Showing papers by "National Bureau of Economic Research published in 2020"

COVID-induced economic uncertainty

Abstract: 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.

The fall of the labor share and the rise of superstar firms

Abstract: The fall of labor's share of GDP in the United States and many other countries in recent decades is well documented but its causes remain uncertain. Existing empirical assessments of trends in labor's share typically have relied on industry or macro data, obscuring heterogeneity among firms. In this paper, we analyze micro panel data from the U.S. Economic Census since 1982 and international sources and document empirical patterns to assess a new interpretation of the fall in the labor share based on the rise of \superstar firms." If globalization or technological changes advantage the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms with high profits and a low share of labor in firm valueadded and sales. As the importance of superstar firms increases, the aggregate labor share will tend tofall. Our hypothesis offers several testable predictions: industry sales will increasingly concentrate in a small number of firms; industries where concentration rises most will have the largest declines in the labor share; the fall in the labor share will be driven largely by between-firm reallocation rather than (primarily) a fall in the unweighted mean labor share within firms; the between-firm reallocation component of the fall in the labor share will be greatest in the sectors with the largest increases in market concentration; and finally, such patterns will be observed not only in U.S. firms, but also internationally. We find support for all of these predictions.

Community Use Of Face Masks And COVID-19: Evidence From A Natural Experiment Of State Mandates In The US.

Abstract: State policies mandating public or community use of face masks or covers in mitigating the spread of coronavirus disease 2019 (COVID-19) are hotly contested. This study provides evidence from a natural experiment on the effects of state government mandates for face mask use in public issued by fifteen states plus Washington, D.C., between April 8 and May 15, 2020. The research design is an event study examining changes in the daily county-level COVID-19 growth rates between March 31 and May 22, 2020. Mandating face mask use in public is associated with a decline in the daily COVID-19 growth rate by 0.9, 1.1, 1.4, 1.7, and 2.0 percentage points in 1-5, 6-10, 11-15, 16-20, and 21 or more days after state face mask orders were signed, respectively. Estimates suggest that as a result of the implementation of these mandates, more than 200,000 COVID-19 cases were averted by May 22, 2020. The findings suggest that requiring face mask use in public could help in mitigating the spread of COVID-19.

Gender differences in COVID-19 attitudes and behavior: Panel evidence from eight countries.

Abstract: The initial public health response to the breakout of COVID-19 required fundamental changes in individual behavior, such as isolation at home or wearing masks. The effectiveness of these policies hinges on generalized public obedience. Yet, people's level of compliance may depend on their beliefs regarding the pandemic. We use original data from two waves of a survey conducted in March and April 2020 in eight Organisation for Economic Co-operation and Development countries (n = 21,649) to study gender differences in COVID-19-related beliefs and behaviors. We show that women are more likely to perceive COVID-19 as a very serious health problem, to agree with restraining public policy measures, and to comply with them. Gender differences in attitudes and behavior are sizable in all countries. They are accounted for neither by sociodemographic and employment characteristics nor by psychological and behavioral factors. They are only partially mitigated for individuals who cohabit or have direct exposure to the virus. We show that our results are not due to differential social desirability bias. This evidence has important implications for public health policies and communication on COVID-19, which may need to be gender based, and it unveils a domain of gender differences: behavioral changes in response to a new risk.

COVID-19 and Remote Work: An Early Look at US Data

Abstract: We report the results of a nationally-representative sample of the US population during the COVID-19 pandemic. The survey ran in two waves from April 1-5, 2020 and May 2-8, 2020. Of those employed pre-COVID-19, we find that about half are now working from home, including 35.2% who report they were commuting and recently switched to working from home. In addition, 10.1% report being laid-off or furloughed since the start of COVID-19. There is a strong negative relationship between the fraction in a state still commuting to work and the fraction working from home. We find that the share of people switching to remote work can be predicted by the incidence of COVID-19 and that younger people were more likely to switch to remote work. Furthermore, states with a higher share of employment in information work including management, professional and related occupations were more likely to shift toward working from home and had fewer people laid off or furloughed. We find no substantial change in results between the two waves, suggesting that most changes to remote work manifested by early April.

How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic

Abstract: We explore how household consumption responds to epidemics, utilizing transaction-level household financial data to investigate the impact of the COVID-19 virus. As the number of cases grew, households began to radically alter their typical spending across a number of major categories. Initially spending increased sharply, particularly in retail, credit card spending and food items. This was followed by a sharp decrease in overall spending. Households responded most strongly in states with shelter-in-place orders in place by March 29th. We explore heterogeneity across partisan affiliation, demographics and income. Greater levels of social distancing are associated with drops in spending, particularly in restaurants and retail.

