.4 percent. Extrapolating from this finding, removing all of the working days from a quarter is 62 or 63 times this, or 25 percent. In other words, if seasonally-adjusted GDP for 2020-Q2 would have been $5.5 trillion at a quarterly rate (see Table), then changing all of that quarter’s working days to the functional equivalent of a weekend or holiday would reduce the quarter’s GDP to $4.2 trillion. Applying the same approach to 2020-Q1, with a lockdown occurring for one-eighth of the quarter, 2020-Q1 real GDP (in 2020-Q2 prices) would be $5.4 trillion. The quarter-over-quarter growth rate of seasonally-adjusted real GDP would, expressed at annual rates, therefore be -10 percent in Q1 and -63 percent in Q2.
Bottom line: Given these and other facts, while even negative 50 percent is an optimistic projection for the annualized growth rate of US GDP in 2020-Q2, (assuming nonessential businesses stay closed over that time), this large figure may understate the true effect, which could total nearly $10,000 per household per quarter.
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While the effect of the COVID-19 virus on financial markets has been apparent for weeks—US equities fell 30% from February 21 to March 20—we are still months away from realizing the full economic effect. However, the recent Survey of Business Uncertainty (SBU) portends a sharp drop in business activity in 2020. Moreover, business pessimism grew from March 9 to March 20, while the survey was in the field.
When asked directly about the impact of coronavirus developments in mid March, firms see a 6.5 percent negative hit to their sales revenues in 2020. Comparing what firms say about their overall sales outlook in March to what they said in February yields a very similar drop in expected sales revenue. Further, firms’ uncertainty about their own sales growth over the next year rose 44 percent from February to March.
In partnership with Steven Davis of the University of Chicago Booth School of Business and Nicholas Bloom of Stanford University, the Federal Reserve Bank of Atlanta has created the Atlanta Fed/Chicago Booth/Stanford Survey of Business Uncertainty (SBU). This innovative panel survey measures the one-year-ahead expectations and uncertainties that firms have about their own employment, capital investment, and sales. The sample covers all regions of the U.S. economy, every industry sector except agriculture and government, and a broad range of firm sizes.
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The graph displays an estimate of overhead costs ($1.16 trillion total) for all non-financial S-corporations based on aggregate data from tax returns. Overhead costs are meant to include required expenses for firms, like interest, rents, utilities, maintenance, and so on. They do not include payments to workers, nor profits for shareholders, nor new capital expenditures.
Three points deserve note. First, overhead costs are important for private firms (approximately 14% of total revenues or 38% of gross profits). Second, we can estimate such costs relatively easily using information from past tax returns, which points toward feasible policy solutions designed to help firms cover these costs quickly during the coronavirus crisis. Third, aggregate overhead costs are especially important in retail and wholesale trade. These industries have many small private firms likely to be hardest hit by the crisis.
Source data are aggregates from the SOI corporate sample for the tax year 2014, aged to 2018 using the growth of nominal GDP. The year 2018 is the latest year for which tax returns would be readily available to the IRS to implement a policy.
 S-corporations likely account for between 1/4 and 1/3 of all overhead among non-financial private business, which includes partnerships, sole proprietorships, and private C-corporations.
- US Labor Market
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The authors use anonymized bank account information on millions of JPMorgan Chase customers to measure how spending and savings over the initial months of the pandemic vary with household-specific demographic characteristics, like pre-pandemic income and industry of employment. The authors find that most households cut spending dramatically in early March, with declines particularly concentrated in sectors sensitive to government shutdowns and increased health risk, like travel, restaurants, and entertainment. Richer households, who typically spend more in these categories, cut their spending slightly more than poorer households.
Starting in mid-April, after government stimulus checks and expanded unemployment benefits are put in place, spending by poor households recovers more rapidly than spending by rich households. At the same time, poor households also have the largest growth in liquid checking account balances. Thus, poorer households simultaneously have faster growth of spending and savings starting in mid-April, even though they face greater exposure to labor market disruptions and unemployment. This suggests an important role for government transfers in stabilizing income and spending during the initial stages of the pandemic, especially for low-income households. This in turn suggests that phasing out broad stimulus too quickly could potentially transform a supply-side recession driven by direct effects of the pandemic into a broader and more persistent recession caused by declines in income and aggregate demand.
