Key Economic Findings About COVID-19

 The COVID-19 pandemic has led to a surge in demand for medical care, and healthcare systems across the United States have faced the risk of being overwhelmed. This creates an opportunity to study the labor markets that hospitals use to manage temporary staffing shortages. How effective are short-term labor markets at re-allocating workers to where they’re needed most? 

Using data from a healthcare staffing firm, the authors study flexibility of nurse supply across the United States. At different points throughout the spring and summer, hospitals in affected regions needed more nurses to deal with pandemic-related surges. The authors find that job postings for temporary nurse positions tripled from their usual rate at the height of the pandemic’s first wave, and increased even faster in places facing extreme pandemic conditions. In New York state, job postings increased eightfold, while the compensation almost doubled. 

The differences across states and across nursing specialties allow the authors to study workers’ flexibility in this market. For example, there was little-to-no increase in wages for nurses working in labor and delivery units, as the first wave of the pandemic did not change the number of women who were already pregnant.In contrast, demand skyrocketed for for nurses in intensive care units (ICU) and emergency rooms (ER). For these specialties, the number of job openings and compensation rates are positively associated with state-level COVID-19 case counts. In other words, more acutely ill COVID-19 patients implies increased need for traveling nurses, and higher payments required to recruit them. Based on one estimate, ICU jobs increased by 239 percent during the first wave of the pandemic, while compensation increased 50 percent. ER jobs increased by 89 percent while compensation increased by 27 percent. 

The large size of the United States, and nurses’ ability to work in different states, appears to be an important part of how this market adapted to the first waves of demand for COVID-19 nursing. An analysis by the authors demonstrates that the increases in quantity may understate the willingness of ICU and ER nurses to travel, given relatively higher compensation. In economic terms, they find nursing supply to be highly elastic, which suggests that price signals are an effective way of reallocating nurses to the parts of the country with increased staffing needs. Likewise, they find that workers who accept such postings travel longer distances from their homes to job locations when pay is higher. 

This work suggests that a national staffing market may offer timely flexibility to accommodate demand shocks. When demand increases in specific geographic areas, nurses’ ability to move can help mitigate a local shortage. That said, adjusting to a simultaneous national demand shock is harder. If numerous different regions experience simultaneous COVID-19 surges, meeting demand may require more than mobility across regions. Even though some nurses can travel, there is still a limited national supply of those with skills in demand. 

Stock markets cratered after mid-February 2020 in countries around the world, as the coronavirus pandemic spread beyond China. In what many see as a puzzle, the global stock market recovered more than half its losses from March 23 to late May. US stock market behavior, in particular, has prompted much head scratching: Despite a failure to control the pandemic, the US stock market recovered 73% of its lost value by the end of May and 95% by July 22.

The authors show that stock prices and workplace mobility (a proxy for economic activity) trace out striking clockwise paths in daily data from mid-February to late May 2020. Global stock prices fell 30% from February 17 to March 12, before mobility declined. Over the next 11 days, stocks fell another 10 percentage points as mobility dropped 40%. From March 23 to April 9, stocks recovered half their losses and mobility fell further. From April 9 to late May, both stocks and mobility rose modestly. The same dynamic played out across the vast majority of the 31 countries in the authors’ sample.

A second finding reveals that stock prices were lower when countries imposed more stringent market lockdown measures: national stock prices are 3 percentage points lower when the own-country lockdown stringency index is one standard deviation higher, and 4.7 points lower when the global average stringency index is one standard deviation higher. These are separate effects, and both are highly statistically significant.

The authors also closely analyzed stock prices in the world’s two largest economies—China and the US. They find that the COVID-19 pandemic had much larger effects on stock prices and return volatilities in the US than in China. At least in part, the larger impact on American stock prices reflects China’s greater success in containing the pandemic. However, the authors stress that the US stock market shows a much greater sensitivity to pandemic-related developments long before it became evident that its early containment efforts would flounder.

The COVID-19 pandemic triggered a shift to working-from-home (WFH) that has already saved billions of hours of commuting time in the United States alone. The authors tap several sources, including original surveys of their own design, to quantify this time-saving effect and to develop evidence on how Americans are using the time savings.

Over the course of May, July, and August 2020, the authors surveyed 10,000 Americans aged 20-64 who earned at least $20,000 in 2019: 37.1% worked from home, 34.7% worked on business premises, and the rest were not working. These figures imply that WFH accounts for 52.3% of employment in the pandemic economy, which is similar to other estimates. By way of comparison, American Time Use Survey data imply a 5.2% WFH rate among employed persons before the pandemic.

To calculate aggregate time savings from increased WFH, the authors gathered data from two national surveys to determine the number of commuting workers and average commuting times. They find that commuting time dropped by 62.4 million hours per day. Cumulating these daily savings from mid-March to mid-September, the authors find that aggregate time savings is more than 9 billion hours.

The accompanying figure illustrates that people spent over one-third of their extra time on their primary job, and nearly one-third on childcare, outdoor leisure and a second job, combined.

As the travel industry experiences a pandemic-induced slump, many are wondering about the future of air travel and how long it will take until people are comfortable enough to fly for work or leisure.

According to the recent Survey of Business Uncertainty^[1], conducted July 13-24, the authors find that firms anticipate slashing their post-pandemic travel budgets and tripling the share of external meetings (those with external clients, patients, suppliers, and customers) conducted virtually.

The authors’ findings cast doubt on the prospect for a quick and complete rebound in business travel. Firms anticipate slashing their pre-pandemic travel expenditures by nearly 30 percent when concerns over the virus subside (see Figure 1). The expected decline in travel expenditures is particularly severe for information, finance, insurance, and professional and business services, which are marking in a nearly 40 percent reduction in travel spending after the pandemic ends.

Such a large, broad-based reduction in travel spending not only suggests a sluggish and potentially drawn-out recovery for the travel, accommodation, and transportation industries, but it also indicates that firms expect to shift from face-to-face meetings to lower-cost virtual meetings. And, as Figure 2 shows, that’s exactly what the authors found when they asked firms about the share of virtual meetings that they held in 2019 versus the share that they anticipate holding in a post-COVID world.

^[1] The SBU is a monthly panel survey developed and fielded by the Federal Reserve Bank of Atlanta in cooperation with Chicago Booth and Stanford.

The authors provide evidence that COVID-19 shifted the direction of innovation toward new technologies that support video conferencing, telecommuting, remote interactivity, and working from home (collectively, WFH).

By parsing automated readings of the subject matter content of US patent applications, the authors find clear evidence that patents for WFH technologies are advancing at an accelerated rate. The accompanying figure reports the percentage of newly filed patent applications that support WFH technologies at a monthly frequency from January 2010 through May 2020. Interestingly, the WFH share of new patent applications rises from 0.53% in January 2020 to 0.77% in February, before the World Health Organization declared the novel coronavirus outbreak a global pandemic. China reported the first death from COVID-19 in early January and imposed a lockdown in Wuhan on January 23, 2020. By the end of January, the virus had spread to many other countries, including the United States. This figure suggests that these developments had already—by February—triggered the beginnings of a shift in new patent applications toward technologies that support WFH.

By March, COVID-19 cases and deaths had exploded in many localities and countries around the world. As the figure illustrates, the WFH percentage of new patent applications from March to May are nearly twice as large as the January value, providing clear evidence for the authors that COVID-19 has shifted the direction of innovation toward technologies that support WFH.

The authors use individual and household-level micro data to document that those workers who have particularly low earnings, low wealth and low buffers of liquid assets are the ones employed in social-intensive occupations where they must show up for work. On the other hand, workers in flexible occupations with low social exposure tend to have higher earnings, robust balance sheets, and enough liquid wealth to weather the storm.

This strong positive correlation between economic exposure to the pandemic and financial vulnerability suggests that the effects of the pandemic have been extremely unequal across the population. This means that there are a range of economic and health policy options, with appropriate patterns of redistribution, that can be used to contain the virus and mitigate its economic effects.

The accompanying charts illustrate this phenomenon, and include the following occupational distinctions:

• Essential: Jobs that are needed for the economy to function and cannot be performed remotely, like nurses, firefighters, or mail carriers.
• Low social intensive/high flexibility: Remote jobs where products do not require high social density, like writers, software developers, and accountants.
• Low social intensive/low flexibility: Jobs that mostly require on-site presence but still allow for social distancing, like carpenters, electricians, and plumbers.
• High social intensive/high flexibility: Jobs that are best performed when workers are in contact with customers or other workers, but which can also be done remotely, like teachers and therapists.
• High social intensive/low flexibility: Jobs where workers need to need to be in close contact with customers or other workers, on-site, like cooks, waiters, and many performance artists.