A Simple Planning Problem for COVID-19 Lockdown

Abstract: We study the optimal lockdown policy for a planner who wants to control the fatalities of a pandemic while minimizing the output costs of the lockdown. We use the SIR epidemiology model and a linear economy to formalize the planner's dynamic control problem. The optimal policy depends on the fraction of infected and susceptible in the population. We parametrize the model using data on the COVID19 pandemic and the economic breadth of the lockdown. The quantitative analysis identifies the features that shape the intensity and duration of the optimal lockdown policy. Our baseline parametrization is conditional on a 1% of infected agents at the outbreak, no cure for the disease, and the possibility of testing. The optimal policy prescribes a severe lockdown beginning two weeks after the outbreak, covers 60% of the population after a month, and is gradually withdrawn covering 20% of the population after 3 months. The intensity of the lockdown depends on the gradient of the fatality rate as a function of the infected, and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown, and shortens the duration of the optimal lockdown which ends more abruptly. Welfare under the optimal policy with testing is higher, equivalent to a one-time payment of 2% of GDP.

How are Small Businesses Adjusting to COVID-19? Early Evidence from a Survey

Abstract: 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

Aggregate Confusion: The Divergence of ESG Ratings

Abstract: This paper investigates the divergence of environmental, social, and governance (ESG) ratings. Based on data from six prominent rating agencies - namely, KLD (MSCI Stats), Sustainalytics, Vigeo Eiris (Moody's), RobecoSAM (SP Global), Asset4 (Refinitiv), and MSCI IVA- we decompose the divergence into three sources: different scope of categories, different measurement of categories, and different weights of categories. We find that scope and measurement divergence are the main drivers, while weights divergence is less important. In addition, we detect a rater effect where a rater's overall view of a firm influences the assessment of specific categories.

Determinants of Disparities in Covid-19 Job Losses

Abstract: We make several contributions to understanding the socio-demographic divide in early labor market responses to the U.S. COVID-19 epidemic and its policies, benchmarked against two previous recessions. First, monthly Current Population Survey (CPS) data show greater declines in employment in April and May 2020 (relative to February) for Hispanics, younger workers, and those with high school degrees and some college. Between April and May, all the demographic subgroups considered regained some employment. Re-employment in May was broadly proportional to the employment drop that occurred through April, except for Blacks who experienced a smaller rebound. Further, we show that compared to the 2001 recession and the Great Recession, employment losses in the early COVID-19 recession were smaller for groups with very low or very high (vs. medium) education. Second, we show that job loss was larger in occupations that require more interpersonal contact and that cannot be performed remotely. Third, we find pre-COVID-19 sorting of workers into occupations and industries along demographic lines can explain a sizeable portion of the gender, race, and ethnic gaps in new unemployment. For example, while women did suffer more job losses than men, their disproportionate pre-epidemic sorting into remote work compatible occupations shielded women from what would have been even larger employment losses during the epidemic. However, there remain substantial gaps in employment losses across groups that cannot be explained by socio-economic differences. We find some larger gaps in labor market impacts when we consider the “employed but absent from work” measure present in the CPS, in addition to the more traditional employment and unemployment measures. We conclude with a discussion of policy lessons and future research needs implied by the disparities in early labor market losses from the COVID-19 crisis.

Does Social Distancing Matter

Abstract: This paper develops and implements a method to monetize the impact of moderate social distancing on deaths from COVID-19. Using the Ferguson et al. (2020) simulation model of COVID-19’s spread and mortality impacts in the United States, we project that 3-4 months of moderate distancing beginning in late March 2020 would save 1.7 million lives by October 1. Of the lives saved, 630,000 are due to avoided overwhelming of hospital intensive care units. Using the projected age-specific reductions in death and age-varying estimates of the United States Government’s value of a statistical life, we find that the mortality benefits of social distancing are about $8 trillion or $60,000 per US household. Roughly 90% of the monetized benefits are projected to accrue to people age 50 or older. Overall, the analysis suggests that social distancing initiatives and policies in response to the COVID-19 epidemic have substantial economic benefits.

