#FrontierFindings

Key Economic Findings from UChicago Research

Summary analysis of the latest research from UChicago scholars, complementing the BFI Working Paper series that draws from more than 200 economists on campus.

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  • April 12, 2022
    Pandemic-Era Uncertainty
    Main Street uncertainty aligned with Wall Street in reaction to the onset of the COVID-19 pandemic and over the ensuing two years; also, uncertainty remains quite elevated as of March 2022, though down significantly since the spring of 2020.
    David Altig, Jose Maria Barrero, Nicholas Bloom, Steven J. Davis, Brent Meyer, and Emil Mihaylov

    Economic uncertainty rose to record levels in the wake of the COVID-19 pandemic in the United States, fueled by concerns over the direct impact of the virus and the public policy response. Many uncertainty measures remain elevated relative to their pre-pandemic levels, even as the economy has recovered.

    The authors examine the evolution of several uncertainty measures that are both forward-looking and available in near real-time. Their analysis benefits from real-time measures that supplement traditional macro indicators, which become available with lags of weeks or months. Forward-looking uncertainty measures gleaned from business decision makers prove especially useful for assessing prospective responses to a pandemic shock or other fast-moving developments.

    In brief, the authors find the following:

    • Equity market traders and executives at nonfinancial firms have shared similar assessments about uncertainty at one-year look-ahead horizons. Put another way, the authors find that, contrary to the message in the popular press, they see little disconnect between “Main Street” and “Wall Street” views. 
    • The 1-month VIX (an index designed to show future market volatility), the Twitter-based Economic Uncertainty Index, and macro forecaster disagreement all rose sharply at the onset of the pandemic but retrenched almost completely by mid-2021. Thus, these measures exhibit a somewhat different time pattern than the one-year VIX and the authors’ survey-based measure of business-level uncertainty.
    • The newspaper-based Economic Policy Uncertainty Index shows that much of the initial pandemic-related surge in uncertainty reflected concerns around healthcare policy, which moderated post-vaccines, as well as fiscal policy and regulation. Rising inflation concerns and Russia’s invasion of Ukraine became important sources of uncertainty by 2022.
    • An analysis of the Survey of Business Uncertainty (SBU)1 reveals that firm-level risk perceptions shifted sharply to the upside beginning in the summer and fall of 2020 and continuing through March 2022, revealing that decision makers in nonfinancial businesses share some of the optimism that seems manifest in equity markets over this time. 
    • Special SBU questions reveal that recently high uncertainty levels are exerting only a mild restraint on capital investment plans for 2022 and 2023. This finding differs from earlier in the pandemic, when first-moment revenue expectations were softer and downside risks still loomed large. 

    The authors note that these and other results illustrate the value of business surveys like the SBU that directly elicit own-firm forecast distributions and self-assessed effects of uncertainties on investment and other outcomes of interest. 


    1 In partnership with Steven J. Davis of Chicago Booth and Nicholas Bloom of Stanford, the Federal Reserve Bank of Atlanta developed the Atlanta Fed/Chicago Booth/Stanford Survey of Business Uncertainty (SBU), a panel survey that measures one-year-ahead expectations and uncertainties that firms have about their own employment and sales. (atlantafed.org/research/surveys/business-uncertainty)

  • February 9, 2022
    Lethal Unemployment Bonuses? Substitution And Income Effects on Substance Abuse, 2020-21
    Most of the amount, timing, and composition of the 240,000 deaths involving alcohol and drugs since early 2020 can be explained by income effects and category-specific price changes.
    Casey Mulligan

    By 2016, the United States had surpassed 100,000 deaths annually from alcohol- or drug-induced causes, with more than 90 percent of the deaths occurring among the nonelderly, and these levels increased in 2020 and at least through mid 2021, up to about 30 percent over trend. This paper investigates whether changes in regulatory and government spending policies, especially including increases in unemployment insurance (UI) payments, affected drug and alcohol mortality rates. 

    Mulligan constructs a model that documents changes in disposable income, marginal money prices of drugs and alcohol, and the full price of (especially) drugs as it relates to the value of time. In other words, if we assume that people’s preference for drugs and/or alcohol stays the same, their demand for such products would vary with, say, variations in income, price, and other demand factors. Mulligan’s model incorporates this insight to investigate whether and how demand factors vary over time, across substances, and across demographic groups, and then makes predictions on the timing and magnitude of mortality changes by substance. This novel model yields the following findings:

    • Unlike suicide deaths, alcohol-induced deaths and deaths involving drug poisoning in the United States during the pandemic were each above prior trends. The increase in drug deaths lagged acute alcohol deaths by a month.  As before the pandemic, these deaths primarily involved alcohol, opioids, or crystal methamphetamine (meth). 
    • Drug deaths between April 2020 and June 2021 were about 11,000, corresponding to more than 400,000 life years lost, above trend due to the substitution effects of unemployment bonuses. 
    • Substitution to home alcohol consumption explains another 7,300 deaths corresponding to more than 200,000 life years. 
    • Moderate income effects of stimulus checks, rent moratorium and unemployment bonuses (less than one percent spent on opioids or meth) explain another 20,000 alcohol and drug deaths or about 750,000 life years.

    Importantly, these findings do not contradict or confirm observations that the pandemic elevated feelings of depression and anxiety. However, these results do challenge the thesis that alcohol and especially drug mortality during the pandemic were primarily driven by new feelings of depression or loneliness. Suicide did not increase in the United States, while drug mortality fell sharply in the months between the $600 and $300 unemployment bonuses.  To the extent that pandemic depression and loneliness initiated new drug and alcohol habits, they might not yet be reflected in the mortality data but will elevate mortality in the years ahead.

    Mulligan stresses that there are many outstanding questions about drug markets during the pandemic that demand attention, and that research into other countries and markets could bring useful insight. Also, future research may show that the theoretical approach of this research yields results more in line with coincidence than predictability. Even so, if the income and substitution effects described in this work are not important factors, then researchers are left with profound puzzles, including: Why do overall alcohol and drug deaths increase significantly while suicides and fatal heroin overdoses decrease? Why do deaths involving psychotropic drugs (especially meth) increase in lesser proportions than both alcohol and narcotics deaths, even while some important narcotics categories do not increase? And why do mortality rates change across age groups? 

  • February 2, 2022
    State-Level Economic Policy Uncertainty
    Economic policy uncertainty in US states rises around gubernatorial and presidential elections, as well as around state-specific and national events; also, the COVID-19 pandemic drove huge increases in policy uncertainty and unemployment, more so in states with stricter government-mandated lockdowns.
    Scott R. Baker, Steven J. Davis, and Jeffrey A. Levy

    High economic policy uncertainty (EPU) can depress economic activity by causing firms to defer certain investments, by raising credit spreads and risk premiums (thereby dampening business investment and hiring), and by prompting consumers to postpone purchases of durable goods. While several studies provide evidence that uncertainty increases around elections and that election-related uncertainty has material effects on economic activity, this new paper provides the first evidence on the relative importance of state and national sources of state-level policy uncertainty, how these sources differ across states, and how they vary over time within states.

    The authors employ the digital archives of nearly 3,500 local newspapers to construct three monthly indexes of economic policy uncertainty for each state: one that captures state and local sources of policy uncertainty (EPU-S), another that captures national and international sources (EPU-N), and a composite index (EPU-C) that captures both state + local and national + international sources. Half the articles that feed into their composite indexes discuss state and local policy, confirming that sub-national matters are important sources of policy uncertainty. Key findings include:

    • EPU-S rises around presidential and own-state gubernatorial elections and in response to own-state episodes such as the California electricity crisis of 2000-01 and the Kansas tax experiment of 2012. 
    • EPU-N rises around presidential elections and in response to such shocks as the 9-11 terrorist attacks, the July 2011 debt-ceiling crisis, federal government shutdowns, and other “national” events. 
    • Close elections (winning vote margin under 4 percent) elevate policy uncertainty much more than less competitive elections; a close presidential election contest raises EPU-N by 60 percent and a close gubernatorial contest raises EPU-S by 35 percent. 
    • EPU spiked in the wake of the COVID-19 pandemic, pushing EPU-N to 2.7 times its pre-COVID peak, and (average) EPU-S to more than four times its previous peak. Policy uncertainty rose more sharply in states with stricter government-mandated lockdowns.
    • Upward shocks to own-state policy uncertainty foreshadow higher unemployment in the state.