Chart 1 reports the average earnings and employment shares of each of the five occupations; average annual earnings are highest for those with high flexibility to work remotely and low social interaction ($79,000), and lowest for those with low flexibility and high social interaction ($32,000). Chart 2 reveals that workers in rigid and essential occupations are significantly more financially vulnerable than those in flexible occupations.

How has government stimulus affected economic welfare? The Coronavirus Aid, Relief, and Economic Security (CARES) Act is a $2.2 trillion economic stimulus bill enacted in the spring of 2020 to support American families, workers and businesses. The authors find that programs under the CARES Act succeeded in mitigating economic welfare losses by around 20% on average, while leaving the cumulative death count effectively unchanged.

The model focused on the four most important components of the CARES Act for household welfare:

• Economic Impact Payments (EIP);
• Expanded Unemployment Insurance (UI);
• The Paycheck Protection Program (PPP); and
• Waiving of tax penalties for retirement account withdrawals.

Figure 1 presents a range of policy options that can be quantitatively compared. The mean with fiscal support from the CARES Act shifts the Pandemic Possibility Frontier forward in the United States, allowing for the same number of fatalities with lower economic costs. In comparison, with the laissez-faire approach, fatalities are highest and the average economic costs of the pandemic are around two months of income because individuals react to rising infections by reducing both social consumption and supply of workplace hours.

The impact of the stimulus package on economic aggregates is substantial. Both the transfer programs (EIP, UI) and PPP boost aggregate consumption by around 6 percentage points, with about 4 points coming from PPP and the remainder from UI and EIP.

However, the stimulus package made the economic consequences of the pandemic more unequal. The stimulus package redistributed heavily toward low-income households, while middle-income households gained little from the stimulus package but will face a higher future tax burden.

In the model, labor incomes fall most for the lowest quartile of the pre-pandemic income distribution and remain persistently low. The drop in labor earnings for workers at the bottom of the income distribution was at least 10 percentage points deeper than those at the top of the income distribution.

Oddly, while labor incomes have fallen more for poor households than for rich ones, and have remained persistently low, consumption expenditures of the poor initially fell by the most but then recovered more quickly than those of the rich. Many households at the bottom of the income distribution with liquidity constraints actually experienced large increases in their total incomes. For many in the bottom distribution, UI benefits exceeded their incomes (with replacement rates over 100%), and recipients of stimulus checks living hand-to-mouth spent their benefits in the first weeks after receipt. As a result, households with lower earnings, greater income drops, and lower levels of liquidity displayed stronger spending responses.

A consequence of the CARES Act is a large increase in government debt. The model shows that after eighteen months, the debt-to-GDP ratio increases by about 12% above its pre-pandemic level, compared with an increase of 3% without the stimulus package.

The debate about how to manage the health and economic effects of the COVID-19 pandemic revolves around varying degrees of lockdown vs. no lockdown at all. However, in their recent paper that describes the distributional effects of existing policies, Greg Kaplan, Benjamin Moll, and Giovanni Violante offer a novel alternative. Instead of shutting down businesses or allowing partial openings to prevent people from gathering and spreading the disease, why not tax people’s behavior instead?

In economic parlance, taxes that are meant to drive behavior to achieve a certain goal are known as Pigouvian taxes, after the English economist A.C. Pigou (1877-1959). An example is a factory that emits lots of air pollution, called a negative externality, which creates problems downwind at little extra cost to the factory. One way to get the factory to scrub its emissions is to tax it relative to the social costs that it is imposing.

Such taxes are also enacted to modify the negative externalities of personal behavior, like drinking alcohol and smoking cigarettes. And it is with personal behavior that the authors apply the idea of Pigouvian taxes to the question of how best to limit the negative health and economic effects of COVID-19. Put directly: If you want to restrict the number of people that gather in a bar to have a drink, then you could tax that drink at such a level that you will attain adequate social distancing without closing the bar. Too many people want to attend a baseball game? Price the tickets to optimize attendance. The same holds true for work. Do people feel the need to attend their workplace even if their job does not require their presence on-site? Then make them pay a tax approximate to the cost that they are inflicting on society. Such a tax will keep most workers at home.

However, either one of these taxes is particularly bad for a subset of individuals – in the case of a tax on social consumption, those working in the social sector, and in the case of a tax on on-site work, those in rigid occupations who must show up for work. These costs can be partially mitigated by using the revenues from the tax to provide lump-sum subsidies to precisely those workers that are most adversely affected.

This is a simple description of the authors’ more detailed analysis, which employs their distributional pandemic possibility frontier (PPF) analysis, a technique that describes the heterogeneous effects of policies. In the accompanying figures, this dispersion of effects is shown by the colored bands that extend around the bold lines. Figure 1 (orange line) traces the PPF for a 30% tax on social consumption that is kept in place for different durations. Deaths due to COVID-19 are plotted on the horizontal (x) axis, and economic cost, as measured in multiples of monthly income, is on the vertical (y) axis. As we can see, the longer a policy is kept in place, the greater is the dispersion in welfare cost.

Alternatively, policymakers could impose a tax on hours worked in the workplace and then rebate the proceeds to workers in occupations that demand their appearance. This tax targets the labor supply margin as the source of the negative externality, as opposed to the social consumption margin. The green line in Figure 1 traces the PPF for a 30% tax on workplace hours with different durations. This policy generates a flatter PPF than a social consumption tax. With a tax on workplace hours in place for 2 months, the mean economic welfare loss is about 2 times monthly income, which is about the same as in the laissez faire scenario, but with a substantially smaller number of deaths, by around 0.1% of the population.

The authors do not claim that such alternative policies would be politically expedient to implement, and they detail limitations and challenges in their paper. However, they stress the lesson that targeted policies do exist that offer a more favorable average trade-off between lives and livelihoods than blunt lockdowns.

Sixty-eight percent of workers who lost their jobs due to the COVID pandemic received benefits that exceeded their previous wages,¹ raising the question of whether those workers would decline offers to retake their old jobs at the prior wage.

To investigate this important policy question, the authors devised a model that approximates the environment faced by unemployed workers, including the short duration of the extra benefits, the likelihood their offer to take back their old job stays valid, the likelihood they will find another job if they turn down their previous employer’s offer, and related issues. They check their model’s results against available data. Except in special cases, the authors find that unemployed workers would accept the offer to return to their old jobs at their old wage.

The authors first consider what workers would do if they made an incorrect, static, decision: Keep the higher benefits or return to work at a lower wage. In such a case, 68% of workers would choose the higher benefits under the CARES Act. However, when workers weigh up these dynamic issues—like whether the benefits would end, whether the job offer was limited in time, and whether other jobs are available—most workers would accept the job offer and return to work. Only a worker with a low previous wage and an almost certain return-to-work offer would turn down their old job and remain unemployed under the CARES Act.

According to this analysis, the CARES Act did not cause high unemployment in April to July 2020 by decreasing labor supply. While the precise cause is beyond the scope of this research, the authors do note the likelihood of low labor demand, and/or low labor supply due to health risks.

1 See Ganong, P., P. Noel and J.S. Vavra (2020): “US Unemployment Insurance Replacement Rates During the Pandemic,” BFI Working paper and BFI COVID-19 Fact.

Signed into law on March 27, 2020, the CARES Act was exceptional both in size (over $2 trillion in allocated funds) and in the speed at which it was legislated and implemented. A major component was a one-time transfer to all qualifying adults of up to $1200, with $500 per additional child. How effective were these transfers in stimulating the consumption of recipients?

Using a large-scale survey of US households, the authors document that only 15% of recipients of this transfer say that they spent (or planned to spend) most of their transfer payment, with the large majority of respondents saying instead that they either mostly saved it (33%) or used it to pay down debt (52%). When asked to provide a quantitative breakdown of how they used their checks, US households report having spent approximately 40% of their checks on average, with about 30% of the average check saved and the remaining 30% used to pay down debt. Little of the spending went to hard-hit industries selling large durable goods (cars, appliances, etc.). Instead, most of the spending went to food, beauty, and other non-durable consumer products that had already seen large spikes in spending because of hoarding.

These average responses mask significant differences across households. For example, lower-income households were significantly more likely to spend their stimulus checks, as were households facing liquidity constraints. Individuals out of the labor force were also more likely to spend their checks than either employed or unemployed individuals, consistent with motives of consumption smoothing and hand-to-mouth behavior.

Other groups that were more likely to report spending most of their checks were those living in larger households, men, Hispanics and those with lower education. In contrast, African-Americans were much more likely to report using their checks primarily to pay off debt, as were older individuals, those with mortgages, unemployed workers and those reporting to have lost earnings due to COVID. For those who did not wish to spend their stimulus payment and had to decide whether to pay off debt or save their checks, higher-income individuals were more likely to save than pay off debts, those with mortgages or renters were much more likely to pay off debts, as were financially constrained individuals.

