Assessing the economic impact of the COVID-19 pandemic is essential for policymakers, but challenging because the crisis has unfolded with extreme speed. We identify three indicators – stock market volatility, newspaper-based economic uncertainty, and subjective uncertainty in business expectation surveys – that provide real-time forward-looking uncertainty measures. We use these indicators to document and quantify the enormous increase in economic uncertainty in the past several weeks. We also illustrate how these forward-looking measures can be used to assess the macroeconomic impact of the COVID-19 crisis. Specifically, we feed COVID-induced first-moment and uncertainty shocks into an estimated model of disaster effects developed by Baker, Bloom and Terry (2020). Our illustrative exercise implies a year-on-year contraction in U.S. real GDP of nearly 11 percent as of 2020 Q4, with a 90 percent confidence interval extending to a nearly 20 percent contraction. The exercise says that about half of the forecasted output contraction reflects a negative effect of COVID-induced uncertainty.

In addition to its impact on public health, COVID-19 has had a major impact on the economy. To shed light on how COVID-19 is affecting small businesses – and on the likely impact of the recent stimulus bill, we conducted a survey of more than 5,800 small businesses. Several main themes emerge from the results. First, mass layoffs and closures have already occurred. In our sample, 43 percent of businesses are temporarily closed, and businesses have – on average – reduced their employee counts by 40 percent relative to January. Second, consistent with previous literature, we find that many small businesses are financially fragile. For example, the median business has more than $10,000 in monthly expenses and less than one month of cash on hand. Third, businesses have widely varying beliefs about the likely duration of COVID related disruptions. Fourth, the majority of businesses planned to seek funding through the CARES act. However, many anticipated problems with accessing the aid, such as bureaucratic hassles and difficulties establishing eligibility.

This paper takes an early look at the Paycheck Protection Program (PPP), a large and novel small business support program that was part of the initial policy response to the COVID-19 pandemic. We use new data on the distribution of the first round of PPP loans and high-frequency micro- level employment data to consider two dimensions of program targeting. First, we do not find evidence that funds flowed to areas more adversely affected by the economic effects of the pandemic, as measured by declines in hours worked or business shutdowns. If anything, funds flowed to areas less hard hit. Second, we find significant heterogeneity across banks in terms of disbursing PPP funds, which does not only reflect differences in underlying loan demand. The top-4 banks alone account for 36% of total pre-policy small business loans, but disbursed less than 3% of all PPP loans in the first round. Areas that were significantly more exposed to low- PPP banks received much lower loan allocations. We do not find evidence that the PPP had a substantial effect on local economic outcomes—including declines in hours worked, business shutdowns, initial unemployment insurance claims, and small business revenues—during the first round of the program. Firms appear to use first round funds to build up savings and meet loan and other commitments, which points to possible medium-run impacts. As data become available, we will continue to study employment and establishment responses to the program and the impact of PPP support on the economic recovery. Measuring these responses is critical for evaluating the social insurance value of the PPP and similar policies.

Drawing on firm-level expectations at a one-year forecast horizon in the Survey of Business Uncertainty (SBU), we construct novel, forward-looking reallocation measures for jobs and sales. These measures rise sharply after February 2020, reaching rates in April that are 2.4 (3.9) times the pre-COVID average for jobs (sales). We also draw on special questions in the April SBU to quantify the near-term impact of the COVID-19 shock on business staffing. We find 3 new hires for every 10 layoffs caused by the shock and estimate that 42 percent of recent layoffs will result in permanent job loss. Our survey evidence aligns well with anecdotal evidence of large pandemic-induced demand increases at some firms, with contemporaneous evidence on gross business formation, and with a sharp pandemic-induced rise in equity return dispersion across firms. After developing the evidence, we consider implications of our evidence for the economic outlook and for policy responses to the pandemic. Unemployment benefit levels that exceed worker earnings, policies that subsidize employee retention, occupational licensing restrictions, and regulatory barriers to business formation will impede reallocation responses to the COVID-19 shock.