    This research also finds that the main locus of policy uncertainty shifted to state and local sources during the pandemic. The authors offer the following simple metric: Consider the ratio of EPU-S to EPU-N for a given state. The cross-state average value of this ratio rose from 0.65 in the pre-pandemic years to 1.1 in the period from March 2020 to June 2021. Since the timing, stringency, and duration of gathering restrictions, school closure orders, business closure orders, and shelter-in-place orders during the pandemic were largely set by state and local authorities, it makes sense that EPU-S saw an especially large increase after February 2020.

  • January 31, 2022
    Vaccine Allocation Priorities Using Disease Surveillance and Economic Data
    It may be less cost-effective to vaccinate—and thus to procure doses for—only a subset of a population in countries where the rate of vaccination is low, because infection will spread to more people, reducing the incremental value of vaccination; that is, the social value of vaccination and the optimum number of doses to purchase rise with the rate of vaccination.
    Anup Malani, Satej Soman, Sabareesh Ramachandran, Alice Chen, and Darius Lakdawalla

    The COVID-19 pandemic has infected over 250 million and killed at least 5 million worldwide. Nearly two years into the crisis, many countries, such as India, have experienced second waves with infection levels greater than the initial wave, and now face a potential third wave from the Omicron variant that is larger still. Despite widespread availability in some countries, many others still face shortages, raising an important question: What vaccine allocation plan maximizes the health and economic benefits from vaccination?

    Prior analyses of optimal vaccine allocation typically begin with a model of disease, then simulate or forecast the effect of various vaccine allocation plans, and finally compare plans based on certain metrics. The authors cite numerous studies that incorporate various features, from prioritization of elderly populations, to accounting for deaths averted and years of life saved, among other factors. This research builds on those prior evaluations of vaccine allocation in three important respects: it includes novel epidemiological data from a low-to-middle income country, India; it incorporates a robust economic valuation of vaccination plans based on willingness to pay for longevity; and—more importantly—it employs a model for social demand for vaccination that can guide governments’ vaccine procurement decisions. 

    Among other findings, this work reveals the following:

    • Allocation matters. In countries such as India, with large populations and vaccine shortages, it matters who gets the vaccine first. Mortality-rate based prioritization may save a million more lives and 10 million more life-years.
    • The social value of vaccination and the optimum number of doses to purchase rise with the rate of vaccination.  It may be cost-effective to vaccinate—and thus to procure doses for—only a subset of the population if the rate of vaccination is low because vaccination campaigns are in a race against the epidemic. Slower vaccination means more people obtain immunity from infection, reducing the incremental protection from—and thus the social value of—vaccination.  
    • However, if the cost of speeding up vaccination is the inability to prioritize, it may be prudent in countries like India, for example, to choose a slower but mortality-rate prioritized vaccination plan. Vaccinating just 25% of the population in a year using mortality-rate prioritization saves more lives and life-years than vaccinating even 100% of the population in 6 months using random allocation. Protecting a small number of the elderly eliminates much of the remaining mortality risk from COVID-19 in India.
    • A substantial portion of the social value from vaccination comes from improvement in consumption when vaccination reduces cases and permits greater economic activity.  

    This paper presents tools that can provide actionable policy advice, with estimates to help governments select optimal vaccination plans on a range of metrics. Importantly, these metrics consider economic factors that influence politicians, even though they may not be what the public health community recommends. Most importantly, these estimates recommend how many doses would be cost effective for governments to procure at different levels of vaccine efficacy and price.

  • January 25, 2022
    The Expected, Perceived, and Realized Inflation of U.S. Households Before and During the Covid19 Pandemic
    In normal times, realized inflation barely differs across observable dimensions; however, low income, low education, and Black households experienced a larger increase in realized inflation during the pandemic than other households did.
    Olivier Coibion, Yuriy Gorodnichenko, and Michael Weber

    When the COVID19 pandemic spread across the United States and households were confronted with empty store shelves of common products like toilet paper and cleansers, they asked themselves questions that also confronted policymakers and researchers: Were those shortages a result of panicked buying, in which case they could wait for an increase in supply to quickly materialize, or from reduced production by manufacturers due to lockdowns or workers staying at home, in which case the shortage could be long-lived.

    Strikingly, the average inflation expectations of households rose, consistent with a supply-side interpretation, but disagreement among households about the inflation outlook also increased sharply. What was behind this pervasive disagreement? Did households, like economists, disagree about whether the shock was a supply or a demand one? Or did they receive different signals about the severity of the shock due, for example, to the specific prices they faced in their regular shopping and heterogeneity in their shopping bundles? The answers to these questions can shed light not just on the pandemic period but more generally on the nature of household expectations, the degree of anchoring in inflation expectations, and the current inflation outlook as post-pandemic inflation rates spike.

    To address these questions, the authors combine large-scale surveys of US households with detailed information on their spending patterns. Spending data allow the authors to observe in detail the price patterns faced by individual consumers and thereby characterize what inflation rate households experienced in their regular shopping. The researchers can then measure households’ perceptions about broader price movements and economic activity as well as their expectations for the future. Jointly, these data permit the authors to characterize the extent to which the specific price changes faced by consumers in their daily lives shaped their economic expectations during this unusual time. 

    Using both the realized and perceived levels of inflation by households, the authors find the following:

    • Pervasive disagreement about the inflation outlook stems primarily from the disparate consumer experiences with prices during this period. The early months of the pandemic were characterized by divergent price dynamics across sectors, leading to significant disparities in the inflation experiences of households. 
    • Perceptions of broader price movements diverged even more widely across households, leading to very different inferences about the severity of the shock. These differences in perceived inflation changes were passed through not just into households’ inflation outlooks but also to their expectations of future unemployment. 
    • Finally, the widespread interpretation of the pandemic as a supply shock by households led those who perceived higher inflation during this period to anticipate both higher inflation and unemployment in subsequent periods.

    The authors stress that these findings raise important implications for current and future policymaking. While the magnitude of the rise in disagreement was notable, the supply side interpretation of the shock by households was not. Instead, it was consistent with a more systematic view taken by households that high inflation is associated with worse economic outcomes. This view is likely not innocuous for macroeconomic outcomes. Since policies like forward guidance are meant to operate in part by raising inflation expectations, this type of supply-side interpretation by households is likely to lead to weaker effects from these policies as households reduce, rather than increase, their purchases when anticipating future price increases.

    Further, as inflation expectations rose through 2021 and into 2022, households became more pessimistic about the economic outlook even as wages and employment rose sharply. This pessimism about the outlook creates a downside risk for the recovery and suggests that policymakers should be wary of removing supportive measures too rapidly. Patience in waiting for supply constraints to loosen therefore seems warranted since pre-emptive contractionary policies would likely amplify the pessimism that risks throttling the recovery from the pandemic.

  • November 16, 2021
    COVID Uncertainty: A Tale of Two Tails
    The nature of firm-level uncertainty about future growth rates has shifted greatly since the pandemic struck: Initially, business executives perceived an enormous increase in downside uncertainty, but as of October 2021, almost all the extra firm-level uncertainty is to the upside.
    Philip Bunn, David Altig, Lena Anayi, Jose Maria Barrero, Nicholas Bloom, Steven J. Davis, Brent Meyer, Emil Mihaylov, Paul Mizen, and Greg Thwaites

    The authors employ two monthly panel surveys of business executives in the US (about 500 monthly responses) and UK (roughly 3,000) to ask about sales growth at their firms over the past year and for sales forecasts over the next year. Importantly, the forecast questions elicit data for five scenarios—a growth rate in each of the lowest, low, medium, high, and highest sales growth scenarios and the probabilities of each scenario. Thus, the surveys yield a 5-point subjective forecast distribution over one-year-ahead sales growth rates for each firm.

    The surveys reveal that the COVID shock pushed average uncertainty among US firms from about 3% before the pandemic to 6.4% in May 2021. Uncertainty fell back to about 4.5% in October 2021. Data for UK firms tell a similar story: Firm-level uncertainty rose from about 4.9% before the pandemic to 8.5% in April 2021 and has since declined to about 6.8%. [The remainder of this Finding is concerned with US survey results; the UK results are very similar, as described in the full paper.]