Finally, and importantly, 90% of employed workers who received a stimulus check reported that the transfer had no effect on their work effort (as opposed to, e.g., searching harder for new work) while 80% of those employed workers who did not qualify for a check reported that receiving such a check would not affect their work effort; the same holds for people out of the labor force. For unemployed workers, approximately 20% of those receiving a payment said that this made them search harder for a job, while two-thirds report that it had no effect.

These results suggest that additional payments to households during the height of the pandemic—either in the form of stimulus checks or additional UI benefits—are unlikely to negatively affect the recovery because of disincentives to work.

Political polarization and competing narratives can undermine public policy implementation. Partisanship may play a particularly important role in shaping heterogeneous responses to collective risk during periods of crisis when political agents manipulate signals received by the public (i.e., alternative facts). We study these dynamics in the United States, focusing on how partisanship has influenced the use of face masks to stem the spread of COVID-19.

Using a wealth of micro-level data, machine learning approaches, and a novel quasi-experimental design, we document four facts: (1) mask use is robustly correlated with partisanship; (2) the impact of partisanship on mask use is not offset by local policy interventions; (3) partisanship is the single most important predictor of local mask use, not COVID severity or local policies; (4) Trump’s unexpected mask use at Walter Reed on July 11, 2020, significantly increased social media engagement with, and positive sentiment toward, mask-related topics. These results unmask how partisanship undermines effective public responses to collective risk and how messaging by political agents can increase public engagement with mask use.

About one in five US workers received unemployment insurance benefits in June 2020, which is five times greater than the highest UI recipiency rate previously recorded. Yet little is known about how unemployment benefits are affecting the economy today. To fill this gap, the authors study the consumption of benefit recipients during the pandemic using data from the JPMorgan Chase Institute.

In normal times, spending among unemployment benefit recipients falls by about seven percent when they become unemployed because typical benefits replace only a fraction of lost earnings. However, the CARES Act added a $600 weekly supplement to state unemployment benefits, replacing more than 100 percent of lost earnings for two-thirds of unemployed workers. As a result, the authors find very different spending patterns for unemployed households during the pandemic.

Although average spending fell for all households as the economy shut down at the start of the pandemic, the authors find that unemployed households actually increased their spending beyond pre-unemployment levels once they began receiving benefits. The fact that spending by benefit recipients rose during the pandemic instead of falling, like in normal times, suggests that the $600 supplement has helped households to smooth consumption and thus propped-up aggregate demand.

The authors also examine spending patterns of the unemployed while waiting for benefits to arrive. Households that receive benefits soon after job loss show no relative decline in spending, while households that wait two months to receive benefits due to processing delays have large spending declines. Compared to the employed, spending falls by 20 percent prior to receiving benefits. This suggests that delays have imposed substantial hardship on benefit recipients.

This research offers insights into the evolving reactions of Americans to the COVID-19 pandemic along political lines, including their reactions to mask-wearing and the likelihood of further lockdowns. The project consists of seven survey waves beginning in April 2020 and ending in November 2020. These three select findings are compiled from the first five waves, conducted from April 6 to May 18.[1]

1. A loss of income due to the pandemic led many to admit that COVID-19 crisis is worse than they expected, with this effect mitigated by the choice of news source.

In the first wave of the survey, commencing April 6, 35% of Republicans said the media were exaggerating the virus’ threat, compared to only 9% of Democrats. In the fourth wave beginning April 27, 57% of Republicans said the pandemic was worse than they expected, compared with 82% of Democrats. Importantly, as illustrated in the accompanying figure, respondents who lost income were more likely to report that COVID-19 was worse than anticipated: 62% vs. 48% for Republicans, and 84% vs. 75% for Democrats. Regarding media influence, among Republicans, 44% of those who watched Fox News were significantly less likely to report that the virus was worse than expected, compared with 56% of those Republicans who did not watch Fox News. Similarly, among Republicans, those who did not support Trump were 50% more likely to report that the crisis was worse than expected than those expected to vote for Trump.

2. An important factor influencing support for mask wearing is trust in the scientific community. This has decreased significantly among Republicans since the start of the pandemic.

Between the beginning and end of April 2020 (waves one through four), Democrats’ confidence in the scientific community was mostly unchanged, 70% vs. 68%. For Republicans, those numbers fell from 51% to 38%.

3. Political views and perception of the gravity of the crisis also influenced the likelihood of anticipating a second lockdown.

At the end of April, about 30% of Republicans said that the government should fully reopen the economy in May, compared to about 5% of Democrats. In Mid-May, the authors asked 398 Democrats and Republicans whether they thought their state would need to reintroduce lockdown measures before the end of the year; 43% of Republicans said that such a lockdown was likely vs. 76% of Democrats.

Finally, while the authors do not hazard predictions, they stress that their research reveals the influence of dramatic events in changing or reinforcing people’s views and preferences, even if those events occur over a short period. Their next survey, slated for October, will likely provide key insights leading into the election.

Footnote:
[1] Researchers at the Poverty Lab and the Rustandy Center for Social Sector Innovation at the University of Chicago are conducting this longitudinal survey in partnership with NORC at the University of Chicago, an independent, non-partisan research institution. The findings refer to different time frames according to the questions analyzed. Surveys are administered to the same sample of more than 1,400 Americans based on NORC’s probability-based AmeriSpeak Panel, which is designed to be representative of the U.S. population.

While active funds as a whole experience outflows during the crisis, funds that apply exclusion criteria in their investment process receive net inflows. Funds with higher sustainability ratings from Morningstar also receive larger flows, driven especially by environmental concerns.  The pre-crisis trend of flows toward sustainability-oriented funds thus continues during the COVID-19 crisis. The fact that investors retain their commitment to sustainability during a major crisis suggests they have come to view sustainability as a necessity rather than a luxury good.

Despite rich investment opportunities presented by market dislocations, most US active equity mutual funds underperform passive benchmarks between February 20 and April 30, 2020. The average fund underperforms the S&P 500 index by 5.6% during the ten-week period (29% annualized). The average underperformance relative to the style benchmark is 2.1% (11% annualized). Eighty percent of funds have negative CAPM alphas, and average fund alphas computed relative to five different factor models are all negative. These results undermine the popular hypothesis that active funds make up for their disappointing unconditional performance by performing well in recessions.

The arrival of COVID-19 resulted in dramatic changes in the US labor markets with initial claims skyrocketing and a sharp decline in labor-force participation of more than 7 percentage points. Less noticed was the key driver of the drop in labor-force participation: a wave of earlier than planned retirements. The authors use customized surveys on a panel of more than 10,000 Americans before, and at the onset of, COVID-19 to show that the share of Americans not actively looking for work because of retirement increased by 7 percentage points between January and early April of 2020.

This increase is more than twice as large among women as among men. This makes early retirement a major force in accounting for the decline in the labor-force participation. Given that the age distribution of the two surveys is comparable, this suggests that the onset of the COVID-19 crisis led to a wave of earlier than planned retirements. With the high sensitivity of seniors to the COVID-19 virus, this may reflect in part a decision to either leave employment earlier than planned due to higher risks of working or a choice to not look for new employment and retire after losing their work in the crisis.

To better understand which parts of the age distribution might drive the increase of retirees in their survey and whether economic incentives at least partially play a role, the authors plot in the accompanying figure the fraction of those claiming being retired (left scale) both in the pre-crisis wave (yellow line) and in the crisis wave (red) together with the difference between the two (blue line, right scale). The crisis has shifted the whole distribution up, that is, for each part of the age distribution a larger fraction of the survey population now claims being retired. Hence, even for those that are well before retirement age, the authors note a large increase in early retirement. Moreover, a notable jump in the difference occurs at age 66, which is the first year people can claim retirement benefits without penalty from the social security administration (SSA). Historically, only a few people returned from retirement to the labor force, which hints toward a sluggish recovery down the road.

This work builds on the authors’ late-April research (The Targeting of the Paycheck Protection Program) that did not find evidence that PPP funds flowed to areas that were more adversely affected by the economic effects of the pandemic, and that lender heterogeneity in PPP participation explains, in part, the weak correlation between economic declines and PPP lending.

