Jaison R. Abel and Richard Deitz
Abel and Deitz look at the outsized impact of the COVID-19 pandemic on some workers, particularly those who are in lower-wage jobs, without a college degree, female, minority, and younger.
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 post yesterday, 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 the use of public transportation and increased 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 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).
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.
Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw
In part I of our analysis, we studied the expected debt relief from the CARES Act on mortgagors and student debt borrowers. We now turn our attention to the 63 percent of American borrowers who do not have a mortgage or student loan. These borrowers will not directly benefit from the loan forbearance provisions of the CARES Act, although they may be able to receive some types of leniency that many lenders have voluntarily provided. We ask who these borrowers are, by age, geography, race and income, and how does their financial health compare with other borrowers.
Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw
COVID-19 and associated social distancing measures have had major labor market ramifications, with massive job losses and furloughs. Millions of people have filed jobless claims since mid-March—6.9 million in the week of March 28 alone. These developments will surely lead to financial hardship for millions of Americans, especially those who hold outstanding debts while facing diminishing or disappearing wages. The CARES Act, passed by Congress on April 2, 2020, provided $2.2 trillion in disaster relief to combat the economic impacts of COVID-19. Among other measures, it included mortgage and student debt relief measures to alleviate the cash flow problems of borrowers. In this post, we examine who could benefit most (and by how much) from various debt relief provisions under the CARES Act.
Rajashri Chakrabarti, William Nober, and Maxim Pinkovskiy
Building upon our earlier Liberty Street Economics post, we continue to analyze the heterogeneity of COVID-19 incidence. We previously found that majority-minority areas, low-income areas, and areas with higher population density were more affected by COVID-19. The objective of this post is to understand any differences in COVID-19 incidence by areas of financial vulnerability. Are areas that are more financially distressed affected by COVID-19 to a greater extent than other areas? If so, this would not only further adversely affect the financial well-being of the individuals in these areas, but also the local economy. This post is the first in a three part-heterogeneity series looking at heterogeneity in the credit market as it pertains to COVID-19 incidence and CARES Act debt relief.
Rajashri Chakrabarti
Following up series on heterogeneity and inequality broadly and in labor market outcomes specifically, we turn our focus to further documenting heterogeneity in credit market outcomes, looking at disparities in home ownership rates, varying exposure to evictions, differing gains from tuition support and Medicare programs, and more.
Rajashri Chakrabarti and William Nober
In this post, we study whether (and how) the spread of COVID-19 across the United States varied by geography, race, income, and population density. Were urban areas more affected by COVID-19 than rural areas? Did population density matter in the spread? Were certain races and income groups affected more by the spread of this deadly coronavirus? Our analysis uncovers stark demographic trends among places affected most severely by the pandemic thus far.
Olivier Armantier, Joelle Scally, Kyle Smith, and Wilbert van der Klaauw
The Federal Reserve Bank of New York released results today from its October 2018 SCE Credit Access Survey, which provides information on consumers’ experiences with and expectations about credit demand and credit access. The survey is fielded every four months and was previously fielded in June.
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