    The US distribution of realized growth rates widened greatly in the wake of the pandemic, as shown in the left panel of the accompanying Figure. Initially, the widening occurred mostly in the lower half of the distribution. For example, the 10th percentile of realized growth rates fell from about -5% in late 2019 to a trough of -35% in May 2020. The 25th percentile shows the same pattern in somewhat muted form. In contrast, growth rates at the 75th and 90th percentiles fell by about 3 percentage points from late 2019 to May 2020. By the summer of 2021, though, the lower tail of the realized growth rate distribution had recovered to pre-pandemic values, while growth rates at the 75th and 90th percentiles had greatly surpassed their pre-pandemic values.

    The average subjective forecast distribution over firm-level growth rates in the year ahead shows a similar pattern, as seen in the right panel of the Figure, which captures both average uncertainty in sales growth rate forecasts at the firm level and whether that uncertainty is mainly to the upside, mainly to the downside, or evenly balanced between the two.

    When the pandemic took hold in March 2020, firms perceived a large increase in downside uncertainty, placing much greater weight on the possibility of highly negative growth rates. While the 90th and 75th percentiles of the forecast distribution changed little, the median fell by about 5 percentage points and the 25th and 10th percentiles fell by 20 and 40 percentage points, respectively. In short, the average firm saw dramatically more downside risk in year-ahead sales growth rates during the early months of the pandemic.

    As the pandemic continued, downside risks abated greatly. By early 2021, the forecast distribution remained highly dispersed (i.e., subjective uncertainty remained high), but it increasingly reflected upside rather than downside risk. In recent months, firm-level subjective uncertainty is mainly about prospects for rapid sales growth over the coming year and only secondarily about the possibility of sharp contractions.

    In broad summary: The early months of the pandemic involved a negative first-moment shock, a positive second-moment shock, and a negative third-moment or skewness shock; that is, the pandemic drove a large drop in the first moment of the economic outlook and much higher uncertainty in the form of highly elevated downside risks.

    Looking ahead, the authors suggest that uncertainty may revert to pre-pandemic levels as COVID case numbers and deaths fall, social distancing subsides, and policy stimulus fades out. Indeed, many firms see tantalizing possibilities to the upside. Nevertheless, there are significant risks to recovery from ongoing supply-chain disruptions, inflationary pressures, low vaccination rates in many countries, and the potential for new SARS-CoV-2 variants.

  • July 28, 2021
    Internet Access and its Implications for Productivity, Inequality, and Resilience
    Moving to high-quality, fully reliable home internet service for all Americans would raise earnings-weighted labor productivity by an estimated 1.1% in the coming years, with implied output gains of $160 billion per year, or $4 trillion when capitalized at a 4% rate.
    Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis

    Recent work by Barrero, Bloom, and Davis revealed that working from home, a phenomenon that rose to ten times pre-COVID levels in spring 2020, will endure post-pandemic (see “Why Working From Home Will Stick” for the Economic Finding and a link to the working paper). The ability to work from home (WFH), and the quality of such work, is influenced by the quality of internet service, and in this paper the authors explore the impact of internet service on previous and likely future WFH experience, earnings inequality, and the psychological benefits of video conferencing in times of social distancing, among other issues.

    To address these questions, the authors tap multiple waves of data from the Survey of Working Arrangements and Attitudes1 (SWAA), an original cross-sectional survey, fielded monthly since May 2020, and thus far collecting 43,000 responses from working-age Americans who earned at least $20,000 in 2019. The survey asks about working arrangements during the pandemic, internet access quality, productivity, subjective well-being, employer plans about the extent of WFH after the pandemic ends, and more. The SWAA measure of working from home does not encompass workdays split between home and office or work at satellite business facilities.

    In their earlier work, the authors estimated that a re-optimization of working arrangements in the post-pandemic economy would boost productivity by 4.6% relative to pre-pandemic levels, mainly attributable to savings in commuting time. This boost reflects a combination of higher productivity when WFH for some workers and the selected nature of who works from home in the post-pandemic economy.

    However, what would happen if everyone had access to high-quality internet service? This new work approaches this question by asking people directly about the effect that such service would have on their productivity. The authors also employed regression models that relate SWAA data on the relative productivity of WFH to internet access quality. Under both approaches, they exploit SWAA data on employer plans for who will work from home in the post-pandemic economy, and how much. Their findings include:

    • Moving to high-quality, fully reliable home internet service for all Americans (“universal access”) would raise earnings-weighted labor productivity by an estimated 1.1% in coming years.
    • The implied output gains are $160 billion per year, or $4 trillion when capitalized at a 4% rate. Estimated flow output payoffs to universal access are nearly three times as large in COVID-like disaster states, when many more people work from home.
    • Better home internet access increases the propensity to work from home. Universal access would raise the extent of WFH in the post-pandemic economy by an estimated 0.7 percentage points, which slightly raises the authors’ estimate for the earnings-weighted productivity benefits of moving to universal access.
    • Better home internet service during the pandemic is also associated with greater subjective well-being, conditional on employment status, working arrangements, and other controls.
    • While intuition suggests that improving internet access for lower-income workers would reduce inequality, the authors find that planned levels of WFH in the post-pandemic economy rise strongly with earnings. This effect cuts the other way. On net, they find that universal access would be of little consequence for overall earnings inequality and for the distribution of average earnings across major demographic groups.

    The authors stress that the desirability of moving part or all the way to universal access depends on the costs as well as the benefits. Also, this work reveals the extra economic and social benefits of universal access during the pandemic and underscores its resilience value in the face of disasters that inhibit travel and in-person interactions—an important but understudied topic.

    This paper was prepared for the Economic Strategy Group at the Aspen Institute.


    1See wfhresearch.com

  • June 10, 2021
    Science Skepticism Reduces Compliance with COVID-19 Shelter-in-Place Policies
    The proportion of people who stay at home after shelter-in-place policies go into effect is significantly lower in counties with a high concentration of science skeptics.
    Adam Brzezinski, Valentin Kecht, David Van Dijcke, and Austin Wright

    It follows that if physical distancing reduces interpersonal transmission risks related to the COVID-19 virus, then government policies that mandate physical distancing should slow the spread of COVID-19. Further, local non-compliance with such shelter-in-place orders would create public health risks and could cause regional spread. Given this, it is important that policymakers understand which local factors impact compliance with public health directives. 

    Recent research highlights several factors that influence compliance, including partisanship, political polarization, poverty and economic dislocation, and differences in risk perception, all of which influence physical distancing in the absence of government mandates. This new research highlights the role of science skepticism and attitudes regarding topics of scientific consensus in shaping patterns of physical distancing.

    To examine the role of science skepticism, the authors leverage the most granular, representative data on science skepticism in the United States—beliefs about the anthropogenic (human) causes of global warming—to study how physical distancing patterns vary with skepticism toward science. The authors combine this county-level science skepticism measure with location trace data on the movement of around 40 million mobile devices as well as data on state-level shelter-in-place policies, to find the following:

    • Science skepticism is likely an important determinant of local compliance with government shelter-in-place policies, even after accounting for the role of partisanship, population density, education, and income, among other factors. 
    • Shelter-in-place policies increase the proportion of devices that stay at home by 2 p.p. (p-value < 0.001) more in counties with low levels of science skepticism compared to counties with high levels of skepticism. This corresponds to an 8% increase in devices that stayed at home, compared to the February average of 25%.

    The authors also benchmark their measure of science skepticism against other measures of belief in science available at the state-level to show that their measure captures a more general notion of skepticism toward topics of scientific consensus.

  • May 11, 2021
    Financial Fragility in the COVID-19 Crisis: The Case of Investment Funds in Corporate Bond Markets
    By providing a liquidity backstop for their bond holdings, the Federal Reserve bond purchase program helped to reverse corporate bond fund outflows during the COVID-19 crisis, especially for the most fragile funds.
    Antonio Falato, Itay Goldstein, and Ali Hortaçsu

    In the decade following the financial crisis of 2008, investment funds in corporate bond markets became prominent market players and generated concerns of financial fragility. Figure 1 demonstrates the dramatic growth of their assets under management relative to the size of the corporate bond market since the 2008-2009 crisis. Increased bank regulation has pushed some of the activities from banks to non-bank intermediaries, heightening fears among regulators. Just in 2019, Mark Carney, the governor of the Bank of England, warned that investment funds that include illiquid assets but allow investors to take out their money whenever they like were “built on a lie” and could pose a big risk to the financial sector. However, despite these concerns, the last decade did not feature major stress events to test the resilience of corporate-bond investment funds. Hence, there is a dearth of systematic evidence on their resilience in large-stress events.