In this work, the authors present two new findings:

1. They reveal no evidence that the PPP had a substantial effect on local economic outcomes during the first round of the program. The authors examined weekly firm-level employment and shutdown data, and they confirmed this evidence using initial unemployment insurance claims at the county level. The absence of a significant effect on UI claims during the initial weeks of the program is striking, especially given that one motivation for the PPP was to provide “relief” for congested state unemployment insurance systems. If the significant funds disbursed by PPP had little effect on unemployment, then what did firms do with the extra cash? The answer follows:
2. The authors draw on Census Small Business Survey data to reveal that firms used PPP funds to increase liquidity, to make loan payments, and to meet other financial obligations. For these firms, the PPP may have strengthened balance sheets at a time when shelter-in-place orders prevented workers from working, and when unemployment insurance was more generous than wages for a large share of workers. Importantly, this suggests that while employment effects are small in the short run, they may well be positive in the medium run because firms are less likely to close permanently. Finally, many less affected firms received PPP funding and may have continued as they would have in the absence of the funds, either by spending less out of retained earnings or by borrowing less from other sources.

For policymakers charged with crafting effective policies that meet desired goals, measuring the social insurance value of the PPP is essential. As data become available, the authors will continue to examine the program’s effects on firms’ ability to meet commitments, as well as other medium- and long-term effects.

The list of uncertainties surrounding the COVID-19 pandemic is long, beginning with health-related issues and extending to the economy, including infection rates, vaccine development, possible new infection waves, near-term policy effects, economic recovery rates, government interventions, shifts in consumer spending, and many other issues.

To get their hands around the nature and scope of economic uncertainty before and during the pandemic, the authors examined a number of measures that focus on forward-looking uncertainty measures. Those measures are illustrated in the figures below; broadly speaking, they reveal huge—and varying—uncertainty jumps, including an 80 percent rise (relative to January 2020) in two-year implied volatility on the S&P 500, to a 20-fold rise in forecaster disagreement about UK growth. Also, time paths differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting the difference in uncertainty measures between Wall Street and Main Street.

While cautious about predictions, the authors do suggest that such high levels of uncertainty are not conducive to a rapid economic recovery. Elevated uncertainty generally makes firms and consumers cautious, retarding investment, hiring, and expenditures on consumer durables. Given the scale of recent job losses and the collapse in investment, a strong, rapid recovery would require a huge surge in new activity, which unprecedented levels of uncertainty will discourage.

The COVID-19 pandemic hit the US labor market with astonishing speed. For the week ending March 14, 2020, there were 250,000 initial unemployment insurance claims—about 20% more than the prior week, but still below January levels. Two weeks later, there were over 6 million claims, shattering the pre-2020 record of 1.07 million, set in January 1982. As of mid-June, claims remained above one million for 13 consecutive weeks, with a cumulative total of over 40 million. At the same time, the unemployment rate spiked from 3.5% in February to 14.7 percent in April, and the number of people at work fell by 25 million.

Given the rapid nature of these extensive job losses, and the inability of existing labor market information systems to keep up with such changes, the authors devised a measurement method that combines data from traditional government surveys with non-traditional data sources, particularly daily work records compiled by Homebase, a private sector firm that provides time clocks and scheduling software to mostly small businesses. The authors linked this data with a survey answered by a subsample of Homebase employees, as well as other data sources to measure the effects of shelter-in-place orders and other policies on employment patterns from March to early June.

The unemployment rate (not seasonally adjusted) spiked by 10.6 percentage points between February and April, reaching 14.4%, while the employment rate fell by over 9 percentage points over the same period. These two-month changes were roughly 50% larger than the cumulative changes in the respective series in the Great Recession, which took over two years to unfold. Both unemployment and employment recovered a small amount in May, but remain in unprecedented territory.

The authors’ novel methodology delivers insights beyond official statistics. For example, Panel B of the accompanying Figure reveals that total hours worked at Homebase firms fell by approximately 60% between the beginning and end of March, with the bulk of this decline in the second and third weeks of the month—facts that go unrevealed in government data. The largest single daily drop was on March 17, when hours, expressed as a percentage of baseline, fell by 12.9 percentage points from the previous day. The nadir seems to have been around the second week of April. Hours have grown slowly and steadily since then.

The CARES Act, signed into law on March 27 to combat the economic fallout from the COVID-19 pandemic, is the largest economic stimulus in US history. Among its many provisions, CARES also contained several corporate tax breaks. Ostensibly, these tax breaks provided immediate liquidity and incentives for firms to avoid layoffs. However, the tax breaks have received a lot of criticism, with some calling them a “giveaway” to large corporations, and several Democratic politicians have introduced measures to scale them back.

An analysis of SEC filings—in which publicly-traded US firms are required to discuss material events—since the passage of CARES reveals the following:

1. Most firms (61%) do not discuss the CARES tax provisions in their filings, suggesting the tax provisions did not materially impact most publicly-traded US firms.

2. The most commonly discussed tax provision was the NOL carryback rule, which allows firms to recoup prior taxes paid. While this provision can provide immediate liquidity, it only applies to firms that were unprofitable in the years immediately prior to the pandemic. The other tax provisions were discussed by fewer than 15% of firms.

3. The firms that were most likely to discuss the NOL carryback provision were those with pre-pandemic losses and large stock price declines during the pandemic, rather than those operating in states or sectors with large increases in unemployment.

4. In contrast, the payroll tax deferral, which was designed to provide liquidity to a broad sample of firms, was more likely to be discussed by firms with more employees and lower cash holdings. And the employee retention credit, intended to encourage firms to keep employees on payroll while they were not working, was more likely to be discussed by firms operating in industries and states with larger unemployment changes. Thus, these two tax provisions appear more likely to benefit firms hardest hit by the pandemic.

5. Certain firms (including those that eroded their liquidity with large shareholder payouts and engaged in substantial lobbying during the CARES Act debate) may have avoided discussing these tax breaks in their SEC filings for fear of negative public attention.

The authors acknowledge that firms may benefit from the provisions without discussing them in their SEC filings, and thus the full picture as to how these tax breaks affected U.S. firms will not be clear for some time. However, these early findings cast some doubt on the idea that the CARES corporate tax provisions provided significant liquidity and incentives to retain employees for most publicly-traded U.S. firms. Furthermore, the most frequently discussed tax provision—the NOL carryback—may have primarily benefitted the firms (and their shareholders) whose stock price had deteriorated the most prior to CARES, rather than the firms operating in areas hardest hit by the pandemic.

Using data from ADP¹ 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 21% between mid-February and late-April, 2020. Given that US private employment in February was 128 million workers (on a non-seasonally adjusted basis), the ADP data suggest that total paid employment in the US fell by about 26.5 million through late April. As of late May, paid employment is still about 19.5 million jobs below its mid-February levels.

The authors reveal that employment declines were disproportionately concentrated among lower-wage workers: 30% of all workers in the bottom quintile of the wage distribution lost their job, at least temporarily, through May. The comparable number for workers in the top quintile was only 5%. Finally, the authors reveal that businesses have cut nominal wages for about 10 percent of continuing employees, about twice the rate during the Great Recession, while forgoing regularly scheduled wage increases for others.

1 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.

Employment declines during the Pandemic Recession were much larger for businesses with fewer than 50 employees, with closures playing an even larger role for this size group. Businesses with fewer than 50 employees saw paid employment declines of more than 25 percent through April 18, while those with between 50 and 500 employees and those with more than 500 employees, respectively, saw declines of 15-20 percent during that same period, and reached troughs a week or two later than the smallest businesses.

The largest declines in employment were in sectors that require substantive interpersonal interactions. Through late-April, paid employment in the “arts, entertainment and recreation” and “accommodation and food services” sectors (i.e., leisure and hospitality) both fell by more than 45 percent while employment in “retail trade” fell by almost 30%. Businesses like laundromats and hair stylists also saw employment declines of nearly 30%. Despite a boom in emergency care treatment within hospitals, the “health care and social assistance” industry experienced a 16.5% decline in employment through late April.

The spread of COVID-19 has not been uniform across the country. Urban areas have generally seen more aggressive spreads of the virus. These differences manifested themselves somewhat in the labor market as well. There is a strong relationship between the exposure to COVID-19 and employment declines.

While employment fell in all states, the employment declines were largest in those states that had more disease exposure. The authors compare two groups of states: (1) a set of large states that broadly opened in late April or early May (FL, GA and TX), and (2) a set of large states that broadly opened in late May and early June (IL, PA, VA and WA). Looking at employment in the Food and Accommodations Sector for both groups of states, the authors find employment in this sector fell similarly through mid-April in both state groupings. Starting in late April, employment in this sector within the states opening early increased faster than employment in the states opening later. In the states that opened early, however, employment in this sector is still 40 percent below February levels as of mid-May. This suggests that opening does not guarantee employment will fully rebound in these sectors.

