Jonathan McCarthy



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).

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).

Anna Snider



An annual tradition at Liberty Street Economics is to present our most‑read posts of the year. Given the events of 2020, New York Fed economists and guest coauthors focused their analysis on the effects of the coronavirus pandemic, writing some seventy articles since March on the subject. Our leading posts, in terms of traffic, all touch on the theme in some way. Consider this space a hub for COVID-19 coverage for some time to come, and take a look back at the top five posts grabbing attention in 2020.

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.

Kristian S. Blickle and João A. C. Santos




Leading up to the COVID-19 outbreak, there were growing concerns about corporate sector indebtedness. High levels of borrowing may give rise to a “debt overhang” problem, particularly during downturns, whereby firms forego good investment opportunities because of an inability to raise additional funding. In this post, we show that firms with high levels of borrowing at the onset of the Great Recession underperformed in the following years, compared to similar—but less indebted—firms. These findings, together with early data on the revenue contractions following the COVID-19 outbreak, suggest that debt overhang during the COVID-recession could lead to an up to 10 percent decrease in growth for firms in industries most affected by the economic repercussions of the battle against the outbreak.

Kristian Blickle, Matteo Crosignani, Fernando Duarte, Thomas Eisenbach, Fulvia Fringuellotti, and Anna Kovner

The COVID-19 pandemic has led to significant changes in banks’ balance sheets. To understand how these changes have affected the stability of the U.S. banking system, we provide an update of four analytical models that aim to capture different aspects of banking system vulnerability.

Matthew Higgins and Thomas Klitgaard



Workers' remittances—funds that migrants send to their country of birth—are an important source of income for a number of economies in Latin America, with the bulk of these funds coming from the United States. Have these flows dried up, given the COVID-19 recession and resulting unprecedented job losses? We find that remittances initially faltered but rebounded in the summer months, performing better than during the last U.S. recession despite more severe job losses. Large government income support payments probably explain some of this resilience. Whether remittances continue to hold up is likely to depend on how quickly the U.S. job market recovers, particularly in hard-hit service industries.