    The authors address this gap by analyzing recent events around the COVID-19 crisis, which provide an opportunity to inspect the resilience of these important non-bank financial intermediaries in a major stress event and the unprecedented policy actions that followed it. The COVID-19 crisis unfolded quickly around the world in early 2020. Initial declaration of a public health emergency was made January 31, with reports of confirmed infections intensifying in March. On March 13, a national emergency at the federal level in the United States was declared. Financial markets tumbled as these events took place, with corporate bond markets in particular experiencing severe stress amid major liquidity problems. 

    The Federal Reserve responded aggressively with a March 23 announcement of the Primary Market Corporate Credit Facility (PMCCF) and Secondary Market Corporate Credit Facility (SMCCF), which were designed to purchase $300 billion of investment-grade corporate bonds. On April 9, the Fed announced the expansion of these programs to a total of $850 billion and an extension of coverage to some high-yield bonds. These facilities were unprecedented in the history of the Fed. As such, their announcements had a major impact on corporate-bond markets. Spreads for both investment-grade and high-yield rated corporate bonds, which almost tripled relative to their pre-pandemic level by March 23, reversed after the two policy announcements.

    This recent episode allowed the authors to empirically investigate two important and related questions: How fragile were these corporate bond funds and how effective were the Fed’s actions in contributing to a resolution? Using daily data on flows into and out of mutual funds in corporate bond markets during the crisis allowed the authors to shed light on the determinants of flows across different funds, and thus to better understand the sources of fragility and what actions mitigated that instability. In summary, they highlight three main sources of fragility: asset illiquidity, vulnerability to fire-sales, and sector exposure. 

    The authors then show that the Fed bond purchase program helped to mitigate fragility by providing a liquidity backstop for their bond holdings. In turn, the Fed bond purchase program had spillover effects, stimulating primary market bond issuance by firms whose outstanding bonds were held by the impacted funds, and stabilizing peer funds whose bond holdings overlapped with those of the impacted funds. This analysis uncovers a novel transmission channel of unconventional monetary policy via non-bank financial institutions, which carries important policy lessons for how the Fed bond purchases transmit to the real economy.

    The authors caution that massive Fed intervention in the market will likely not become the norm and, likewise, some of the structural fragilities in the way investment funds operate in illiquid markets must be addressed more directly.

  • May 10, 2021
    Work from Home & Productivity: Evidence from Personnel & Analytics Data on IT Professionals
    While total hours worked increased by roughly 30% during a COVID-induced work-from-home period at a large Asian IT company, average output did not significantly change, and productivity fell by about 20%.
    Michael Gibbs, Friederike Mengel, and Christoph Siemroth

    The Covid-19 pandemic forced a dramatic rush to work from home (WFH) in early 2020. Even if only a fraction of this global shift became permanent, it would have implications for urban design, infrastructure development, and reallocation of investment from inner cities to residential areas. Of course, it would also have significant implications for how businesses organize and manage their workforces.

    There is significant debate about the effectiveness of WFH, including how much further we can improve implementation, and the extent to which firms will continue the practice. Initial experiences led to optimism, but many firms are starting to question the sustainability of extensive WFH. One of the most important questions in this context is how WFH affects productivity. 

    This paper provides an analysis of the effects of the switch to WFH in a large Asian IT services company that abruptly switched all employees to WFH in March 2020. This study has several novel features, including a rich dataset for a sample of more than 10,000 employees for 17 months before and during WFH. The data include information on productivity, hours worked and how that time was allocated, and the employee’s contacts with colleagues inside and outside the firm. In addition, it includes an estimate of the employee’s commute time when they had worked at the office, and how many children (if any) they have at home.

    The key measures are based on relatively objective measures of work time and the employee’s output, which were collected from the firm’s workforce analytics systems. The company has a highly developed process for setting goals and tracking progress, culminating in a primary output measure for each employee. The data also include information on hours worked, the authors’ primary input measure. Productivity is measured as output divided by hours worked. Most prior studies of WFH were based on survey data, so this is an unusual opportunity to study employee performance using the measures that the firm employs.

    These data also include (for a subset of employees) time allocation for various activities, including meetings, collaboration, and time focused on performing work without distractions. It also includes information on networking activities (contacts) with colleagues inside and outside the firm, as well as various employee characteristics. 

    Of note, most employees at this company are highly skilled professionals in an IT company where nearly all are college educated. The jobs involve significant cognitive work, developing new software or hardware applications or solutions, collaborating with teams of professionals, working with clients, and engaging in innovation and continuous improvement. These job characteristics may present significant challenges to effective WFH. By contrast, previous studies of WFH productivity either used self-reported measures of productivity or focused on occupations where workers have relatively simple and repetitive tasks, often follow scripts, and work independently, such as call center workers.

    Finally, the data allowed them to compare outcomes for the same employee before and during WFH. The authors find the following:

    • Employees significantly increased total hours worked, by about 30%, during WFH. Much of this increase came from working outside of normal office hours. 
    • Despite the disruption due to the pandemic and shift to WFH, there was no significant change in measured output (the primary evaluation metric for each employee). In other words, employees continued to meet their goals, which were not changed after the switch to WFH. 
    • Given their results on work time and output, the authors estimate that productivity declined considerably, about 20%. These results are consistent with employees becoming less productive during WFH and working longer hours to compensate.

    Why did productivity decline? The authors find that employees spent more time engaged in various types of formal and informal meetings during WFH, especially video conferences. Likewise, they spent substantially less time working without interruption. They also spent less time networking (both within the firm and with clients), and less time receiving coaching or 1:1 meetings with supervisors. These findings suggest that increased coordination costs during WFH at least partially explain the drop in productivity.

    The authors also found that the productivity of women was more negatively affected by WFH than men. However, this gender difference was not due to the presence of children in the home. Rather, the likely culprit is other demands placed on women in the domestic setting. Employees with children at home increased working hours significantly more than those who did not have children at home, accounting for a greater decrease in productivity. 

    Among other considerations, these and other findings suggest that communication, coordination, and collaboration are hampered under WFH, and employers should not underestimate the value of networking and uninterrupted work time on employee productivity.

  • April 30, 2021
    The Impact of Domestic Travel Bans on COVID-19 is Nonlinear in their Duration
    While very short and long restrictions limit the spread of disease, moderately lengthy restrictions substantially increase infections.
    Fiona Burlig, Anant Sudarshan, and Garrison Schlauch

    Governments around the world have deployed numerous policy instruments to control the spread of COVID-19, with some instruments, such as large-scale lockdowns, causing significant economic harm. These costs have been especially pronounced in developing countries, where economic slowdowns associated with COVID-19 policies combined with weak social safety nets were expected to push between 71–100 million people into extreme poverty in 2020.

    Domestic travel bans are a particularly severe and relatively common restriction. Motivated in part by simulation exercises that model them as effective methods for reducing the spread of disease, they also impose substantial and inequitable economic costs, which make them difficult to sustain indefinitely. As a result, these policy instruments necessarily involve two decisions: (i) whether to restrict freedom of movement and (ii) for how long to do so.

    To examine these decisions, the authors focus on domestic travel bans implemented by developing countries, which are frequently characterized by the presence of large populations of migrant workers. A United Nations report that examines data from 70 countries and more than 70% of the global population found that more than 763 million people were living within their home country but outside their region of birth in 2005. In addition, the rural-to-urban migration most affected by COVID-19 mobility restrictions is more common in developing countries than in the developed world, and the presence of a large population that may respond to economic shocks by moving has motivated many developing countries to utilize travel bans to prevent the spread of disease.

    For this work, the authors estimate the impact of travel ban duration on the spread of COVID-19 by simulating disease transmission using a standard model that mimics a real-world scenario facing many developing countries, in which migrants leaving an urban hotspot spread infections to a rural destination. The results from this modeling exercise generates their key hypothesis: that the impact of travel bans is nonlinear in duration.