The authors also found that employment in these sectors within states that opened later started to increase even prior to those states re-opening. While the increase was modest it showed that demand was increasing even before the states officially re-open. These findings suggest caution by researchers and policymakers alike seeking to link employment gains to re-opening schedules.

Through late April, women experienced a decline in employment that was 4 percentage points larger than men (22 percent for women to 18 percent for men). The gap has grown slightly to 5 percentage points through mid-May. These trends are in sharp contrast to prior recessions where men experienced larger job declines. Why are women being hit harder in the Pandemic Recession? The answer is not clear. One obvious factor is that traditionally female dominated industries, such as retail, leisure and hospitality industries, are being hit harder by the recession. The authors find, however, that less than 0.5 percentage points of the 4-5 percentage point difference in employment losses between men and women can be explained by industry. In other words, across industry sectors, women are experiencing larger job declines relative to men.

More research using household-level surveys with additional demographic variables can explore this critical question. It may be that other factors of the pandemic, such as an increased need for childcare, will explain some portion of the gender gap in employment losses during the recession.

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.

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.

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), [1]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.

[1] The SBU is a monthly panel survey developed and fielded by the Federal Reserve Bank of Atlanta in cooperation with Chicago Booth and Stanford.

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.

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 “reopening” 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 as the reluctance of individuals to engage in social economic activity.

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: 

1. 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.
2. 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.
3. 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.

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.

In response to the economic fallout of the COVID-19 pandemic, the US government has enacted the CARES Act, with over $2 trillion of stimulus measures. Amongst its various provisions, American households under certain income thresholds qualify to receive direct payments in the form of stimulus checks.* How did households respond to this cash infusion?

In updated research, the authors studied households’ consumption and spending behavior responses to the stimulus checks through a multitude of dimensions, using high-frequency, real-time household financial transaction data. By observing 44,460 individuals across the US who received stimulus checks, the authors found that households responded rapidly at first by increasing spending by $0.29 per dollar of stimulus during the first 10 days of observation, primarily on food and non-durable goods, and rent and bill payments. Households with lower incomes, greater income declines, and lower levels of liquidity exhibit relatively stronger spending responses.

Household liquidity plays the most important role in determining spending behavior, with no observed spending response for households with relatively higher levels of bank balances and ready access to funds. Compared to the 2001 recession and 2008 Financial Crisis, the study found relatively little increase in spending on durable goods, with a number of potentially important downstream implications for the economic recovery.

These findings could inform policy formulation and help reduce the time to gauge impact between a policy’s enactment and its implementation. Likewise, further debate is warranted on the timely targeting of stimulus checks, their distribution, and intended effects in jump starting consumer spending to facilitate recovery.

*Individuals earning less than $75,000 get checks worth $1,200, and $2,400 for married couples earning less than $150,000 – each qualifying child entitles the household to an additional $500 of direct payments. Single households earning between $75-99,000 get increasingly smaller checks, and those earning above $99,000 ($198,000 for couples) will not qualify for any stimulus checks.

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” (PVS_t)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 PVS_t 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 PVS_t, 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, PVS_t, 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. PVS_t 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 PVS_t and the aggregate stock market during the COVID-19 pandemic (February 14, 2020 through April 30, 2020). The figure shows that PVS_t 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 PVS_talmost completely untouched, implying that perceptions of risk had not changed significantly. In other words, the evolution of PVS_t 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, PVS_t 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 PVS_t 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. PVS_t 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 PVS_t and the aggregate stock market to the passing of the bill is consistent with the authors’ previous interpretation that PVS_t 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.

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.

South Korea’s success in battling COVID-19 is largely due to its widespread testing and contact tracing, but its key innovation is to publicly disclose detailed information on the individuals who test positive for COVID-19. This new research reveals that public disclosure measures are more effective at reducing deaths than comprehensive stay-at-home orders.

The COVID-19 outbreak was identified in South Korea on January 13, and since then South Koreans have received text messages whenever new cases were discovered in their neighborhood, as well as information and timelines of infected persons’ travel. The authors combined detailed foot-traffic data in Seoul with publicly disclosed information on the location of individuals who had tested positive. The results reveal that public disclosure can help people target their social distancing, which proves especially helpful for vulnerable populations who can more easily avoid areas with a higher rate of infection.

The authors estimate that over the next two years, the current strategy in Seoul will lead to a cumulative 925,000 cases, 17,000 deaths (10,000 for those 60 and older and 7,000 for ages 20 to 59), and economic losses that average 1.2 percent of GDP. In a model representing partial lockdown, the authors estimate the same number of cases, but deaths increase from 17,000 to 21,000 (14,000 for those 60 and older and 7,000 for ages 20 to 59) and economic losses increase from 1.2 to 1.6 percent of GDP.

Importantly, while death rates among older populations are significantly higher under lockdowns, those under 60 suffer economic losses twice as high, compared to South Korea’s current strategy.

In the absence of a vaccine, the authors conclude that targeted social distancing is much more effective in reducing the transmission of the disease, while minimizing the economic cost of social isolation. However, they also note that these benefits come with a cost: Disclosure of public information infringes upon the privacy of affected individuals. The authors anticipate the day when cost measures for privacy loss are available, after which a full cost/benefit analysis is possible.

Testing for COVID-19 is only as good as compliance. If people don’t show up for testing, or if only symptomatic people show up, then the benefits of such a program will be lost, as “silent spreaders” will go undetected. Indeed, costs could increase under such a scenario if people are encouraged to re-engage in the economy under the false promise of such a testing program.

The question, then, is how to encourage healthy people to stand in line with, possibly, sick people, to undergo an uncomfortable test, and then return in two weeks to do it again, and for many weeks after that. The answer lies at the heart of economics—incentives—and the authors offer a unique suggestion: a COVID lottery (which they coin “Pandemillions”) that gives away large prizes every week to random test participants. On Sunday mornings, for example, states would notify individuals selected for testing that week, and those people would then have until the end of the week to get tested. A completed test would convert into a “ticket” in the lottery, with winners announced every Saturday night.

The benefits of widespread testing would be large, and the federal government could afford to fund a very lucrative prize pool. At $200 million per week, the annual cost of the lottery would only be only $10 billion, or roughly 0.5% of the cost of the CARES Act. As to implementation, while a federal lottery might be optimal, given that 45 states already manage lotteries, the best path forward might be to use existing state infrastructure.

For those who need incentive to quarantine once they test positive, the authors recommend a second plan: offer a $2,000 weekly payment for every American adult compelled to stay home, even if they are asymptomatic. Based on quarantining up to 20 million people this year, the cost would approach $80 billion, a large but still quite modest sum compared to the total costs of this pandemic.

Strong incentives cause strong reactions, and it is possible that some individuals would purposefully try to contract COVID-19 to receive stay-at-home payments; however, the authors believe this number would be sufficiently low and would not come close to outweighing the program’s significant benefits. The authors also acknowledge that while such payments would likely face political hurdles, the high returns from such a program—in morbidity and mortality reductions, and resources saved—would also prove politically attractive.

Absent a vaccine, which is at best a number of months out, the best way to safely reopen the economy is to establish a testing regimen for COVID-19 which ensures that all individuals—both symptomatic and asymptomatic—get tested on a regular basis.

One provision of the CARES Act created an additional $600 weekly unemployment benefit to help workers losing jobs as a result of the COVID-19 pandemic. The authors use micro data on earnings together with the details of each state’s UI system under the CARES Act to compute the entire distribution of current UI benefits and show how replacement rates vary across occupations and states.

The authors find that 68% of unemployed workers who are eligible for UI will receive benefits that exceed lost earnings. The median replacement rate is 134%, and one out of five eligible unemployed workers will receive benefits at least twice as large as their lost earnings. We also show that there is sizable variation in the effects of the CARES Act across occupations and across states, with important distributional consequences. For example, the median retail worker who is laid-off can collect 142% of prior wages in UI, while grocery workers are not receiving any automatic pay increases. Janitors working at businesses that remain open do not necessarily receive any hazard pay, while unemployed janitors who worked at businesses that shut down can collect 158% of their prior wage.

After documenting these basic patterns, the authors explore how various alternative UI expansion policies would alter the distribution of replacement rates. We show how the parameters of various simple UI expansion policies shape the entire distribution of UI benefits across workers and thus provide a lens into how policy choices jointly affect liquidity provision, progressivity, and labor supply incentives.

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.

Using customized large-scale surveys, this work provides real-time estimates on the changing economic landscape following lockdowns. The authors find that consumer spending for a typical US household dropped by $1,000 per month, which corresponds to a 31% drop in overall spending. Households also spent substantially less on discretionary expenses and decreased their planned spending on durables, with an average drop in spending on durables of almost $1,000.