Using weekly administrative payroll data from the largest U.S. payroll processing company, we measure the evolution of the U.S. labor market during the first four months of the global COVID-19 pandemic. After aggregate employment fell by 21 percent through late-April, employment rebounded somewhat through late-June. The re-opening of temporarily shuttered businesses contributed significantly to the employment rebound, particularly for smaller businesses. We show that worker recall has been an important component of recent employment gains for both re-opening and continuing businesses. Employment losses have been concentrated disproportionately among lower wage workers; as of late June employment for workers in the lowest wage quintile was still 20 percent lower relative to mid-February levels. As a result, average base wages increased between February and June, though this increase arose entirely through a composition effect. Finally, we document that businesses have cut nominal wages for almost 7 million workers while forgoing regularly scheduled wage increases for many others.

Big data on job-vacancy postings reveal several dimensions of the impact of COVID-19 on the U.S. job market. Firms have cut back on postings for high-skill jobs more than for low-skill jobs, with small firms nearly halting their new hiring altogether. New-hiring cuts and downskilling are most pronounced in local labor markets lacking depth (where employment is concentrated within a few firms), in low-income areas, and in areas with greater income inequality. Cuts are deeper in industries where workers are more unionized and in the non-tradable sector. Access to finance modulates corporate hiring, with credit-constrained firms curtailing their job postings the most. Our study shows how the early-2020 global pandemic is shaping the dynamics of hiring, identifying the firms, jobs, places, industries, and labor markets most affected by it. Our results point to important challenges to the scale and speed of a recovery.

COVID-19 abruptly impacted the labor market with the unemployment rate jumping to 14.7 percent less than two months after state governments began adopting social distancing measures. Unemployment of this magnitude has not been seen since the Great Depression. This paper provides the first study of how the pandemic impacted minority unemployment using CPS microdata through April 2020. African-Americans experienced an increase in unemployment to 16.6 percent, less than anticipated based on previous recessions. In contrast, Latinx, with an unemployment rate of 18.2 percent, were disproportionately hard hit by COVID-19. Adjusting for concerns of the BLS regarding misclassification of unemployment, we create an upper-bound measure of the national unemployment rate of 26.5 percent, which is higher than the peak observed in the Great Depression. The April 2020 upper-bound unemployment rates are an alarming 31.8 percent for blacks and 31.4 percent for Latinx. Difference-in-difference estimates suggest that blacks were, at most, only slightly disproportionately impacted by COVID-19. Non- linear decomposition estimates indicate that a slightly favorable industry distribution partly protected them from being hit harder by COVID-19. The most impacted group are Latinx. Difference-in-difference estimates unequivocally indicate that Latinx were disproportionately impacted by COVID-19. An unfavorable occupational distribution and lower skills contributed to why Latinx experienced much higher unemployment rates than whites. These findings of early impacts of COVID-19 on unemployment raise important concerns about long-term economic effects for minorities.

In March of 2020, banks faced the largest increase in liquidity demands ever observed. Firms drew funds on a massive scale from pre-existing credit lines and loan commitments in anticipation of cash flow disruptions from the economic shutdown designed to contain the COVID-19 crisis. The increase in liquidity demands was concentrated at the largest banks, who serve the largest firms. Pre-crisis financial condition did not limit banks’ liquidity supply. Coincident inflows of funds to banks from both the Federal Reserve’s liquidity injection programs and from depositors, along with strong pre-shock bank capital, explain why banks were able to accommodate these liquidity demands.

Social distancing restrictions and demand shifts from COVID-19 are expected to shutter many small businesses, but there is very little early evidence on impacts. This paper provides the first analysis of impacts of the pandemic on the number of active small businesses in the United States using nationally representative data from the April 2020 CPS – the first month fully capturing early effects from the pandemic. The number of active business owners in the United States plummeted by 3.3 million or 22 percent over the crucial two-month window from February to April 2020. The drop in business owners was the largest on record, and losses were felt across nearly all industries and even for incorporated businesses. African-American businesses were hit especially hard experiencing a 41 percent drop. Latinx business owners fell by 32 percent, and Asian business owners dropped by 26 percent. Simulations indicate that industry compositions partly placed these groups at a higher risk of losses. Immigrant business owners experienced substantial losses of 36 percent. Female-owned businesses were also disproportionately hit by 25 percent. These findings of early-stage losses to small businesses have important policy implications and may portend longer-term ramifications for job losses and economic inequality.

