The Federal Reserve Bank of New York works to promote sound and well-functioning financial systems and markets through its provision of industry and payment services, advancement of infrastructure reform in key markets and training and educational support to international institutions.
Once a bank grows beyond a certain size or becomes too complex and interconnected, investors often perceive that it is “too big to fail” (TBTF), meaning that if the bank were to become distressed, the government would likely bail it out. In a recent post, I showed that the implicit funding subsidies to systemically important banks (SIBs) declined, on average, after a set of reforms for eliminating TBTF perceptions was implemented. In this post, I discuss whether these subsidies increased again during the COVID-19 pandemic and, if so, whether the increase accrued to large firms in all sectors of the economy.
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.
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.
The Main Street Lending Program was the last of the facilities launched by the Fed and Treasury to support the flow of credit during the COVID-19 pandemic. The others primarily targeted Wall Street borrowers; Main Street was for smaller firms that rely more on banks for credit. It was a complicated program that worked by purchasing loans and sharing risk with lenders. Despite its delayed launch, Main Street purchased more debt than any other facility and was accelerating when it closed in January 2021. This post first locates Main Street in the constellation of COVID-19 credit programs, then looks in detail at its design and usage with an eye toward any future programs.
Andreas I. Mueller, Johannes Spinnewijn, and Giorgio Topa
In addition to its terrible human toll, the COVID-19 pandemic has also caused massive disruption in labor markets. In the United States alone, more than 25 million people lost their jobs during the first wave of the pandemic. While many have returned to work since then, a large number have remained unemployed for a prolonged period of time. The number of long-term unemployed (defined as those jobless for twenty-seven weeks or longer) has surged from 1.1 million to almost 4 million. An important concern is that the long-term unemployed face worse employment prospects, but prior work has provided no consensus on what drives this decline in employment prospects. This post discusses new findings using data on elicited beliefs of unemployed job seekers to uncover the forces driving long-term unemployment.
The coronavirus pandemic and the various measures to address it have led to unprecedented convulsions to the U.S. and global economies. In this post, I examine those extraordinary impacts through the lens of personal consumption expenditures on discretionary and nondiscretionary services, a framework I developed in a 2011 post (and subsequently employed in 2012, 2014, and 2017). In particular, I show that there were exceptional declines in both services categories during the spring; their recoveries, however, have displayed notably different patterns in recent months, with nondiscretionary services expenditures nearly back to their prior level and discretionary services expenditures seemingly stalled well below their pre-pandemic peak.
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).
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