Strikingly, they find one of the largest drops occurring for debt payments. This result highlights the possibility of a wave of defaults in the next few months, which could ultimately affect the financial system, slow the economic recovery and explain the recent increase in loan provisions by major US banks.

In line with these negative outcomes at the individual level, households’ macroeconomic expectations have become far more pessimistic. Average perceptions of the current unemployment rate increased by 11 percentage points, with similar magnitudes for expectations of unemployment over the next three to five years, indicating that households expect the downturn to have persistently negative effects on the labor market. Consistent with this view, inflation expectations over the next twelve months dropped sharply on average while uncertainty increased. Current mortgage rate perceptions as well as expectations for the end of 2021 dropped on average by about 0.4 percentage points with even larger drops in average expectations over the next five to ten years.

The negative effect on long-run expectations suggests that the lower bound on nominal interest rates might be a binding constraint for monetary policymakers for the foreseeable future. Increased uncertainty at the household level and the large drop in planned spending point toward some form of liquidity insurance to curb the desire for precautionary spending and stimulate demand once local lockdowns are lifted.

Finally, to assess the economic damage that households attribute to the virus, the authors elicited information on the perceived financial situation of the survey participants and possible losses due to the coronavirus, both in income and wealth. Forty-two percent of employed respondents reported having lost earnings due to the virus with an average loss of more than $5,000. More than 50% of households with significant financial wealth reported having lost wealth due to the virus and the average wealth lost is at $33,000. This decline in wealth is putting further downward pressure on future consumption.

Using data from ADP[1] 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.

 ^[1] 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.

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.

This paper takes an early look at a large and novel small business support program that was part of the initial crisis response package, the Paycheck Protection Program (PPP).

First, we find no evidence that funds flowed to areas that were more adversely affected by the economic effects of the pandemic, as measured by declines in hours worked or business shutdowns. If anything, we find some suggestive evidence that funds flowed to areas less hard hit. The fraction of establishments receiving PPP loans is greater in areas with better employment outcomes, fewer COVID-19 related infections and deaths, and less social distancing.

Second, lender heterogeneity in PPP participation appears to be one reason why we find a weak correlation between economic declines and PPP lending. For example, we find that areas that were significantly more exposed to banks whose PPP lending shares exceeded their small business lending market shares received disproportionately larger allocations of PPP loans. Underperforming banks—whose participation in the PPP underperformed their share of the small business lending market—account for two-thirds of the small business lending market but only twenty percent of total PPP disbursements. The top-4 banks alone account for 36% of the total number of small business loans but disbursed less than 3% of all PPP loans.

Our results highlight the importance of banks as a conduit for public policy interventions. Measuring these responses is critical for evaluating the social insurance value of the PPP and similar policies.

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.

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.

Understanding the nature of a negative economic shock is key to getting the policy prescription right. After ensuring that households have enough short-term resources, policymakers are confronted with the following conundrum: Should the aim of policy be to encourage people to spend more, that is to provide stimulus, or should policy focus purely on providing forms of social insurance?

The authors’ key insight is that the coronavirus shock is a supply shock of a special nature, as it affects different sectors unevenly. The central argument of their work is that the coronavirus shock will likely cause a reduction in aggregate demand larger than the original reduction in labor supply, something that the authors coin a “Keynesian supply shock.” Their work describes two forces that propagate the shock from those it directly affects, or those in affected (or contact-intensive) sectors, to those in less affected sectors: complementarities across sectors and incomplete markets. In the first case, when people are restricted from spending on certain goods, like restaurants and events, they do not spend the same amount on other complementary goods and services, and there is less overall spending

In the second case, the overall reduction in spending spreads to unaffected sectors because those who retain their jobs do not spend enough to prevent this occurrence (in economists’ parlance, the marginal propensity to consume of those in the unaffected sectors is less than those in affected sectors). Together, these two forces transform the original supply shock into a demand shock.

The authors’ findings pose challenges for policymakers, as a “typical” increase in government consumption may be less powerful in a pandemic shock. The reason is that government spending can only lift incomes in the unaffected sectors, not in the affected sectors, but it’s the workers in the affected sectors who have the highest propensity to consume, and they are exactly those who cannot benefit from an aggregate spending increase. On the other hand, fiscal stimulus can be desirable when combined with polices more targeted towards the workers in the affected sectors.

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/.

Most states and cities in the US have shut all non-essential businesses in response to COVID-19. In this note, we argue that as policies are developed to “re-open” the economy and send people back to work, strategies for childcare arrangements, such as reopening schools and daycares, will be important. Substantial fractions of the US labor force have children at home and will likely face obstacles to returning to work if childcare options remain closed.^[1] Younger workers, who might be able to return to work earlier to the extent that they are less susceptible to the virus, are also more likely to require childcare arrangements in order to return to work.

Using 2018 data from the Census Bureau’s American Community Survey, we calculate the share of employed households who are affected by childcare constraints.^[2] We focus on the civilian employed population older than 18.

The first row in Table 1 shows that 32% of that workforce has someone in their household who is under 14. Thus, 50 million Americans must consider childcare obligations when returning to work. Daycares and preschools might open sooner than primary schools, since they tend to have fewer children and thus less scope for disease transmission, so the remaining columns of Table 1 distinguish children under 6 and those 6-14 years old. For about 30% of the workforce with childcare requirements, all of their children are under the age of 6. Thus, opening daycares alone could address childcare obstacles for one in three constrained workers.

Of course, many workers with children at home are not sole caregivers. Workers who live in a household with another non-working adult – such as a partner who is not employed, a retired parent or in-law, or an older child above 18 who lives at home – can likely return to work while another household member addresses their childcare needs. The second row of Table 1 reports the share of all workers who live in a household with someone under 14 and no available caregiver. If non-working adults can assume household childcare responsibilities, 21% of the workforce would nonetheless have unaddressed childcare obligations.

Although 21% of the workforce will face some childcare burden when schools and daycares remain closed, some of them may resume work while other workers in their household address childcare needs. In particular, many workers with children live in households with other workers. Each household would potentially only need one adult to remain home with the children, freeing up the other adults to return to work. The third row of Table 1 shows that accounting for these childcare options leaves 11% of the workforce (or 17.5 million workers) facing major barriers to work if schools and daycares remain closed.

The White House and various other commentators have proposed a phased reopening of the economy in which initially only younger, less vulnerable workers return to work (https://www.whitehouse.gov/openingamerica/). Schools, daycares, and camps are proposed to open in later phases. Since older patients are more vulnerable to COVID-19, this would potentially balance the health risks for the most at-risk population while promoting economic activity. However, the obstacles to returning to work imposed by school closings are somewhat higher for the under 55 population, because 40% of these workers have a child at home. Table 1 shows that 14% (or roughly one in seven) of workers under 55 would likely face childcare-related obstacles to returning to work (even after accounting for the fact that in this scenario, workers over 55 in the household could then provide childcare).  Under a policy where young workers return to work while schools remain closed, 35 million workers who are over 55 would not be able to return to work and another 16 million who are under 55 would be constrained by childcare obligations.

The obstacles that childcare imposes on workers during the COVID-19 crisis is similar across industries. Table 2 shows the key statistics for each broad industry category: the share of workers without within-household child care would only range from 18% in transportation to 25% in education and health care.

Figure 1 depicts spatial variation in the share of workers with childcare obligations and no available caregiver in their household. While this figure is as low as 13% and as high as 33% for some commuting zones, the vast majority of regions are near the national average of 21%. Thus, addressing childcare obligations as part of “re-opening” strategies is an important consideration for policymakers across the United States.

These results suggest that childcare-related constraints imposed by school closings should feature prominently in discussions of reopening the economy. While there is scope for a large rebound in employment even if schools and daycares remain closed, the economy will remain 17 million workers short of normal employment in this scenario. Furthermore, many of those working when schools are closed will only be able to do so if a spouse or partner or who would typically be working instead remains home. The longer school closures persist into the recovery of the economy, the greater will be the burden faced by those workers with young children and no obvious childcare options. We again note that we are making no attempt to evaluate any public-health benefits of school closures or make any assessment of when schools should be reopened. Public-health policies that mitigate the spread of the virus likely have high returns for the ultimate shape of any economic recovery. We instead simply note that discussions of returning to work ought to include discussion of returning to school.

References

Alon, Titan, Matthias Doepke, Jane Olmstead-Rumsey, and Michele Tertilt. “The Impact of COVID-19 on Gender Equality”, Covid Economics: Vetted and Real-Time Papers, Issue 4, April 14 2020.