How quickly will American businesses reopen after COVID-19 lockdowns end? We use a nationwide survey of small businesses to measure firms’ expectations about their re-opening and future demand. A plurality of firms in our sample expect to reopen within days of the end of legal restrictions, but a sizable minority expect to delay their reopening. While health-related variables, such as COVID-19 case rates and physical proximity of workers, do explain the prevalence and expected duration of regulated lockdown, these variables have little or no correlation with post- lockdown reopening intentions. Instead, almost one half of closed or partially open businesses said that their reopening would depend on the reopening of related businesses, including customers and suppliers. Owners expect demand to be one-third lower than before the crisis through autumn. Firms with more pessimistic expectations about demand predict a later reopening. Using an instrumental variables strategy, we estimate the relationship between demand expectations and reopening. These estimates suggest that post-lockdown delays in reopening can be explained by low levels of expected demand.

How Did Covid-19 And Stabilization Policies Affect Spending And Employment? A New Real-Time Economic Tracker Based On Private Sector Data.

We build a publicly available platform that tracks economic activity at a granular level in real time using anonymized data from private companies. We report daily statistics on consumer spending, business revenues, employment rates, and other key indicators disaggregated by county, industry, and income group. Using these data, we study the mechanisms through which COVID-19 affected the economy by analyzing heterogeneity in its impacts across geographic areas and income groups. We first show that high-income individuals reduced spending sharply in mid-March 2020, particularly in areas with high rates of COVID-19 infection and in sectors that require physical interaction. This reduction in spending greatly reduced the revenues of businesses that cater to high-income households in person, notably small businesses in affluent ZIP codes. These businesses laid off most of their low-income employees, leading to a surge in unemployment claims in affluent areas. Building on this diagnostic analysis, we use event study designs to estimate the causal effects of policies aimed at mitigating the adverse impacts of COVID. State-ordered reopenings of economies have little impact on local employment. Stimulus payments to low-income households increased consumer spending sharply, but had modest impacts on employment in the short run, perhaps because very little of the increased spending flowed to businesses most affected by the COVID-19 shock. Paycheck Protection Program loans have also had little impact on employment at small businesses. These results suggest that traditional macroeconomic tools – stimulating aggregate demand or providing liquidity to businesses – may have diminished capacity to restore employment when consumer spending is constrained by health concerns. During a pandemic, it may be more fruitful to mitigate economic hardship through social insurance. More broadly, this analysis illustrates how real-time economic tracking using private sector data can help rapidly identify the origins of economic crises and facilitate ongoing evaluation of policy impacts.

The collapse of economic activity in 2020 from COVID-19 has been immense. An important question is how much of that resulted from government restrictions on activity versus people voluntarily choosing to stay home to avoid infection. This paper examines the drivers of the collapse using cellular phone records data on customer visits to more than 2.25 million individual businesses across 110 different industries. Comparing consumer behavior within the same commuting zones but across boundaries with different policy regimes suggests that legal shutdown orders account for only a modest share of the decline of economic activity (and that having county-level policy data is significantly more accurate than state-level data). While overall consumer traffic fell by 60 percentage points, legal restrictions explain only 7 of that. Individual choices were far more important and seem tied to fears of infection. Traffic started dropping before the legal orders were in place; was highly tied to the number of COVID deaths in the county; and showed a clear shift by consumers away from larger/busier stores toward smaller/less busy ones in the same industry. States repealing their shutdown orders saw identically modest recoveries--symmetric going down and coming back. The shutdown orders did, however, have significantly reallocate consumer activity away from “nonessential” to “essential” businesses and from restaurants and bars toward groceries and other food sellers.

We use traditional and non-traditional data to measure the collapse and partial recovery of the U.S. labor market from March to early July, contrast this downturn to previous recessions, and provide preliminary evidence on the effects of the policy response. For hourly workers at both small and large businesses, nearly all of the decline in employment occurred between March 14 and 28. It was driven by low-wage services, particularly the retail and leisure and hospitality sectors. A large share of the job losses in small businesses reflected firms that closed entirely, though many subsequently reopened. Firms that were already unhealthy were more likely to close and less likely to reopen, and disadvantaged workers were more likely to be laid off and less likely to return. Most laid off workers expected to be recalled, and this was predictive of rehiring. Shelter-in-place orders drove only a small share of job losses. Last, states that received more small business loans from the Paycheck Protection Program and states with more generous unemployment insurance benefits had milder declines and faster recoveries. We find no evidence that high UI replacement rates drove job losses or slowed rehiring.