    To test this finding empirically, they examine a natural experiment in Mumbai, India—the country’s financial capital and initial COVID-19 epicenter—which relaxed travel bans after varying durations. On March 25th, the country imposed a nationwide lockdown, maintaining a ban on domestic travel out of the city, causing immense suffering as the economy rapidly contracted and unemployment rose, especially among migrant workers, who do not have access to the social safety net in India. Under intense pressure, the government allowed the first wave of migrants to return to homes outside Mumbai’s state of Maharashtra on May 8. Phase 2 migrants, returning to districts in the Mumbai Metropolitan Area, were allowed to leave on June 5, and Phase 3 migrants, departing to all other destinations, were able to leave on August 20.  Finally, the authors used cross-country data to examine travel bans in Indonesia, India, South Africa, the Philippines, China, and Kenya. Together, these countries comprise roughly 40% of the global population.

    The authors’ model and empirical results are in agreement about domestic travel bans: relatively short and relatively long restrictions can successfully limit the spread of COVID-19; however, intermediate length bans—once lifted—can significantly increase COVID-19 growth rates, cumulative infections, and deaths. The full effect of travel bans can therefore only be quantified after they are lifted. More broadly, these results underscore that quantifying the unintended consequences of COVID-19 restrictions, including both disease and economic costs, is critical for policy decisions.

  • April 22, 2021
    Why Working From Home Will Stick
    Surveys reveal that 20 percent of full workdays will be supplied from home after the pandemic ends, compared with just 5 percent before.
    Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis

    COVID-19 triggered a mass social experiment in working from home (WFH). Americans, for example, supplied roughly half of paid work hours from home between April and December 2020, as compared to 5 percent before the pandemic. Will this phenomenon continue after the pandemic ends?

    To answer this question and to gauge other post-pandemic effects, the authors employed multiple waves of data from an original cross-sectional survey design that they have fielded about once a month since May 2020, and which includes 27,500 responses from working-age Americans. Their findings include the following:

    • Employers plan for workers to supply 20.5 percent of full workdays from home after the pandemic ends. Roughly speaking, WFH is feasible for half of employees, and the typical plan for that half involves two workdays per week at home. Business leaders often mention concerns around workplace culture, motivation, and innovation as important reasons to bring workers onsite three or more days per week, while acknowledging net WFH benefits for one or two days per week.
    • Most workers welcome the option to work remotely one or more days per week, according to our data, with respondents willing to accept pay cuts of 8 percent, on average, for the option to work from home two or three days per week after the pandemic. WFH desires are pervasive across groups defined by age, education, gender, earnings, and family circumstances. The actual incidence of WFH rises steeply with education and earnings.
    •  The extent of WFH in the post-pandemic economy is four times its pre-pandemic level, but only two-fifths of its average level during the pandemic. This implies a partial reversal of the massive COVID-induced surge in WFH. The reversal mostly involves adjustments on the intensive margin, whereby many persons WFH five days per week during the pandemic will shift to two or three days per week after it ends.

     These shifts in work patterns will have important consequences. For example, high-income workers, especially, will enjoy large benefits from greater remote work. Also, spending in major city centers will fall by 5-10 percent or more relative to pre-pandemic levels. Finally, the authors’ data on employer plans and the relative productivity of WFH imply a 6 percent productivity boost in the post-pandemic economy due to re-optimized working arrangements. Less than one-fifth of this productivity gain will show up in conventional productivity measures, because they do not capture gains from less commuting.

  • February 4, 2021
    Measuring Movement and Social Contact with Smartphone Data: A Real-Time Application to COVID-19
    The authors develop and make publicly available a location exposure index that summarizes county-to-county movements, and a device exposure index that quantifies social contact within venues.
    Victor Couture, Jonathan Dingel, Allison Green, Jessie Handbury, and Kevin R. Williams

    Personal digital devices generate streams of detailed data about human behavior. Their temporal frequency, geographic precision, and novel content offer social scientists opportunities to investigate new dimensions of economic activity.

    The authors find that smartphone data cover a significant fraction of the US population and are broadly representative of the general population in terms of residential characteristics and movement patterns. They produce a location exposure index (“LEX”) that describes county-to-county movements and a device exposure index (“DEX”) that quantifies the exposure of devices to each other within venues. These indices track the evolution of intercounty travel and social contact from their sudden collapse in spring 2020 through their gradual, heterogeneous rises over the following months.

    Importantly for researchers, the authors are publishing these indices each weekday in a public repository available to noncommercial users for research purposes. Their aim is to reduce entry costs for those using smartphone movement data for pandemic-related research. By creating publicly available indices defined by documented sample-selection criteria, the authors hope to ease the comparison and interpretation of results across studies.

    More broadly, this work provides guidance on potential benefits and relevant caveats when using smartphone movement data for economic research. Researchers in economics and other fields are turning to smartphone movement data to investigate a great variety of social science questions, and the authors focus on the distinctive advantages of the data frequency and immediacy.

  • January 10, 2021
    COVID-19 Is a Persistent Reallocation Shock
    Recent data from the firm-level Survey of Business Uncertainty reveal three pieces of evidence that COVID-19 is a persistent reallocation shock.
    Jose Maria Barrero, Nicholas Bloom, Steven J. Davis, and Brent Meyer

    COVID-19 and policy responses to the pandemic have generated massive shifts in demand across businesses and industries. The authors draw on firm-level data in the Atlanta Fed/Chicago Booth/Stanford Survey of Business Uncertainty (SBU)1 to quantify the pace of reallocation across firms before and after the pandemic struck, to investigate what firm-level forecasts in December 2020 say about expected future sales, and to examine how industry-level employment trends relate to the capacity of employees to work from home.

    The authors report three pieces of evidence on the persistent re-allocative effects of the COVID-19 shock:

    • First, rates of excess job and sales reallocation over 24-month periods have risen sharply since the pandemic struck, especially for sales. The authors focus on rates of “excess” reallocation, which adjust for net changes in aggregate activity.
    • Second, as of December 2020, firm-level forecasts of sales revenue growth over the next year imply a continuation of recent changes, not a reversal. Firms hit most negatively during the pandemic expect (on average) to continue shrinking in 2021, and firms hit positively expect to continue growing.
    • Third, COVID-19 shifted relative employment growth trends in favor of industries with a high capacity of employees to work from home, and against industries with a low capacity.


    1 The SBU is a monthly panel survey of U.S. business executives that collects data on own-firm past, current, and expected future sales and employment. The Atlanta Fed recruits high-level executives to join the panel and sends them the survey via email, obtaining about 450 responses per month. The survey yields data on realized firm-level employment and sales growth rates over the preceding twelve months and subjective forecast distributions over own-firm growth rates at a one-year look-ahead horizon.

  • December 2, 2020
    Why Working From Home Will Stick
    Nearly one-quarter (22%) of full workdays will be supplied from home in the United States after the pandemic, compared with just 5% before, and productivity will improve.
    Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis

    One of the looming pandemic-related questions for the US economy is to what degree workers will remain working from home when the pandemic ends. By some estimates, roughly half of all work occurred at home, either in whole or in part, through October 2020. Crucial to this question is not only whether workers can work from home, but whether they should. Put another way, does worker productivity suffer when it occurs at home? 

    The authors surveyed 15,000 working-age Americans between May and October 2020 in waves, and the authors’ analysis of those responses reveals the following five reasons why working from home will likely stick:

    1. Reduced stigma. Most respondents report perceptions about working from home have improved among people they know. 
    2. Employer learning. The pandemic forced workers and firms to experiment with working from home en masse, enabling them to learn how well it actually works. 
    3. New investment. The average worker invested over 13 hours and about $660 dollars in equipment and infrastructure to facilitate working from home, amounting to 1.2% of GDP. In addition, firms made sizable investments in back-end information technologies and equipment to support working from home. 
    4. Lingering fear. About 70% of respondents expressed a reluctance to return to some pre-pandemic activities, even when a vaccine is widely available, for example, riding subways and crowded elevators, or dining indoors at restaurants. 
    5. New technologies. The rate of innovation around technologies that facilitate working from home has likely accelerated.

    Network effects are likely to amplify the impact of these five mechanisms. For example, coordination among several firms will facilitate doing business while their employees are working from home. When several firms are operating partially from home, it lowers the cost for other firms and workers to do the same, creating a positive feedback loop. 

