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.