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Nearly 28 million persons in the US filed new claims for unemployment benefits over the six-week period ending April 25. Further, the US economy shrank at an annualized rate of 4.8% in the first quarter of 2020, and many analysts project it will shrink at a rate of 25% or more in the second quarter. Yet, even as much of the economy is shuttered, some firms are expanding in response to pandemic-induced demand shifts.
By pairing anecdotal evidence from news reports and other sources, along with the rich dataset provided by the Survey of Business Uncertainty (SBU), the authors construct novel, forward-looking measures of expected job reallocation across US firms. The authors draw on two special questions fielded in the April 2020 SBU, one asks (as of mid-April) about the coronavirus impact on own-company staffing since March 1, 2020, and another asks about the anticipated impact over the ensuing four weeks. Responses reveal that pandemic-related developments caused near-term layoffs equal to 12.8 percent of March 1 employment and new hires equal to 3.8 percent. In other words, the COVID-19 shock caused 3 new hires in the near term for every 10 layoffs.
Firm-level sales forecasts show a similar pattern, further supporting the authors’ view that COVID-19 is a major reallocation shock. In addition, the authors’ measure of the expected excess job reallocation rate rose from 1.5% of employment in January 2020 to 5.4% in April. The April value is 2.4 times the pre-COVID average and is, by far, the highest value in the short history of the series.
The authors also draw on special questions put to firms in the May 2020 SBU to quantify the anticipated shift to working from home after the coronavirus pandemic ends, relative to the situation that prevailed before the pandemic. They find that full work days performed at home will triple in the post-pandemic economy. This tripling will involve shifting one-tenth of all full work days from business premises to residences (and one-fifth for office workers). Since the scope for working from home rises with worker earnings, the shift in worker spending power from business districts to locations nearer residences is even greater.
Finally, the authors find that much of the near-term re-allocative impact of the pandemic will persist, as indicated by their forward-looking reallocation measures and their evidence on the shift to working from home. Drawing on special questions in the April SBU and historical evidence of how layoffs relate to realized recalls, they project that 32% to 42% of COVID-induced layoffs will be permanent. The authors also construct projections for the permanent-layoff share of recent job losses from other sources, obtaining similar results.
 The SBU is a monthly panel survey developed and fielded by the Federal Reserve Bank of Atlanta in cooperation with Chicago Booth and Stanford.
Using data from ADP one of the world’s largest human resources management companies, to measure changes in the US labor market during the early stages of this “Pandemic Recession,” the authors find that paid US employment declined by about 22% between mid-February and mid-April, 2020. This translates to a reduction in US employment of about 29 million workers as measured in the payroll data. In no prior recession since the Great Depression has US employment declined by a cumulative 2% during the first three-months of the recession (Chart 1). Across all prior recessions since the 1940s, peak employment declines were never more than 6.5%. The US economy has already experienced a 22% decline in employment during the first month of this recession (Chart 2).
Among other important findings, the authors reveal that employment declines were disproportionately concentrated among lower-wage workers: 35% of all workers in the bottom quintile of the wage distribution lost their job, at least temporarily, during the first month of the recession. The comparable number for workers in the top quintile was only 9% (Chart 3). This implies that over 36% of the 29 million jobs lost during the first four weeks of this recession were concentrated among workers in the lowest wage quintile. Job declines were larger in-service industries (such as leisure and hospitality) and in smaller firms, which disproportionately employ lower-wage workers (Chart 4).
The recession is having a disproportionate effect on small firms and lower-skilled workers: precisely those without the cash flow and savings to smooth consumption. The longer the recession persists, the greater the likelihood that lower wage workers may suffer the disproportionate brunt of the recession.
 ADP processes payroll for about 26 million US workers each month, representing the US workforce along many labor market dimensions. These sample sizes are orders of magnitude larger than most household surveys that measure individual labor market outcomes at monthly frequencies.
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Social distancing policies have led to many workers losing their jobs, at least temporarily, and the burden of job loss has mostly fallen on economically vulnerable workers. New research reveals that employment losses are around four times larger for workers without a college degree, one and half times larger for non-white workers, and five times larger for workers in the bottom half of the income distribution (see figure). This is related to the characteristics of the jobs of these types of workers. Poor and economically disadvantaged workers are more likely to be employed in jobs that are less likely to be conducted from home. These jobs also tend to rank highly in terms of the amount of close physical interaction that occurs at work (e.g., a nail salon worker). Combined, these results imply that workers that have been hurt most by the crisis economically, are also at the highest health risk as they go back to work.