    For dense cities like New York and San Francisco, a pronounced shift to working from home will likely have a negative effect. The authors estimate that worker expenditures on meals, entertainment, and shopping in central business districts will fall by 5% to 10% of taxable sales.

    Finally, many workers reported higher productivity while working from home during the pandemic than previously. Taking the survey responses at face value, accounting for employer plans about who gets to work from home, and aggregating, the authors estimate that worker productivity will be 2.4% higher post-pandemic due to working from home.

  • November 19, 2020
    When Nurses Travel: Labor Supply Elasticity During COVID-19 Surges
    Increased compensation is key to getting nurses to travel long distances to meet demand in other locations. This work suggests that mobility across an integrated national market is helpful for re-allocating workers when demand surges.
    Joshua Gottlieb, and Avi Zenilman

     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. 

  • November 3, 2020
    Stock Prices and Economic Activity in the Time of Coronavirus
    Global stock prices fell 30% from February 17 to March 12, 2020, 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.
    Steven J. Davis, Dingqian Liu, and Xuguang Simon Sheng

    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.

  • October 12, 2020
    Powering Work from Home
    From April to July 2020, American households spent nearly $6 billion in excess residential electricity consumption. Electricity bills were over $20/month higher on average for utilities serving one-fifth of US households.
    Steve Cicala

    To reduce the risk of exposure to the COVID-19 virus, roughly one-third of the American labor force has been working from home. Household expenditures have also changed dramatically, reflecting both the loss of income and consumption opportunities, and a shift toward household production. Additional time and consumption at home requires significant increases in electricity consumption. This represents an additional and essential expense at a time that many households are also experiencing severe economic hardship.

    Using data that provides hourly residential electricity consumption in Texas, along with another dataset that reports monthly consumption of electricity by customer class (residential, commercial, and industrial) for most U.S. utilities, the author found that the increase in residential consumption corresponds with those workers able to work from home. Also, while rising unemployment is strongly associated with commercial and industrial electricity declines, it is weakly associated with residential increases. Non-essential business closures do not have statistically significant impacts on usage beyond the direct potential employment effects.

    Further, the author finds that the increase in residential consumption is not common in economic downturns; for example, it did not occur during the Great Recession. From April to July 2020, American households spent nearly $6 billion in excess residential electricity consumption. Electricity bills were over $20/month higher on average for utilities serving one-fifth of US households. This increased expenditure reduces the net benefits of working from home associated with less commuting and improved environmental quality. As industrial and commercial activity recovers, working from home has the potential to increase emissions from the power sector on net. In the same way that dense cities are more energy efficient than suburbs, it requires more energy to heat and cool entire homes than the offices and schools.

  • September 18, 2020
    60 Million Fewer Commuting Hours Per Day: How Americans Use Time Saved by Working From Home
    The pandemic-induced shift to working from home lowers commuting time among Americans by more than 60 million hours per workday. Cumulative time savings over the past seven months exceed 9 billion hours.
    Jose Maria Barrero, Nick Bloom, and Steven J. Davis

    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.

  • September 18, 2020
    Businesses Anticipate Slashing Post-Pandemic Travel Budgets
    Firms expect to cut pre-pandemic travel expenditures by nearly 30 percent when concerns over COVID-19 subside.
    David Altig, Jose Maria Barrero, Nick Bloom, Steven J. Davis, Brent Meyer, Emil Mihaylov, and Nick Parker

    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.
  • September 18, 2020
    COVID-19 Shifted Patent Applications toward Technologies that Support Working from Home
    From March to May 2020, new patent applications that represent work-from-home technologies were well above the January level.
    Nicholas Bloom, Steven J. Davis, and Yulia Zhestkova

    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.

  • September 2, 2020
    The Occupation Effects of COVID-19
    Workers with low earnings, low wealth and low buffers of liquid assets are often employed in social-intensive occupations with limited possibilities for remote work, while workers in flexible occupations with low social exposure tend to have higher earnings, robust balance sheets, and adequate liquid wealth.
    Greg Kaplan, Benjamin Moll, and Giovanni L. Violante

    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.

  • September 2, 2020
    The Impact of the CARES Act on Economic Welfare
    The CARES Act mitigated economic welfare losses by around 20% on average without increasing fatalities. It redistributed economic gains heavily toward low-income households, while middle-income households gained little from the stimulus package.
    Greg Kaplan, Benjamin Moll, and Giovanni L. Violante

    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.

  • September 2, 2020
    A Tax-based Alternative to Lockdowns
    Taxes on personal behavior—whether attending social businesses or going to work when it is not required—rather than bluntly shutting down all non-essential businesses, are an alternative method to reduce the negative health and economic effects of COVID-19.
    Greg Kaplan, Benjamin Moll, and Giovanni L. Violante

    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.

  • August 19, 2020
    Dynamic Trade-Offs and Labor Supply Under the CARES Act
    New research finds that unemployed workers receiving benefits that exceed their previous wages would still prefer their old job, at the prior wage.
    Corina Boar, and Simon Mongey

    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.
  • August 14, 2020
    How Did US Consumers Use Their Stimulus Payments?
    Most respondents report that they primarily saved or paid down debts with their transfers, with only about 15 percent reporting that they mostly spent it. When providing a detailed breakdown of how they used their checks, individuals report having spent or planning to spend only around 40 percent of the total transfer on average.
    Olivier Coibion, Yuriy Gorodnichenko, and Michael Weber

    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.

  • August 6, 2020
    Partisanship Linked to Local Mask Use in US
    We find robust evidence that partisanship is correlated with mask use. Voting patterns in 2016 are the best predictor of local mask use. Messaging by President Trump increases public sentiment about mask-related content.
    Maria Milosh, Marcus Painter, David Van Dijcke, Konstantin Sonin, and Austin Wright

    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.

  • July 18, 2020
    Expanded Unemployment Insurance Benefits Have Propped-up Household Spending
    Unemployed households receiving UI benefits during the pandemic do not have depressed spending like they do in normal times, while those with delayed benefits have more depressed spending than in normal times.
    Diana Farrell, Peter Ganong, Fiona Greig, Max Liebeskind, Pascal Noel, and Joseph S. Vavra


    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.

  • July 17, 2020
    How COVID-19 is Changing Americans’ Behaviors, Expectations, and Political Views
    While strong divisions persist across party lines, personal experiences with COVID-19, such as loss of income, may affect views and preferences among Americans.
    Marianne Bertrand, Guglielmo Briscese, Maddalena Grignani, and Salma Nassar

    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.
  • July 13, 2020
    Investors Remain Focused on Sustainability During the COVID-19 Crisis
    When investors move their money during the crisis, they favor funds with high sustainability ratings and funds that apply portfolio exclusion criteria.
    Lubos Pastor, and M. Blair Vorsatz

    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.

  • July 13, 2020
    Active Mutual Funds Underperform Passive Benchmarks During the COVID-19 Crisis
    74% of active equity mutual funds underperform the S&P 500, and 58% of funds underperform their style benchmarks, in the ten weeks after the February 2020 market peak.
    Lubos Pastor, and M. Blair Vorsatz

    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.

  • July 9, 2020
    COVID-19 Results in Wave of Early Retirement
    The arrival of COVID-19 resulted in a large decline in labor force participation due to wave of early retirement, especially among women.
    Olivier Coibion, Yuriy Gorodnichenko, and Michael Weber
    COVID-19 Keeping Some Older Workers Home … Permanently

    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.

  • July 3, 2020
    Assessing the Paycheck Protection Program and Economic Outcomes
    The Payroll Protection Program (PPP) did not have a substantial effect on local economic outcomes during the first round of the program; rather, firms used funds to increase savings, pay loans, and meet other commitments.
    Joao Granja, Christos Makridis, Constantine Yannelis, and Eric Zwick
    Paycheck Protection Program Exposure (PPPE) and Post-PPP Outcomes

    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.

  • June 30, 2020
    Economic Uncertainty Before and During the COVID-19 Pandemic
    Most indicators of economic uncertainty in the US and UK reached their highest values on record following the onset of the COVID-19 pandemic, with peaks varying among indicators and along differing timelines
    David E. Altig, Scott R. Baker, Jose Maria Barrero, Nick Bloom, Phil Bunn, Scarlet Chen, Steven J. Davis, Brent Meyer, Emil Mihaylov, Paul Mizen, Nick Parker, Thomas Renault, Pawel Smietanka, and Greg Thwaites

    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.