The Paycheck Protection Program (PPP) aimed to quickly deliver hundreds of billions of dollars of loans to small businesses, with the loans administered via private banks. In this paper, we use firm-level data to document the demand and supply of PPP funds. Using an instrumental variables approach, we find that PPP loans led to a 14 to 30 percentage point increase in a business’s expected survival, and a positive but imprecise effect on employment. Moreover, the effects on survival were heterogeneous and highlight an important tradeoff faced by policymakers: while administering the loans via private banks allowed for rapid delivery of funds, it also limited the government’s ability to target the funding - instead allowing pre-existing connections between businesses and banks to determine which firms would benefit from the program.

Using unique City of Oakland data during COVID-19, we document that small business survival capabilities vary by firm size as a function of revenue resiliency, labor flexibility, and committed costs. Nonemployer businesses rely on low cost structures to survive 73% declines in own-store foot traffic. Microbusinesses (1-to-5 employees) depend on 14% greater revenue resiliency. Enterprises (6-to-50 employees) have twice-as-much labor flexibility, but face 11%-to-22% higher residual closure risk from committed costs. Finally, inconsistent with the spirit of Chetty- Friedman-Hendren-Sterner (2020) and Granja-Makridis-Yannelis-Zwick (2020), PPP application success increased medium-run survival probability by 20.5%, but only for microbusinesses, arguing for size-targeting of policies.

New technology promises to expand the supply of financial services to borrowers poorly served by the banking system. Does it succeed? We study the response of FinTech to financial services demand created by the introduction of the Paycheck Protection Program (PPP). We find that FinTech is disproportionately used in ZIP codes with fewer bank branches, lower incomes, and a larger minority share of the population, as well as in industries with little ex ante small-business lending. Its role in PPP provision is also greater in counties where the economic effects of the COVID-19 pandemic were more severe. To understand whether these differences arise because certain groups are switching from traditional banks to FinTech or if they are being newly served by FinTech, we study whether FinTech-enabled PPP loans were more widespread in areas with fewer traditional loans. Using the predicted responsiveness of traditional banks to the program as an instrument, we show that borrowers were more likely to get a FinTech-enabled PPP loan if they were located in ZIP codes where local banks were unlikely to originate PPP loans.

The Paycheck Protection Program (PPP), a principal element of the fiscal stimulus enacted by Congress in response to the COVID-19 economic shock, is intended to assist small businesses to maintain employment and wages during the crisis. An obstacle to assessing whether the PPP achieved this goal is the absence of granular, high-frequency employment data that can precisely capture any causal effect of the PPP on employment. We use administrative data from ADP—one of the world’s largest payroll processing firms—to contrast the evolution of payroll employment at PPP-eligible and PPP-ineligible firms, where eligibility is determined by industry-specific firm-size cutoffs. We estimate that the PPP boosted employment at eligible firms by 2 to 4.5 percent, with a preferred central tendency estimate of approximately 3.25 percent. Our estimates imply that the PPP increased aggregate U.S. employment by 1.4 million to 3.2 million jobs through the first week of June 2020, with a preferred central tendency estimate of about 2.3 million workers. In an alternative analysis, we identify the effect of the PPP from a set of firms for which we can observe loan take-up and obtain results at the upper end of the range of estimates. Although the evidence is supportive of a causal effect of the PPP on aggregate employment, we are careful to highlight puzzles where they occur and view our work as preliminary in nature. Future work will leverage loan-level PPP data to calibrate the relationship between eligibility, take-up, and employment.

We link the county-level rollout of stay-at-home orders to anonymized cell phone records and consumer spending data. We document three patterns. First, stay-at-home orders caused people to stay home: Countylevel measures of mobility declined 8% by the day after the stay-at-home order went into effect. Second, stay-at-home orders caused large reductions in spending in sectors associated with mobility: small businesses and large retail stores. However, consumers sharply increased spending on food delivery services after orders went into effect. Third, responses to stay-at-home orders were fairly uniform across the country, and do not vary by income, political leanings, or urban/rural status.

We study the short-term causal effect of the Paycheck Protection Program on unemployment. Using the 2019 density of Small Business Administration member bank offices in a county as an instrument for PPP loans originated in that county during April 2020, we find statistically and economically significant effects from the program on unemployment. Our results highlight the importance of this relief policy and the financial system infrastructure in preserving jobs during the COVID-19 crisis.