^[1] We explicitly refrain from any evaluation of public-health considerations related to school closures since we have no expertise in this area.  We instead seek to focus solely on measuring economic constraints that arise in a phased employment recovery. It is entirely possible that these constraints may be unavoidable for public-health reasons.

^[2] Alon, Doepke, Olmstead-Rumsey and Tertilt (2020) use ACS data to compute a number of closely related statistics, but they focus on measuring household childcare burdens while we use employed workers as our unit of analysis and focus specifically on measuring the importance of childcare constraints for aggregate, regional, and industry employment.

The initial allotment for the Small Business Administration’s Paycheck Protection Program (PPP) was $349 billion, and was meant to cover primarily employee costs—including some funds for utilities, rent, and mortgage interest—for approximately eight weeks. However, many in Congress now deem this insufficient and the Treasury Dept. has requested an additional $250 billion, bringing the potential total to $599 billion. This begs the question: How many applications could be submitted and how big should PPP be? The authors calculate that maximum requests could total $720 billion (updated 4/16) if all small businesses in the US apply.

To make their calculations (see Paycheck Protection Program Calculation Tool online), the authors determined two pieces of information: the number of eligible businesses, and those businesses’ monthly payroll costs, including salaries, wages, retirement, and benefits. Eligible businesses include those with less than 500 employees, with an exception for larger businesses in the accommodation and food service sectors. In sum, the authors calculate about $3.4 trillion in total estimated payroll cost for the purposes of PPP that, when divided by 12 and multiplied by 2.5 to get the total eligible loan amount, comes to about $720 billion.

If Congress decides to increase the pool of funds to $600 billion, the PPP should be at least close to sufficiently funded to fulfill all application requests, mitigating the problems of the “first-come, first-served” design. However, it is also true that at $600 billion, Congress and taxpayers would not just fund a subset of small businesses in need, but would instead fund nearly the entire payroll for all small businesses for two months.

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.

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.[1] 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.

The authors focus on three key variables: the employment-to-population ratio, the unemployment rate, and the labor force participation rate. Historically, the employment-to-population ratio and the unemployment rate are near reverse images of one another during recessions, as workers move out of employment and into unemployment. More severe recessions also sometimes lead to a phenomenon of “discouraged workers,” in which some unemployed workers stop looking for work. These workers are reclassified as “out of the labor force” by Bureau of Labor Statistics (BLS) definitions, so the unemployment rate can decline along with the labor force participation rate while the employment-to-population ratio shows little recovery.

The authors figures, based on survey data from Coibion et al. (2020), document the following three facts. First, the employment-to-population ratio has declined sharply from 60% down to 52.2% (Panel B). This decline in employment is equivalent to 20 million people losing their jobs and is larger than the entire decline in the employment to population ratio experienced during the Great Recession. Second, the unemployment rate rose from 4.2% to 6.3% (Panel A). While this increase is the single biggest discrete jump in unemployment over the last 15 years, this change in unemployment corresponds only to about one-third of the increase observed during the Great Recession. For comparison with the employment-to-population ratio, if all twenty million newly unemployed people were counted in the unemployment rate, there would have been an increase in the unemployment rate from 4.2% to 16.4%, the highest level since 1939. Third, the reason for the discrepancy between the two is that many of the newly non-employed people are reporting that they are not actively looking for work, so they do not count as unemployed but rather as exiting the labor force. The labor force participation rate dropped from 64.2% to 56.8% (Panel C). Our survey evidence suggests that 6 percentage points of the decline and, hence, almost the entire decrease can be explained by people moving out of the labor force into retirement.

The typical approach in the epidemiology literature is to study the dynamics of the pandemic–for infected, deaths, and recovered–as functions of some exogenously chosen diffusion parameters, which are in turn related to various policies, such as the partial lockdown of schools, businesses, and other measures of diffusion mitigation. We use a simplified version of these models to analyze how to optimally balance the fatality induced by the epidemic with the output costs of the lockdown policy. The planner’s objective is to minimize the present discounted value of fatalities while also trying to minimize the output costs of the lockdown policy.

In our baseline parameterization, conditional on a 1% fraction of infected agents at the outbreak, the possibility of testing and no cure for the disease, the optimal policy prescribes a lockdown starting two weeks after the outbreak, covering 60% of the population after one month. The lockdown is kept tight for about a full month, and is subsequently gradually withdrawn, covering 20% of the population three months after the initial outbreak. The output cost of the lockdown is high, equivalent to losing 8% of one year’s GDP (or, equivalently, a permanent reduction of 0.4% of output). The total welfare cost is almost three times bigger due to the cost of deaths. The intensity of the lockdown depends on the gradient of the fatality rate as a function of the infected, the value of a statistical life, and the availability of testing. We find that an antibody test, which allows to avoid lockdown of those immune, improves welfare by about 2% of one year’s GDP.

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.

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.

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.

One of the provisions of the new stimulus bill is called Pandemic Unemployment Assistance, which will extend unemployment benefits to self-employed workers, including gig workers. This is very different from the response in the Great Recession, when UI was not extended to the self-employed. While todays’ provisions are not completely unprecedented—they are largely based on the 1974 Disaster Unemployment Act—nothing like this has ever happened at this scope and scale. The author’s new research on gig work provides some insight into how many gig workers might be newly eligible for new Unemployment Insurance.

In research examining administrative tax records, Koustas and his co-authors find that around 11% of the workforce engages in some type of gig work. If we define gig work as all independent contract/ freelancing, most gig work is not at all new (see Figure 1). While gig work has grown over the last few years, almost all of the recent growth has come from work mediated via new online platforms, the largest component of which are ridesharing platforms.

Around 60% of gig workers do this work as a “side-gig,” holding a “regular” job as a traditional employee. This share rises to 81% in the online platform economy. For these workers, unemployment benefits eligibility will almost certainly be determined based on their main, non-gig job. Still, millions of gig-only workers might now be eligible for benefits, represented by the yellow line in Figure 1 below.

While gig work in the online platform economy is concentrated in urban areas, the highest concentration of gig work is actually in more rural areas of the plains and Southern states, reaching 20% or more of all work in some counties (see Figure 2). These geographic patterns are important because implementation and eligibility verification for the new UI benefits will be left to the states.

As a result of the scale of the current crisis, as well as the lack of precedent and federal guidance on how to verify gig and self-employment income, state governments are likely to face novel challenges that will mean delays and barriers for workers eligible for benefits.

With mandated shutdowns of most non-essential businesses, the great majority of small businesses in the United States are under serious economic strain. As rents become due, many will fail to make their payments, resulting in mass defaults. This is harmful to the tenants, our small business community, but also to the landlords who value and rely on these long-term relationships. In typical times, landlords would work with tenants to work out alternatives before moving forward with eviction proceedings. But these processes can be timely and expensive.

While the Coronavirus, Aid, Relief and Economic Security (CARES) Act offers forgivable loans to help small businesses cover their expenses, millions of businesses may not survive the time it takes for the transaction. A customizable, one-page lease addendum, drafted by the authors in coordination with legal and business input, provides a simple tool for appending to and modifying any commercial lease. The authors recommend that tenants pay only 10 percent of their usual rents during the relief period, with a further recommendation that 90 percent be deferred and 10 percent permanently forgiven by the landlord.

For more information visit: https://centerforrisc.org/lease

Absent a vaccine or widespread testing,[1] “social distancing,” which requires employees in many jobs to work from home, is the best policy option to reduce the spread of COVID-19. This suggests that returning to work will likely occur more slowly for jobs that require a large degree of proximity to other individuals, such as those who work in closely arranged cubicles. So, who are the workers who do not have the opportunity to work from home and, therefore, are at greater risk of infection?

Building on recent work that describes the type of jobs that allow for working at home[2] and merging multiple datasets, the authors of this new research compare the characteristics of individuals in various occupations who cannot work from home to those of workers in occupations that can work from home. Individuals in occupations that cannot be done from home are:

• economically more vulnerable,
• less likely to have a college degree,
• less likely to have health insurance,
• likely nonwhite,
• likely to work at a small firm,
• likely to rent, rather than own, their home,
• and more likely born outside the United States.

An understanding of how individuals vary across occupations, and the likely impact of such strategies as social distancing, is important for policymakers considering how to best target economic policies designed to assist workers.

[1] See related fact on this page and BFI White Paper in this series, “An SEIR and Infectious Disease Model with Testing and Conditional Quarantine”
[2] See related fact on this page and BFI White Paper in this series, “How Many Jobs Can Be Done At Home?”

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,[1] 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.

[1] http://caseymulligan.blogspot.com/2020/03/the-economic-cost-of-shutting-down-non.html

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.[1] 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.[2] 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.[3]

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.[4] 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.