  • June 26, 2020
    Measuring the Labor Market at the Onset of the COVID-19 Crisis
    The COVID-19 economic collapse was extremely sudden, nearly all of the decline in hours worked occurred between March 14 and March 28.
    Alexander Bartik, Marianne Bertrand, Feng Lin, Jesse Rothstein, and Matthew Unrath
    The Labor Market Collapse

    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.

     

  • June 25, 2020
    Did the Corporate Tax Breaks in the CARES Act Benefit Firms Most Impacted by COVID-19?
    An analysis of SEC filings suggests that the corporate tax breaks in the CARES Act benefitted firms with big stock price drops, but not states or industries with greater unemployment
    John Gallemore, Stephan Hollander, and Martin Jacob

    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.
    1. 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.
    1. 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.
    1. 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.
    1. 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.

  • June 24, 2020
    The Pandemic Recession’s Disproportionate Effect on Wage Earners
    US private sector employment contracted by about 21% between mid-February and late-April 2020, with workers in the bottom quintile of the wage distribution experiencing a 30% employment decline through May, and those in the top quintile only a 5% decline
    Tomaz Cajner, Leland Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher J. Kurz, and Ahu Yildirmaz

    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.
  • June 24, 2020
    Business Size and Type are Key Factors in Terms of Pandemic Recession Effects
    Employment declines were highest among businesses with fewer than 50 employees, with business closures occurring more frequently among interpersonal businesses
    Tomaz Cajner, Leland Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher J. Kurz, and Ahu Yildirmaz

    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.

  • June 24, 2020
    Employment Gains and State Re-Openings in the COVID Economy
    For the Food and Accommodations Sector, employment increased faster in states that re-opened sooner, but these gains were still significantly below February levels
    Tomaz Cajner, Leland Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher J. Kurz, and Ahu Yildirmaz

    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.

  • June 24, 2020
    The Pandemic Recession’s Disproportionate Impact on Women
    Women have experienced a sharper decline in employment than men, with a gender gap of 5 percentage points through mid-May
    Tomaz Cajner, Leland Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher J. Kurz, and Ahu Yildirmaz

    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.

  • June 23, 2020
    Spending and Savings of the Poor Growing Most Since April
    Spending plunged for all households at the onset of the pandemic. After government stimulus, poorer households had more rapid spending and savings growth than richer households.
    Natalie Cox, Peter Ganong, Pascal Noel, Joseph S. Vavra, Arlene Wong, Diana Farrell, and Fiona Greig

    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.

     

  • June 23, 2020
    Income and Poverty in the COVID-19 Pandemic
    Government policy effectively lowered income poverty shortly after the start of the COVID-19 pandemic in the US, with poverty rates falling 2.3 percentage points between January/February and April/May
    Jeehoon Han, Bruce Meyer, and James X. Sullivan

    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.

     

  • June 23, 2020
    COVID-19 is also a Reallocation Shock
    The COVID-19 economic shock caused 3 new hires in the near term for every 10 layoffs, with 32% to 42% of layoffs likely permanent
    Jose Maria Barrero, Nick Bloom, and Steven J. Davis
    Expected Rates of Employment Growth and Excess Job Reallocation Rate

    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.
  • June 19, 2020
    Treasury Inconvenience Yields during the COVID-19 Crisis
    US Treasury markets experienced severe stress and illiquidity in response to the COVID-19 pandemic and, in contrast to previous crises, prices of long-term Treasuries fell dramatically.
    Zhiguo He, Stefan Nagel, and Zhaogang Song
    Treasury Yields and Volatility Index (VIX) During the COVID-19 Crisis

    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.

  • June 19, 2020
    Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline 2020
    Consumer traffic to US stores fell by 60 percentage points at the outset of the COVID-19 pandemic, but legal restrictions explain only 7 percentage points of this decline.
    Austan Goolsbee, and Chad Syverson
    Consumer Visits Over Time by Store Size/Traffic

    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.

  • June 15, 2020
    The COVID Crisis and Productivity Growth
    Effective public policy could mitigate COVID's negative impact on productivity, while also encouraging growth.
    Filippo di Mauro, and Chad Syverson
    Productivity's Components: An Example (2008-2016)

    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.

  • June 11, 2020
    Coronavirus: Impact on Stock Prices and Growth Expectations
    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.
    Ralph Koijen, and Niels Gormsen
    Expected Dividend and GDP Growth from Dividend Futures

    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.

  • June 10, 2020
    Stimulus Checks Increase Household Spending
    Households respond rapidly to receipt of stimulus payments, with spending increasing by $0.29 per dollar of stimulus during the first 10 days.
    Scott R. Baker, R.A. Farrokhnia, Steffen Meyer, Michaela Pagel, and Constantine Yannelis
    Spending Around Stimulus Payments

    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.
  • May 29, 2020
    Financial Market Risk Perceptions during COVID-19
    The COVID-19 shock has impacted the short-term cashflows of firms. But it has also moved investor perceptions of risk, which may have longer-term effects on firm investment and the overall economy.
    Carolin Pflueger, Emil Siriwardane, and Adi Sunderam
    Daily Price of Volatile Stocks (PVs)

    Financial markets have fluctuated significantly as the COVID-19 epidemic has progressed.These fluctuations likely reflect both the anticipation of a steep drop in corporate earnings, as well as a reassessment of the risk of business investment. It is important to separate these two factors because upward revisions in risk perceptions can themselves reduce investment, deepening and prolonging the recession.

    To understand movements in risk perceptions relevant for the macroeconomy in near real-time, the authors employ the “price of volatile stocks” (PVSt)1, which is the book-to-market ratio of low-volatility stocks minus the book-to-market ratio of high-volatility stocks. In previous work, the authors showed that PVSt is low when perceived risk directly measured from surveys and option prices is high. Further, using time-series data from 1970 to 2016, the authors showed that when perceived risk is high according to PVSt, future real investment tends to be lower because the cost of capital is higher for risky firms.

    Figure 1 shows a daily time series of the authors’ measure of perceived risk, PVSt, from 1970 and through April 2020. It shows the price of volatile stocks fell sharply – and hence perceived risk rose sharply – as news about COVID-19 was hitting US markets and households in March 2020. PVSt reached its low for the year on April 3, 2020, when it was down 2.6 standard deviations from its level at the start of 2020. While this decline is large, it is comparable to movements in risk perceptions in prior recessions, particularly the downturn following the dotcom bubble in the early 2000s. It is also much smaller than the move in risk perceptions during the financial crisis of 2008-2009. Estimates for the period 1970-2016 indicate that a move in risk perceptions of the size experienced from the beginning of the year until this trough has typically been associated with a drop in the natural real risk-free rate of 3.3 percentage points, and a decline in the ratio of economy-wide capital expenditures to total assets of ratios of 0.91 percentage points (relative to a pre-2016 standard deviation of 1.16%).

    Figure 2 provides a close-up view of PVSt and the aggregate stock market during the COVID-19 pandemic (February 14, 2020 through April 30, 2020). The figure shows that PVSt is useful for interpreting individual events during the COVID-19 crisis and often contains information that is distinct from the aggregate stock market. One thing that stands out from this figure is that the steep drop in the aggregate stock market at the end of February left PVStalmost completely untouched, implying that perceptions of risk had not changed significantly. In other words, the evolution of PVSt at the onset of the crisis suggests that investors initially believed there would be a short-term decline in earnings, but did not believe there would be an amplification effect from heightened risk perceptions to the aggregate economy. However, PVSt and the aggregate market began to drop in tandem around March 11, the day the WHO declared COVID-19 a pandemic and wide-spread international travel restrictions were imposed. One possible interpretation for this decoupling and recoupling is that COVID-19 initially appeared to affect only the short-term cash flows of internationally connected firms, whereas the spread of the virus and the associated policy measures imposed in mid-March affected the risk outlook for a much broader swath of the economy. These trends were in turn reflected in the prices of volatile stocks.