The Cost of the COVID-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending

Abstract: We study how the differential timing of local lockdowns due to COVID-19 causally affects households’ spending and macroeconomic expectations at the local level using several waves of a customized survey with more than 10,000 respondents. About 50% of survey participants report income and wealth losses due to the corona virus, with the average losses being $5,293 and $33,482 respectively. Aggregate consumer spending dropped by 31 log percentage points with the largest drops in travel and clothing. We find that households living in counties that went into lockdown earlier expect the unemployment rate over the next twelve months to be 13 percentage points higher and continue to expect higher unemployment at horizons of three to five years. They also expect lower future inflation, report higher uncertainty, expect lower mortgage rates for up to 10 years, and have moved out of foreign stocks into liquid forms of savings. The imposition of lockdowns can account for much of the decline in employment in recent months as well as declines in consumer spending. While lockdowns have pronounced effects on local economic conditions and households’ expectations, they have little impact on approval ratings of Congress, the Fed, or the Treasury but lead to declines in the approval of the President.

The Rise of Market Power and the Macroeconomic Implications

Abstract: We document the evolution of market power based on firm-level data for the U.S. economy since 1955. We measure both markups and profitability. In 1980, aggregate markups start to rise from 21% above marginal cost to 61% now. The increase is driven mainly by the upper tail of the markup distribution: the upper percentiles have increased sharply. Quite strikingly, the median is unchanged. In addition to the fattening upper tail of the markup distribution, there is reallocation of market share from low- to high-markup firms. This rise occurs mostly within industry. We also find an increase in the average profit rate from 1% to 8%. Although there is also an increase in overhead costs, the markup increase is in excess of overhead. We discuss the macroeconomic implications of an increase in average market power, which can account for a number of secular trends in the past four decades, most notably the declining labor and capital shares as well as the decrease in labor market dynamism.

Race and Economic Opportunity in the United States: an Intergenerational Perspective

Abstract: We study the sources of racial disparities in income using anonymized longitudinal data covering nearly the entire U.S. population from 1989 to 2015. We document three results. First, black Americans and American Indians have much lower rates of upward mobility and higher rates of downward mobility than whites, leading to persistent disparities across generations. Conditional on parent income, the black-white income gap is driven by differences in wages and employment rates between black and white men; there are no such differences between black and white women. Hispanic Americans have rates of intergenerational mobility more similar to whites than blacks, leading the Hispanic-white income gap to shrink across generations. Second, differences in parental marital status, education, and wealth explain little of the black-white income gap conditional on parent income. Third, the black-white gap persists even among boys who grow up in the same neighborhood. Controlling for parental income, black boys have lower incomes in adulthood than white boys in 99% of Census tracts. The few areas with small black-white gaps tend to be low-poverty neighborhoods with low levels of racial bias among whites and high rates of father presence among blacks. Black males who move to such neighborhoods earlier in childhood have significantly better outcomes. However, less than 5% of black children grow up in such areas. Our findings suggest that reducing the black-white income gap will require efforts whose impacts cross neighborhood and class lines and increase upward mobility specifically for black men.

Taming the Factor Zoo: A Test of New Factors

Abstract: We propose a model selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high‐dimensional set of existing factors explains. Our methodology accounts for model selection mistakes that produce a bias due to omitted variables, unlike standard approaches that assume perfect variable selection. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are shown to be redundant relative to the existing factors, a few have statistically significant explanatory power beyond the hundreds of factors proposed in the past.

Financial Heterogeneity and the Investment Channel of Monetary Policy

Abstract: We study the role of financial frictions and firm heterogeneity in determining the investment channel of monetary policy. Empirically, we find that firms with low default risk—those with low debt burdens and high “distance to default”— are the most responsive to monetary shocks. We interpret these findings using a heterogeneous firm New Keynesian model with default risk. In our model, low‐risk firms are more responsive to monetary shocks because they face a flatter marginal cost curve for financing investment. The aggregate effect of monetary policy may therefore depend on the distribution of default risk, which varies over time.

Estimating and simulating a sird model of COVID-19 for many countries, states, and cities

Abstract: We use data on deaths in New York City, Madrid, Stockholm, and other world cities as well as in various U.S. states and various countries and regions to estimate a standard epidemiological model of COVID-19. We allow for a time-varying contact rate in order to capture behavioral and policy-induced changes associated with social distancing. We simulate the model forward to consider possible futures for various countries, states, and cities, including the potential impact of herd immunity on re-opening. Our current baselinemortality rate (IFR) is assumed to be 1.0% but we recognize there is substantial uncertainty about this number. Our model fits the death data equally well with alternative mortality rates of 0.5% or 1.2%, so this parameter is unidentified in our data. However, its value matters enormously for the extent to which various places can relax social distancing without spurring a resurgence of deaths.

