As people and economies around the world reel from the impact of the novel coronavirus (COVID-19), one thing is clear: facts are at a premium. The value of trusted data has never been more in evidence than in the months since the onset of COVID-19 in China at the end of 2019, and its rapid spread around the world.

I have been struck time and time again by how much my colleagues want to contribute to finding solutions to the COVID-19 crisis. Yet, we are not qualified to develop a vaccine or to treat those who are suffering. However, economists at the University of Chicago, with their grounding in rigorous research and commitment to public policy, are uniquely positioned to offer insights into the ongoing economic challenges occasioned by this historic health crisis.

So, we decided that what BFI could contribute is a set of facts about COVID-19 that we believe can help people better understand its consequences and potential policy responses. Specifically, we aim to deliver key economic insights that are often missing from policy discussions. The economic implications of COVID-19 are significant and varied, and we address a range of questions: What is the economic benefit of social distancing? What would the impacts of universal testing for COVID-19 be for mortality rates and economic outcomes? Which sectors will be hardest hit? What do the latest stock market gyrations tell us about the expectations for growth? What can China teach us about the economic implications of widescale lockdowns? The answers to these and other important questions are addressed in the following selected facts.

This is a dynamic effort. And in this signal social and economic period, BFI will continue to develop, update, and communicate facts as part of our contribution to minimizing COVID-19’s harm to people and society.

Please visit this page regularly for updates.

Michael Greenstone
Director of the Becker Friedman Institute for Economics
Milton Friedman Distinguished Service Professor of Economics

Public Health & the Economy

  • Does Social Distancing Matter?

    By reducing coronavirus deaths over the next six months, social distancing is projected to increase the well-being of Americans by more than $8 trillion.

    Michael Greenstone, Vishan Nigam


    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

  • Coronavirus Testing Can Relieve Severity of Quarantine Measures

    Universal testing, combined with more targeted quarantine measures for positive cases, could reduce the economic impacts of the COVID-19 pandemic.

    Simon Mongey


    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.

  • Surge Capacity of the US Healthcare Workforce

    By increasing workloads to 60 hours per week, the US healthcare workforce can provide 60% more care from nurses and respiratory therapists, but only 16% more care from physicians.

    Joshua Gottlieb, Neale Mahoney

    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.
    [6] E.g., and
  • Risk Perception and Politics in the Time of COVID-19

    Areas with higher Trump vote shares perceived less risk to COVID-19 early in the pandemic, and practiced less social distancing.

    John Barrios, Yael V. Hochberg


    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.

Global Economy

  • Preliminary Estimates of Economic Effect of Lockdown in China

    Pulling together official statistics with other datasets offers early insights into the effect of COVID-19 on China’s economy.

    Zhiguo He, Chang-Tai Hsieh, Zheng Michael Song


    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.”
  • Tracking the Economy in Real Time with Electricity Data

    Power demand in Italy has fallen 28% so far. Such data can be a real-time indicator of coming economic damage.

    Steve Cicala


    With Americans largely self-isolating amid concerns about COVID-19, some of the hardest-hit areas are already seeing electricity demand begin to weaken. It is useful to review what has happened to power demand in Italy, which some say is about 11 days ahead of the US trajectory of the virus. Compiling regional grid data and adjusting for weather changes reveals that power demand has plunged in Italy since the middle of February.

    On Friday, February 21, life was largely going about as normal in Italy. The following day, the Italian government began to institute quarantine measures. By Monday, power demand began to slow. Since a national lock-down on March 10, national power demand had fallen over 28% compared to demand just prior to the quarantine measures.

    Power demand could be a real-time indicator of the more widespread impacts on the Italian economy. Also, what is happening in Italy could point to what the United States could expect in the coming weeks as states issue tighter restrictions on daily life. When there is a sharp shock to the economy, other indicators like employment may lag in reflecting the impact. This is because laying off workers is often seen as a last resort as companies start by taking other measures like ramping down production or adjusting maintenance schedules. Conversely, electricity demand shows the more immediate change and is a broad measure of economic activity. This was on display during the last recession in the United States. US power demand began to fall a month before the official start date of the recession according to the National Bureau of Economic Research—a date that was determined after an additional year of data had been collected. As policymakers today are considering which countermeasures may be in order to buffer the economic effects of coronavirus, a real-time indicator of the economy’s strength is of the utmost importance.

US Financial Markets

  • Coronavirus: Impact on Stock Prices and Growth Expectations

    As of March 18, our forecast of annual growth in dividends is down 28% in the US and 25% in the EU, and our forecast of GDP growth is down by 2.6% both in the US and in the EU.

