David Dam, Meghana Gaur, Fatih Karahan, Laura Pilossoph, and Will Schirmer



The ongoing COVID-19 pandemic and the various measures put in place to contain it caused a rapid deterioration in labor market conditions for many workers and plunged the nation into recession. The unemployment rate increased dramatically during the COVID recession, rising from 3.5 percent in February to 14.8 percent in April, accompanied by an almost three percentage point decline in labor force participation. While the subsequent labor market recovery in the aggregate has exceeded even some of the most optimistic scenarios put forth soon after this dramatic rise, the recovery has been markedly weaker for the Black population. In this post, we document several striking differences in labor market outcomes by race and use Current Population Survey (CPS) data to better understand them.

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy



The introduction of numerous social distancing policies across the United States, combined with voluntary pullbacks in activity as responses to the COVID-19 outbreak, resulted in differences emerging in the types of work that were done from home and those that were not. Workers at businesses more likely to require in-person work—for example, some, but not all, workers in healthcare, retail, agriculture and construction—continued to come in on a regular basis. In contrast, workers in many other businesses, such as IT and finance, were generally better able to switch to working from home rather than commuting daily to work. In this post, we aim to understand whether following the onset of the pandemic there was a wedge in the incidence of commuting for work across income and race. And how did this difference, if any, change as the economy slowly recovered? We take advantage of a unique data source, SafeGraph cell phone data, to identify workers who continued to commute to work in low income versus higher income and majority-minority (MM) versus other counties.

Jaison R. Abel and Richard Deitz



As the COVID-19 pandemic took hold in the United States, in just two months—between February and April 2020—the nation saw well over 20 million workers lose their jobs, an unprecedented 15 percent decline. Since then, substantial progress has been made, but employment still remains 5 percent below its pre-pandemic level. However, not all workers have been affected equally. This post is the first in a three-part series exploring disparities in labor market outcomes during the pandemic—and represents an extension of ongoing research into heterogeneities and inequalities in people’s experience across large segments of the economy including access to credit, health, housing, and education. Here we find that some workers were much more likely to lose their jobs than others, particularly lower-wage workers and those without a college degree, as well as women, minorities, and younger workers. However, as jobs have returned during the recovery, many of these differences have narrowed considerably, though some gaps are widening again as the labor market has weakened due to a renewed surge in the coronavirus. The next post in the series examines differences in patterns of commuting during the pandemic, and finds that workers in low-income and Black- and Hispanic-majority communities were more likely to commute for work. The final post in the series analyzes unemployment dynamics during the pandemic, and finds that Black workers experienced a lower job-finding rate and a higher separation rate into unemployment than white workers during the recovery, though this trend has reversed to some extent recently.

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy



This is the fourth and final post in this series aimed at understanding the gap in COVID-19 intensity by race and by income. The previous three posts focused on the role of mediating variables—such as uninsurance rates, comorbidities, and health resource in the first post; public transportation, and home crowding in the second; and social distancing, pollution, and age composition in the third—in explaining the racial and income gap in the incidence of COVID-19. In this post, we now investigate the role of employment in essential services in explaining this gap.

Ruchi Avtar, Raji Chakrabarti, Lindsay Meyerson, and Maxim Pinkovskiy



This is the third post in a series looking to explain the gap in COVID-19 intensity by race and by income. In the first two posts, we have investigated whether comorbidities, uninsurance, hospital resources, and home and transit crowding help explain the income and minority gaps. Here, we continue our investigation by looking at three additional potential channels: the fraction of elderly people, pollution, and social distancing at the beginning of the pandemic in the county. We aim to understand whether these three factors affect overall COVID-19 intensity, whether the income and racial gaps of COVID-19 can be further explained when we additionally include these factors, and whether and to what extent these factors independently account for income and racial gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy




This is the second post in a series that aims to understand the gap in COVID-19 intensity by race and income. In our first post, we looked at how comorbidities, uninsurance rates, and health resources may help to explain the race and income gap observed in COVID-19 intensity. We found that a quarter of the income gap and more than a third of the racial gap in case rates are explained by health status and system factors. In this post, we look at two factors related to indoor density—namely public transportation use and home crowding. Here, we will aim to understand whether these two factors affect overall COVID-19 intensity, whether the income and racial gaps of COVID-19 can be further explained when we additionally include these factors, and whether and to what extent these factors independently account for income and racial gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).

Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy





Our previous work documents that low-income and majority-minority areas were considerably more affected by COVID-19, as captured by markedly higher case and death rates. In a four-part series starting with this post, we seek to understand the reasons behind these income and racial disparities. Do disparities in health status translate into disparities in COVID-19 intensity? Does the health system play a role through health insurance and hospital capacity? Can disparities in COVID-19 intensity be explained by high-density, crowded environments? Does social distancing, pollution, or the age composition of the county matter? Does the prevalence of essential service jobs make a difference? This post will focus on the first two questions. The next three posts in this series will focus on the remaining questions. The posts will follow a similar structure. In each post, we will aim to understand whether the factors considered in that post affect overall COVID-19 intensity, whether the racial and income gaps can be further explained when we additionally include the factors in consideration in that post, and whether and to what extent the factors under consideration in that post independently affect racial and income gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).

Jaison R. Abel, Jason Bram, Richard Deitz, and Jonathan Hastings



The New York-Northern New Jersey region experienced an unprecedented downturn earlier this year, one more severe than that of the nation, and the region is still struggling to make up the ground that was lost. That is the key takeaway at an economic press briefing held today by the New York Fed examining economic conditions during the pandemic in the Federal Reserve’s Second District. Despite the substantial recovery so far, business activity, consumer spending, and employment are all still well below pre-pandemic levels in much of the region, and fiscal pressures are mounting for state and local governments. Importantly, job losses among lower-wage workers and people of color have been particularly consequential. The pace of recovery was already slowing in the region before the most recent surge in coronavirus cases, and we are now seeing signs of renewed weakening as we enter the winter.

Gara Afonso, Marco Cipriani, Steph Clampitt, Haitham Jendoubi, Gabriele La Spada, and Will Riordan

Changes in the distribution of banks’ reserve balances are important since they may impact conditions in the federal funds market and alter trading dynamics in money markets more generally. In this post, we propose using the Lorenz curve and Gini coefficient as a new approach to measuring reserve concentration. Since 2013, concentration, as captured by the Lorenz curve and the Gini coefficient, has co-moved with aggregate reserves, decreasing as aggregate reserves declined (such as in 2015-18) and increasing as aggregate reserves increased (such as at the onset of the COVID-19 pandemic).

Rajashri Chakrabarti, Maxim Pinkovskiy, Will Nober, and Lindsay Meyerson



Does health insurance improve health? This question, while apparently a tautology, has been the subject of considerable economic debate. In light of the COVID-19 pandemic, it has acquired a greater urgency as the lack of universal health insurance has been cited as a cause of the profound racial gap in coronavirus cases, and as a cause of U.S. difficulties in managing the pandemic more generally. However, estimating the effect of health insurance is difficult because it is (generally) not assigned at random. In this post, we approach this question in a novel way by exploiting a natural experiment—the adoption of the Affordable Care Act (ACA) Medicaid expansion by some states but not others—to tease out the causal effect of a type of health insurance on COVID-19 intensity.