The Return to Protectionism

Abstract: After decades of supporting free trade, in 2018 the United States raised import tariffs and major trade partners retaliated. We analyze the short-run impact of this return to protectionism on the U.S. economy. Import and retaliatory tariffs caused large declines in imports and exports. Prices of imports targeted by tariffs did not fall, implying complete pass-through of tariffs to duty-inclusive prices. The resulting losses to U.S. consumers and firms that buy imports was $51 billion, or 0.27% of GDP. We embed the estimated trade elasticities in a general-equilibrium model of the U.S. economy. After accounting for tariff revenue and gains to domestic producers, the aggregate real income loss was $7.2 billion, or 0.04% of GDP. Import tariffs favored sectors concentrated in politically competitive counties, and the model implies that tradeable-sector workers in heavily Republican counties were the most negatively affected due to the retaliatory tariffs. JEL Code: F1.

Aggregate and Firm-Level Stock Returns During Pandemics, in Real Time

Abstract: We show that unexpected changes in the trajectory of COVID-19 infections predict US stock returns, in real time. Parameter estimates indicate that an unanticipated doubling (halving) of projected infections forecasts next-day decreases (increases) in aggregate US market value of 4 to 11 percent, indicating that equity markets may begin to rebound even as infections continue to rise, if the trajectory of the disease becomes less severe than initially anticipated. Using the same variation in unanticipated projected cases, we find that COVID-19-related losses in market value at the firm level rise with capital intensity and leverage, and are deeper in industries more conducive to disease transmission. These relationships provide important insight into current record job losses. Measuring US states' drops in market value as the employment weighted average declines of the industries they produce, we find that states with milder drops in market value exhibit larger initial jobless claims per worker. This initially counter-intuitive result suggests that investors value the relative ease with which labor versus capital costs can be shed as revenues decline.

Disparities in Coronavirus 2019 Reported Incidence, Knowledge, and Behavior Among US Adults.

Abstract: Importance Data from the coronavirus disease 2019 (COVID-19) pandemic in the US show large differences in hospitalizations and mortality across race and geography However, there are limited data on health information, beliefs, and behaviors that might indicate different exposure to risk Objective To determine the association of sociodemographic characteristics with reported incidence, knowledge, and behavior regarding COVID-19 among US adults Design, Setting, and Participants A US national survey study was conducted from March 29 to April 13, 2020, to measure differences in knowledge, beliefs, and behavior about COVID-19 The survey oversampled COVID-19 hotspot areas The survey was conducted electronically The criteria for inclusion were age 18 years or older and residence in the US Data analysis was performed in April 2020 Main Outcomes and Measures The main outcomes were incidence, knowledge, and behaviors related to COVID-19 as measured by survey response Results The survey included 5198 individuals (mean [SD] age, 48 [18] years; 2336 men [45%]; 3759 white [72%], 830 [16%] African American, and 609 [12%] Hispanic) The largest differences in COVID-19–related knowledge and behaviors were associated with race/ethnicity, sex, and age, with African American participants, men, and people younger than 55 years showing less knowledge than other groups African American respondents were 35 percentage points (95% CI, 15 to 55 percentage points;P = 001) more likely than white respondents to report being infected with COVID-19, as were men compared with women (32 percentage points; 95% CI, 20 to 44 percentage points;P Conclusions and Relevance In this survey study of US adults, there were gaps in reported incidence of COVID-19 and knowledge regarding its spread and symptoms and social distancing behavior More effort is needed to increase accurate information and encourage appropriate behaviors among minority communities, men, and younger people

Health Care Hotspotting - A Randomized, Controlled Trial.

Abstract: Background There is widespread interest in programs aiming to reduce spending and improve health care quality among “superutilizers,” patients with very high use of health care services. T...

A Model of Cryptocurrencies

Abstract: We model a cryptocurrency as membership in a decentralized digital platform developed to facilitate transactions between users of certain goods or services. The rigidity induced by the cryptocurrency price having to clear membership demand with supply of token by speculators, especially with strong complementarity in membership demand, can lead to market breakdown. While user optimism mitigates the market fragility by increasing user participation, speculator sentiment exacerbates it by crowding users out. Informational frictions attenuate the risk of breakdown by dampening price volatility and platform performance. Furthermore, the users' anticipation of losses from strategic attacks by miners exacerbates the market fragility.

Coronavirus: Impact on Stock Prices and Growth Expectations

Abstract: We use data from the aggregate stock and dividend futures markets to quantify how investors' expectations about economic growth evolve across horizons in response to the new coronavirus (COVID-19) outbreak and subsequent policy responses until July 2020. 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 forecasting model. We show how the actual forecast and the bound evolve over time. As of July 20, our forecast of annual growth in dividends points to a decline of 8% in both the US and Japan and a 14% decline in the EU compared to January 1. Our forecast of GDP growth points to a decline of 2% in the US and Japan and 3% in the EU. The lower bound on the change in expected dividends is -17% in the US and Japan and -28% in the EU at the 2-year horizon. News about fiscal stimulus around March 24 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 all geographies.