    Another striking feature of Figure 2 is the large increase in PVSt that began on April 21, 2020, the day that the United States Senate passed the Paycheck Protection Program and Health Care Enhancement Act. The bill provided nearly $500 billion in additional funding to support the CARES Act, much of which was geared towards aiding small and medium-sized businesses. PVSt increased nearly 0.66 standard deviations between the time that the bill was passed in the Senate and when it was signed into law by President Trump on April 24. Interestingly, the market-to-book ratio of the aggregate stock market increased only 0.17 standard deviations over the same time period. The differential response of PVSt and the aggregate stock market to the passing of the bill is consistent with the authors’ previous interpretation that PVSt reflects perceptions of risk that are relevant for privately owned firms, which tend to be smaller and riskier than the larger, less volatile publicly traded firms that dominate the aggregate stock market.

    1  As developed in Pflueger, C., E. Siriwardane, and A. Sunderam (2020). “Financial market risk perceptions and the macroeconomy.” Quarterly Journal of Economics, forthcoming.

  • May 28, 2020
    Calculating the Impact of Voluntary Reallocation of Economic Activity Across Sectors of the Economy
    New research model suggests that, absent government-enforced shutdowns, individuals will likely take appropriate precautionary measures to the benefit of their health, in turn helping the economy as a whole.
    Dirk Krüger, Harald Uhlig, and Taojun Xie
    Reversing the Curve

    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.

  • May 19, 2020
    Public Disclosure of COVID-19 Cases Is More Effective than Lockdowns
    South Korea's public disclosure plan effectively protects the vulnerable while preserving economic stability during the pandemic.
    David Argente, Chang-Tai Hsieh, and Munseob Lee
    Disclosure Policy: Detected Cases and Deaths in Seoul, South Korea

    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.

  • May 15, 2020
    UI Benefits Exceed Lost Earnings for Most Unemployed
    The $600 supplement to UI under the CARES Act leads to earnings replacement rates above 100% for two-thirds of eligible unemployed.
    Peter Ganong, Pascal Noel, and Joseph S. Vavra
    UI Benefit Replacement Rates

    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.

     

     

  • May 11, 2020
    Targeted Lockdowns to Combat COVID-19
    Evidence from NYC reveals that targeted closures can achieve the same reductions in the epidemic spread as uniform city-wide closure policies at 40%-85% less economic cost.
    John R. Birge, Ozan Candogan, and Yiding Feng
    Optimal Targeted Closures for NYC

    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.
  • May 8, 2020
    The Cost of the COVID-19 Crisis
    Lockdowns substantially lower consumer spending, decrease employment, and lower inflation expectations, but they raise unemployment rate expectations, putting additional downward pressure on aggregate demand.
    Olivier Coibion, Yuriy Gorodnichenko, and Michael Weber
    COVID Cases, Lockdown, and Mobility

    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.

  • May 7, 2020
    The US Labor Market During the Beginning of the Pandemic Recession
    US private sector employment contracted by about 22% between mid-February and mid-April, 2020, with workers in the bottom quintile of the wage distribution experiencing a 35% employment decline and those in the top quintile only a 9% decline.
    Tomaz Cajner, Leland Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher J. Kurz, and Ahu Yildirmaz

    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.
  • April 28, 2020
    Burden of Job Losses Has Fallen on the Poor
    There have been large employment losses over February to March, which have disproportionately fallen on poor workers.
    Simon Mongey, Laura Pilossoph, and Alex Weinberg
    Who Has Born the Risk of Job Loss?

    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.

  • April 24, 2020
    The Shocking Supply-Side Effects of COVID-19
    The negative economic shock caused by COVID-19 is similar to a supply shock that causes a reduction in aggregate demand larger than the original reduction in labor supply.
    Veronica Guerrieri, Guido Lorenzoni, Ludwig Straub, and Iván Werning
    How Negative Supply Shocks Can Lead to Demand Shortages

    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.

  • April 24, 2020
    Overlapping Visits to Commercial Venues Are Down Two-Thirds
    Smartphone movement data reveal larger drops in cities where more can work from home.
    Jonathan Dingel
    Device Exposure is Down by Two-Thirds

    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/.

  • April 18, 2020
    Childcare Obligations and the Challenge of Reopening the Economy
    As conversations over re-opening the economy continue, 50 million Americans must consider childcare obligations when returning to work.
    Jonathan Dingel, Christina Patterson, and Joseph S. Vavra

    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.
  • April 14, 2020
    The Impact of COVID-19 on Small Businesses
    New Survey Reveals that Many US Small Businesses are Financially Fragile, Need Assistance, and Fear a Long Recession
    Alexander W. Bartik, Marianne Bertrand, Zoë B. Cullen, Edward L. Glaeser,, Michael Luca, and Christopher T. Stanton
    How Long Will This Last? Fraction Who Believe Crisis Will End Before Each Date

    Small businesses account for nearly 50 percent of US workers, and this new survey of nearly 6,000 firms reveals the financial fragility of many of those businesses and signals a cautionary note for policymakers, as most respondents expect the crisis to extend beyond the spring and well into the summer.

    The late-March 2020 survey focused on assessing small businesses’ current financial status, the extent of temporary closures and laid-off employees, duration expectations and the impact on decision-making, and whether businesses planned to apply for CARES Act funding and how such a decision could impact closures and lay-offs. Broadly, the survey revealed the following:

    • Disruption to US small businesses is severe, with 43% of the respondents temporarily closed. Employee reductions stood at 40% across all respondents. Regionally, mid-Atlantic states, including New York City, reported closures of 54% and layoffs of 47%. Industry responses varied widely, with service sector firms reporting employment declines over 50 percent.
    • Many US small businesses are standing on financially shaky ground, with the median firm with expenses over $10,000 per month retaining only enough cash to last for two weeks. For 75% of respondents, there was only enough cash to cover expenses for two months or less.
    • US small businesses are widely uncertain about when the crisis will end, with half expecting the crisis to persist into mid-summer, meaning that many firms expect this economic challenge to persist well beyond their available cash levels.

    For policymakers, the following results are particularly salient:

    • More than 13% of respondents did not plan to seek CARES Act funding because of application hassle, distrust that loans will be forgiven, and eligibility complexity.
    • If the crisis extends beyond four months, many firms—especially many in the service industries—do not expect to remain viable.
    • Extrapolating the 72 percent of businesses that would apply for CARES Act funding, and assuming all businesses would request maximum loans (2.5 months of expenses), the total volume of loans from all US businesses would approach about $410 billion, beyond the $349 allocated in the CARES Act at the time of the survey.
  • April 14, 2020
    Poverty and Economic Dislocation Reduce Compliance with COVID-19 Shelter-in-Place Protocols
    Compliance with COVID-19 shelter-in-place ordinances is higher in high-income areas and lower in areas affected by the recent trade war and with higher GOP vote shares.
    Konstantin Sonin, and Austin Wright
    Varying Income Levels by County (2016)

    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.

  • April 14, 2020
    Estimating the Fraction of Unreported COVID-19 Infections
    For every COVID-19 case reported in the US in early March, there are likely 6 to 24 unreported cases (accounting for reporting lags).
    Ali Hortaçsu, Jiarui Liu, and Timothy Schwieg
    Estimated Reported Infections by County

    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.

  • April 13, 2020
    Labor Force Participation Plummets, but US Experiences Modest Rise in Unemployment
    More than 20 million jobs were lost in March with only modest increases in the unemployment rate because many survey participants dropped out of the labor force after losing jobs.
    Olivier Coibion, Yuriy Gorodnichenko, and Michael Weber

    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.

  • April 3, 2020
    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.

  • April 1, 2020
    Major Changes to US Household Spending Due to COVID-19
    US Households sharply increased spending as pandemic spread.
    Constantine Yannelis
    Average Daily Household Spending in 2020

     

    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.

  • March 31, 2020
    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
    Welfare Effects of Closing Non-essential Businesses

    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
  • March 27, 2020
    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.

  • March 27, 2020
    Overhead Costs for Private Firms
    Overhead costs are an important consideration for developing post-coronavirus policies for protecting Main Street.
    Eric Zwick
    Approximate Overhead Costs by Industry for Private Firms

    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.
  • March 27, 2020
    US Firms Foresee Huge Sales Hit from Coronavirus
    Coronavirus developments cut expected sales revenue by more than 6% in 2020.
    Steven J. Davis
    Survey of Business Uncertainty (March 9 - 20, 2020)

    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.
  • March 27, 2020
    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, and Vishan Nigam
    Benefits of Social Distancing in the US are Projected to Exceed $8 Trillion

    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.