    Ralph Koijen, Niels Gormsen


    We 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 March 18, our forecast of annual growth in dividends is down 28% in the US and 25% in the EU, and our forecast of GDP growth is down by 2.6% both in the US and in the EU. As a word of caution, we 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 -43% in the US and -50% in the EU on the 3-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 13 appear to have increased stock prices by lowering risk aversion and lifting long-term growth expectations, but did little to improve expectations about short-term growth. The events on March 16 and March 18 reflect a deterioration of expected growth in the US and predict a deepening of the economic downturn. We also show how data on dividend futures can be used to understand why stock markets fell so sharply, well beyond changes in growth expectations.

  • The Unprecedented Stock Market Reaction to COVID-19

    Current US stock market volatility is by far the highest disease-related reaction since 1900, with news about COVID-19, including policy responses, the likely driver.

    Steven J. Davis


    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.
  • Tracking the Treasury Yield Curve

    The demand for short-term liquidity in the face of uncertainty is, in large part, driving the prices and the yield curve of Treasuries.

    Zhiguo He, Zhaogang Song

    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.

Impact on US Firms

  • US Firms Foresee Huge Sales Hit from Coronavirus

    Coronavirus developments cut expected sales revenue by more than 6% in 2020.

    Steven J. Davis


    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.
  • The Economic Cost of Closing Non-essential Businesses

    Expected GDP losses for Q2 2020 are massive, but they nonetheless likely understate the true costs to households and businesses, which could reach nearly $10,000 per household per quarter.

    Casey Mulligan


    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.

  • Overhead Costs for Private Firms

    Overhead costs are an important consideration for developing post-coronavirus policies for protecting Main Street.

    Eric Zwick


    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.
    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.
  • Major Changes to US Household Spending Due to COVID-19

    US Households sharply increased spending as pandemic spread.

    Constantine Yannelis

    In a new study, the authors use de-identified data from a non-profit Fintech to study how US household spending responded to the COVID-19 crisis. Households dramatically changed their spending as COVID-19 spread. As cases began to spread in late February, spending increased sharply, indicative of households stockpiling goods in anticipation of a higher level of home-production, an inability to visit retailers, or shortages. Total spending rose by approximately half between February 26 and March 11, when a national emergency was declared and as cases grew throughout the country. There is also an increase in credit card spending, which could indicate borrowing to stockpile goods.  Between the imposition of a national emergency and many states and cities issuing shelter-in-place orders starting on March 17, there are elevated levels of grocery spending. These patterns continue through the month of March.

    The authors use the rich dataset to characterize heterogeneity across spending categories, demographics, income groups and partisan affiliation. There are very sharp drops in restaurants, retail, air travel, and public transport in mid to late March. The decrease in spending was not consistent across all categories, e.g., grocery spending increased, as did food deliveries. Despite increases in some categories, total spending dropped by approximately 50%.

    Men stockpile slightly less, and families with children stockpile more than other households. Younger households stockpile later than other households. There is little heterogeneity across income—although our sample is skewed toward lower income individuals. Cell phone records indicate differences in social distancing between political groups—individuals in states with more Trump voters were much more likely to move around in mid and late March. Republicans stockpiled more than Democrats, purchasing more on groceries in late February and early March. Republicans were spending more in retail shops and at restaurants in late March, which may reflect differences in beliefs about the epidemic’s threat, or differential risk exposure to the virus.

US Labor Market

  • How Many Jobs Can Be Done at Home?

    37% of US jobs can be performed at home, accounting for 46% of all wages.

    Jonathan Dingel, Brent Neiman


    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).
  • Who are the Workers Who Cannot Work from Home?

    Those who can’t work from home are disproportionately nonwhite, lack a college degree, and have no health insurance, among other characteristics suggesting economic vulnerability.

    Simon Mongey


    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?”
  • Shutdown Sectors Represent Large Share of All US Employment

    Many sectors have largely shut down for public health reasons. These sectors are a large share of all US employment.

    Joseph S. Vavra


    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.
  • Most Unemployed Workers in the US Do Not Receive Unemployment Insurance

    In a typical month, only 1 in 4 unemployed workers receives unemployment insurance.

    Peter Ganong, Pascal Noel


    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.

  • Unemployment Insurance Claims Sky-Rocket

    New unemployment insurance (UI) claims for the week ending March 21 total 3,283,000. This corresponds to the cumulative total of new UI claims over the first 6 months of the Great Recession.

    Simon Mongey


    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.

  • Gig and Self-Employed Workers Now Eligible for Unemployment Insurance

    States on the front lines will likely face challenges in determining eligibility and processing benefits for gig workers.

    Dmitri Koustas

    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.

Policy Proposals