Did California’s Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Effects

Abstract: On March 19, 2020, California Governor Gavin Newsom issued Executive Order N-33-20 2020, which required all residents of the state of California to shelter in place for all but essential activities such as grocery shopping, retrieving prescriptions from a pharmacy, or caring for relatives. This shelter-in-place order (SIPO), the first such statewide order issued in the United States, was designed to reduce COVID-19 cases and mortality. While the White House Task Force on the Coronavirus has credited the State of California for taking early action to prevent a statewide COVID-19 outbreak, no study has examined its impact. This study is the first to estimate the effect of SIPO adoption on health. Using daily state-level coronavirus data and a synthetic control research design, we find that California’s statewide SIPO reduced COVID-19 cases by 125.5 to 219.7 per 100,000 population by April 20, one month following the order. We further find that California’s SIPO led to as many as 1,661 fewer COVID-19 deaths during this period. Back-of-the-envelope calculations suggest that there were about 400 job losses per life saved during this short-run post-treatment period.

Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis

Abstract: Consider a researcher estimating the parameters of a regression function based on data for all 50 states in the United States or on data for all visits to a website. What is the interpretation of the estimated parameters and the standard errors? In practice, researchers typically assume that the sample is randomly drawn from a large population of interest and report standard errors that are designed to capture sampling variation. This is common even in applications where it is difficult to articulate what that population of interest is, and how it differs from the sample. In this article, we explore an alternative approach to inference, which is partly design‐based. In a design‐based setting, the values of some of the regressors can be manipulated, perhaps through a policy intervention. Design‐based uncertainty emanates from lack of knowledge about the values that the regression outcome would have taken under alternative interventions. We derive standard errors that account for design‐based uncertainty instead of, or in addition to, sampling‐based uncertainty. We show that our standard errors in general are smaller than the usual infinite‐population sampling‐based standard errors and provide conditions under which they coincide.

The Effect of Home-Sharing on House Prices and Rents: Evidence from Airbnb

Abstract: We assess the impact of home-sharing on residential house prices and rents. Using a dataset of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we show that Airbnb has a positive impact on house prices and rents. This effect is stronger in zipcodes with a lower share of owner-occupiers, consistent with non-owner-occupiers being more likely to reallocate their homes from the long- to the short-term rental market. At the median owner-occupancy rate zipcode, we find that a 1% increase in Airbnb listings leads to a 0.018% increase in rents and a 0.026% increase in house prices. Considering the median annual Airbnb growth in each zipcode, these results translate to an annual increase of $9 in monthly rent and $1,800 in house prices for the median zipcode in our data, which accounts for about one fifth of actual rent growth and about one seventh of actual price growth. Finally, we formally test whether the Airbnb effect is due to the reallocation of the housing supply. Consistent with this hypothesis, we find that, while the total supply of housing is not affected by the entry of Airbnb, Airbnb listings increase the supply of short-term rental units and decrease the supply of long-term rental units.

Inflation Expectations and Firm Decisions: New Causal Evidence

Abstract: Author(s): Coibion, O; Gorodnichenko, Y; Ropele, T | Abstract: We use a unique design feature of a survey of Italian firms to study the causal effect of inflation expectations on firms' economic decisions. In the survey, a randomly chosen subset of firms is repeatedly treated with information about recent inflation whereas other firms are not. This information treatment generates exogenous variation in inflation expectations. We find that higher inflation expectations on the part of firms leads them to raise their prices, increase demand for credit, and reduce their employment and capital. However, when policy rates are constrained by the effective lower bound, demand effects are stronger, leading firms to raise their prices more and no longer reduce their employment.

Demographic determinants of testing incidence and COVID-19 infections in New York city neighborhoods

Abstract: New York City is the hot spot of the COVID-19 pandemic in the United States. This paper merges information on the number of tests and the number of infections at the New York City zip code level with demographic and socioeconomic information from the decennial census and the American Community Surveys. People residing in poor or immigrant neighborhoods were less likely to be tested;but the likelihood that a test was positive was larger in those neighborhoods, as well as in neighborhoods with larger households or predominantly black populations. The rate of infection in the population depends on both the frequency of tests and on the fraction of positive tests among those tested. The non-randomness in testing across New York City neighborhoods indicates that the observed correlation between the rate of infection and the socioeconomic characteristics of a community tells an incomplete story of how the pandemic evolved in a congested urban setting.