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.[5] As some states have already started doing,[6] easing licensing restrictions can give hospitals the flexibility to better cope with this unprecedented spike in demand.

[1] 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.
[2] 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.
[3] The authors restrict their analysis to those working in hospitals and physicians’ offices, as these industries are most relevant for COVID-19 care.
[4] Data on additional occupations are shown in the Appendix.
[5] https://khn.org/news/help-wanted-retired-doctors-and-nurses-don-scrubs-again-in-coronavirus-fight/
[6] E.g., https://malegislature.gov/Bills/191/S2615 and http://www.op.nysed.gov/COVID-19Volunteers.html

To evaluate the economic impact of “social distancing,” one must determine how many jobs can be performed at home, what share of total wages are paid to such jobs, and how the scope of working from home varies across cities and industries. By analyzing surveys[1] about the nature of people’s jobs, the authors classified whether that work could be performed at home. The authors then merged these job classifications with information from the Bureau of Labor Statistics on the prevalence of each occupation in the aggregate, as well as in particular metropolitan areas and industries.

This analysis reveals that 37% of US jobs can plausibly be performed at home. Assuming all occupations involve the same hours of work, these jobs account for 46% of all wages (occupations that can be performed at home generally earn more). As the accompanying map indicates, there is significant variation across cities. For example, 40% or more of jobs in San Francisco, San Jose, and Washington, DC, can be performed at home, compared with fewer than 30% in Fort Myers, Grand Rapids, and Las Vegas. There are also large differences across industries. A large majority of jobs in finance, corporate management, and professional and scientific services can be performed at home, whereas very few jobs in agriculture, hotels, or restaurants can do so.

[1] Feasibility of working from home was based on two surveys from the Occupational Information Network (O*NET).

A large number of businesses are mostly shut down for public health reasons, others are facing greatly diminished demand or are likely to shut down in the near future. Using data from the Bureau of Labor Statistics Current Employment statistics by detailed NAICS industry codes, we can measure how many people work in these most exposed businesses. Six of the most directly exposed sectors include: Restaurants and Bars, Travel and Transportation, Entertainment (e.g., casinos and amusement parks), Personal Services (e.g., dentists, daycare providers, barbers), other sensitive Retail (e.g., department stores and car dealers) and sensitive Manufacturing (e.g., aircraft and car manufacturing). In total, these sectors account for just over 20% of all US payroll employment, so shutdowns of these sectors on their own will lead to massive declines in employment. These will be offset partially by increased hiring in grocery stores, package delivery, etc., but this is unlikely to do much to dampen the blow.

This will likely get much worse if these shutdowns persist for multiple months and to the extent that they start to spill over substantially into other sectors like construction and broader manufacturing. Policy measures to reduce the depth and long-run effects of the recession should focus on 1) limiting the spread of the virus itself through direct health spending and allowing for effective social distancing, such as paid sick leave, expanded unemployment insurance and providing the tools for businesses with lots of in-person contact to idle; 2) providing liquidity so that households in shutdown industries can continue to shelter at home, eat, and not face devastating declines in their financial conditions. These policies will limit the long-run harm of the recession and also reduce the spillovers into less directly affected industries. Note that providing liquidity also helps with allowing for social distancing and the first policy goal.

Footnote: NAICS Classification: Restaurants and bars: 7223-7225. Travel and Transportation: 4811,4812, 4853, 4854, 4859, 4881,4883, 7211. Personal Services: 6212, 8121,8129. Entertainment: 7111, 7112, 7115, 7131, 7132, 7139. Other sensitive retail: 4411, 4412, 4421, 4422, 4481, 4482, 4483,4511,4512, 4522, 4531, 4532, 4539, 5322, 5323, 4243, 4413, 4543. Sensitive Manufacturing: 3352, 3361, 3362, 3363, 3364, 3366, 3371, 3372, 3379, 3399, 4231, 4232, 4239, 3132, 3141, 3149, 3152.

How can we understand today’s enormous increase in UI claims at the onset of the COVID-19 epidemic? Given how quickly the situation has moved we knew there would be a large increase in UI claims, whereas in a slower moving crisis, the weekly flows into UI slowly increase as the stock of UI claimants balloons. To put things in perspective we can go back to the Great Recession and accumulate UI claims in excess of what we would normally expect. The chart below shows that new UI claims in one week correspond to all new UI claims during the first six months of the Great Recession.

These statistics reflect public health policy aimed at slowing the spread of the disease. In terms of the labor market, if they also represent workers on temporary layoff, with their jobs kept intact and income support, we may see a V-shaped recovery. If, on the other hand, they represent workers that have now become truly unemployed, with their jobs terminated, and little income support, this will be a painful, slow, L-shaped recovery. As Ganong and Noel note elsewhere in these facts, UI claims may even undershoot the fraction of workers who would be eligible to claim.

On January 23, the Chinese government locked down the city of Wuhan (Hubei Province). In subsequent days, similar measures were taken in other cities in Hubei and throughout China. This note offers some preliminary gauge on the effect of the measures taken to protect public health on economic activity in China. We will make use of three data sources. First, some official data on industrial output already exists. Second, we make use of data on trucking flows to measure the flow of goods across China. Third, Baidu Map data allow us to estimate the effect on services and worker movements within China.

We begin with official data provided by China’s National Bureau of Statistics (NBS). The most recent data (as of March 23, 2020) is from February 2020. Figure 1 shows that industrial value added fell by 4.3% and 25.9% in January and February of 2020 on a year-on-year basis. If the counterfactual growth in absence of the epidemic is 5.7%, the average growth in 2019, the slump would be even more dramatic.

An alternative data on industrial output is data on shipment of goods across Chinese cities. We have data from a private trucking company that provides logistical services to truck drivers. This company, G7, has real-time GPS data from two million trucks, accounting for about 10 percent of all trucks operating in China. We aggregated the movement of trucks in and out of a provincial capital by day. Figure 2 plots the daily truck flows between provincial capital cities, with the beginning day of the year normalized to one. The decline of truck flows before Wuhan lockdown captures the slowdown associated with the coming Chinese New Year. Strikingly, the truck data suggest that goods flows between Wuhan and the other provincial capital cities remained at a very low level and did not recover at all since the lockdown.

The next data we show are flows of people within and between cities. Here, we use indices of movements of people provided by Baidu. This data is based on “location-based services” (LBS) in Baidu Map. Figure 3 plots within-city travel intensity, with the beginning day of the year normalized to one. Panel A and B plot the data for 2019 and 2020, respectively. The red bar in Panel A marks the 2019 Chinese New Year. The black bar in Panel B marks Wuhan lockdown, which is two days before the 2020 Chinese New Year and exactly precedes the free fall of within-city travels in Hubei. The index dropped by more than half within a three-day window and remained low for six weeks, only to pick up recently until the mid-March. The indices outside Hubei were picking up more rapidly and have almost reached the level in early January.

The movement of people across Chinese cities was more severely affected, as shown in Figure 4. The travels to/from cities in Hubei were nearly frozen. The cross-city travels that do not involve Hubei cities also experienced sharp declines, though to a lesser extent than those involving Hubei cities. In mid-March, the cross-city travels outside Hubei have fully recovered to its early January level.

In sum, the economic impact of lockdown on China is large, severe, and perhaps still mounting despite various massive economic and financial policies that are rolled out by top authorities in Beijing in a timely fashion[1]. China is facing a daunting challenge for its economic recovery at this point, especially because the deteriorating pandemic situation across the globe is bringing an almost complete halt to the export sector in China, and could make it difficult for Chinese firms to access critical inputs provided by firms outside of China.

[1] View related white paper, “Dealing with a Liquidity Crisis: Economic and Financial Policies in China during the Coronavirus Outbreak.”

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.[1] 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.

[1] 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.

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.

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.

[1]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.

[2] 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.

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)[1] 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.

[1]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.

Most unemployed workers in the United States do not usually receive unemployment insurance (UI). In 2019, only 1 in 4 unemployed workers received UI benefits, because of eligibility rules and barriers to program participation. In normal times, receipt of UI benefits requires: 1) proof that the worker was laid off because of changes in labor demand (“good cause”), 2) proof that the worker is searching for a job, and (3) a sufficient work history. In addition, there are usually several administrative hurdles that laid-off workers need to leap to claim benefits.

Although these requirements lead to low UI recipiency throughout the US, some states’ UI systems are particularly ill-equipped to address the coming increase in layoffs. In North Carolina, for example, only 1 in 10 unemployed workers receives UI benefits. However, no state is well-prepared. Even in the states that are doing relatively well, like Pennsylvania, fewer than 1 in 2 unemployed workers receive UI benefits.

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.

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.

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.