Research Database

Workers are unequal in the face of the COVID-19 pandemic: Those who work in essential sectors face higher health risk whereas those in non-essential social-consumption sectors face greater economic risk. We study how these health and economic risks cascade into other sectors through supply chains and demand linkages. In the U.S., we find the cascading effects account for about 25-30% of the exposure to both risks. The cascading effect increases the health risk faced by workers in the transportation and retail sectors, and it increases the economic risk faced by workers in the textile and petroleum sectors. We provide sectoral estimates of the health and economic risk for 42 other countries in an online interactive document.

Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. We show that these data cover broad sections of the US population and exhibit movement patterns similar to conventional survey data. We 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. We use these indices to document how pandemic-induced reductions in activity vary across people and places.

We offer causal evidence of higher returns to experience in big cities. Exploiting a natural experiment that settled refugees across labor markets in Denmark between 1986 and 1998, we find that refugees initially earn similar wages across locations. However, those placed in Copenhagen exhibit 35% faster wage growth with each additional year of experience. Faster sorting of workers towards the type of establishments, occupations, and industries typically found in cities accounts for the vast majority of this urban wage growth premium.

This paper uses a unique large-scale survey administered in April 2020 to assess disparities on several dimensions of wellbeing under rising COVID-19 infections and mitigation restrictions in the US. The survey includes three modules designed to assess different dimensions of well-being in parallel: physical health, mental and social health, and economic and financial security. The survey is unique among early COVID-19 data efforts in that provides insight on diverse dimensions of wellbeing and for subnational geographies. I find dramatic declines in wellbeing from pre-COVID baseline measures across both people and places. Place-level variation is not well explained by local characteristics that either precede or coincide with the pandemic. Analysis by demographic groups also shows large and unequal declines in wellbeing in the COVID era. Hispanic, younger, and lower-earning individuals all faced disproportionately worsening economic conditions, as did those with school-aged children. I conclude that place-based relief policies are unlikely to be efficient relative to support targeted to the neediest individuals. I also find that individual COVID-19 exposure and risk show concerning relationships with employment, protective behavior, and mental health. Those with direct COVID-19 exposure through their households continue working similar hours to others, and those with recent fever symptoms or elevated risk for COVID complications are not reducing their work hours or taking additional precautions, despite negative mental health status changes indicating concern. These findings suggest that some support policies might be directly targeted to households with confirmed infections or heighted risk.

We document new facts about risk in male wages and earnings, household earnings, and pre- and post-tax income in the Netherlands and the United States. We find that, in both countries, earnings display important deviations from the typical assumptions of linearity and normality. Individual-level male wage and earnings risk is relatively high at the beginning and end of the working life, and for those in the lower and upper parts of the income distribution. Hours are the main driver of the negative skewness and, to a lesser extent, the high kurtosis of earnings changes. Even though we find no evidence of added-worker effects, the presence of spousal earnings reduces the variability of household income compared to that of male earnings. In the Netherlands, government transfers are a major source of insurance, substantially reducing the standard deviation, negative skewness, and kurtosis of income changes. In the U.S. the role of family insurance is much larger than in the Netherlands. Family and government insurance reduce, but do not eliminate nonlinearities in household disposable income by age and previous earnings in either country.

I examine the impact of the COVID-19 shock on parents' labor supply during the initial stages of the pandemic. Using difference-in-difference estimation and monthly panel data from the Current Population Survey (CPS), I compare labor market attachment, non-work activity, hours worked, and earnings and wages of those in areas with early school closures and stay-in-place orders with those in areas with delayed or no pandemic closures. While there was no immediate impact on detachment or unemployment, mothers with jobs in early closure states were 68.8 percent more likely than mothers in late closure states to have a job but not be working as a result of early shutdowns. There was no effect on working fathers or working women without school age children. Mothers who continued working increased their work hours relative to comparable fathers; this effect, however, appears entirely driven by a reduction in fathers’ hours worked. Overall, the pandemic appears to have induced a unique immediate juggling act for working parents of school age children. Mothers took a week of leave from formal work; fathers working fulltime, for example, reduced their hours worked by 0.53 hours over the week. While experiences were different for mothers and fathers, each are vulnerable to scarring and stunted opportunities for career growth and advancement due to the pandemic.

In this paper, we ask how bankruptcy law affects the financial decisions of corporations and its implications for firm dynamics. According to current U.S. law, firms have two bankruptcy options: Chapter 7 liquidation and Chapter 11 reorganization. Using Compustat data, we first document capital structure and investment decisions of non-bankrupt, Chapter 11, and Chapter 7 firms. Using those data moments, we then estimate parameters of a general equilibrium firm dynamics model with endogenous entry and exit to include both bankruptcy options. Finally, we evaluate a bankruptcy policy change similar to one recommended by the American Bankruptcy Institute that amounts to a "fresh start" for bankrupt firms. We find that changes to the law can have sizable consequences for borrowing costs and capital structure which via selection affects productivity, as well as long run welfare.

Sudden Stops are financial crises defined by a large, sudden current-account reversal. They occur in both advanced and emerging economies and result in deep recessions, collapsing asset prices, and real exchange-rate depreciations. They are preceded by economic expansions, current-account deficits, credit booms, and appreciated asset prices and real exchange rates. Fisherian models (i.e. models with credit constraints linked to market prices) explain these stylized facts as an outcome of Irving Fisher's debt-deflation mechanism. On the normative side, these models feature a pecuniary externality that provides a foundation for macroprudential policy (MPP). We review the stylized facts of Sudden Stops, the evidence on MPP use and effectiveness, and the findings of the literature on Fisherian models. Quantitatively, Fisherian amplification is strong and optimal MPP reduces sharply the size and frequency of crises, but it is also complex and potentially time-inconsistent, and simple MPP rules are less effective. We also provide a new MPP analysis incorporating investment. Using a constant debt-tax policy, we construct a crisis probability-output frontier showing that there is a tradeoff between financial stability and long-run output (i.e., reducing the probability of crises reduces long-run output).

We develop and calibrate a search-theoretic model of the labor market in order to forecast the evolution of the aggregate US labor market during and after the coronavirus pandemic. The model is designed to capture the heterogeneity of the transitions of individual workers across states of unemployment and employment and across different employers. The model is designed also to capture the trade-offs in the choice between temporary and permanent layoffs. Under reasonable parametrizations of the model, the lockdown instituted to prevent the spread of the novel coronavirus is shown to have long-lasting negative effects on unemployment. This is because the lockdown disproportionately disrupts the employment of workers who need years to find stable jobs.

Social learning plays an important role in models of productivity dispersion and long-run growth. In economies with a continuum of producers and unbounded productivity distributions, social learning can sometimes leave long-run growth rates completely indeterminate. This paper modifies a model in which potential entrants attempt to imitate randomly selected incumbent firms by introducing an upper bound on how much entrants can learn from incumbents. When this upper bound is taken to infinity, a unique long-run growth rate emerges, even though the economy without upper bound has an unbounded continuum of balanced growth rates.