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

Editor’s note: When this post was first published, the columns in the second table were mislabeled; the table has been corrected. (August 19, 9:30 a.m.)



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 series looking at heterogeneity in the credit market as it pertains to COVID-19 incidence and CARES Act debt relief.

Jaison R. Abel and Richard Deitz



Many students are reconsidering their decision to go to college in the fall due to the coronavirus pandemic. Indeed, college enrollment is expected to be down sharply as a growing number of would-be college students consider taking a gap year. In part, this pullback reflects concerns about health and safety if colleges resume in-person classes, or missing out on the “college experience” if classes are held online. In addition, poor labor market prospects due to staggeringly high unemployment may be leading some to conclude that college is no longer worth it in this economic environment. In this post, we provide an economic perspective on going to college during the pandemic. Perhaps surprisingly, we find that the return to college actually increases, largely because the opportunity cost of attending school has declined. Furthermore, we show there are sizeable hidden costs to delaying college that erode the value of a college degree, even in the current economic environment. In fact, we estimate that taking a gap year reduces the return to college by a quarter and can cost tens of thousands of dollars in lost lifetime earnings.

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw



In an October post, we showed the effect of college tuition subsidies in the form of merit-based financial aid on educational and student debt outcomes, documenting a large decline in student debt for those eligible for merit aid. Additionally, we reported striking differences in these outcomes by demographics, as proxied by neighborhood race and income. In this follow-up post, we examine whether and how this effect passes through to other debt and consumption outcomes, namely those related to autos, homes, and credit cards. We find that access to merit aid leads to an immediate but temporary increase in eligible individuals’ consumption in these categories. The increase is followed by a decline in consumption and a reduction in total debt of these types in the longer term. Importantly, there are marked differences in these consumption and debt patterns across income and race groups.

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw



Across the United States, the cost of all types of higher education has been rising faster than overall inflation for more than two decades. Despite rising costs, aggregate undergraduate enrollment rose steadily between 2000 and 2010 before leveling off and dipping slightly to its current level. Rising college costs have steadily increased dependence on student debt for college financing, with many students and parents turning to federal and private loans to pay for higher education. An earlier post in this series reported that borrowers in majority Black areas have higher student loan balances and rates of default than those in both majority white and majority Hispanic areas. In this post, we study how differences in college attendance rates and in the types of colleges attended generate heterogeneity in loan experiences. Specifically, using nationwide data, we analyze heterogeneities in college-going and heterogeneities in student debt and default experiences by college type across individuals living in majority Black, majority Hispanic, and majority white zip codes.

Rajashri Chakrabarti



Average economic outcomes serve as important indicators of the overall state of the economy. However, they mask a lot of underlying variability in how people experience the economy across geography, or by race, income, age, or other attributes. Following our series on heterogeneity broadly in October 2019 and in labor market outcomes in March 2020, we now turn our focus to further documenting heterogeneity in the credit market. While we have written about credit market heterogeneity before, this series integrates insights on disparities in outcomes in various parts of the credit market. The analysis includes a look at differing homeownership rates across populations, varying exposure to foreclosures and evictions, and uneven student loan burdens and repayment behaviors. It also covers heterogeneous effects of policies by comparing financial health outcomes for those with access to public tuition subsidies and Medicare versus those not eligible. The findings underscore that a measure of the average, particularly relating to policy impact, is far from complete. Rather, a sharper picture of the diverse effects is essential to understanding the efficacy of policy.

Rajashri Chakrabarti and Maxim Pinkovskiy



Getting health insurance in America is intimately connected to choosing whether and where to work. Therefore, it should not be surprising that the U.S. health insurance market may influence, and be influenced by, the market for higher education—which itself is closely tied to the labor market. In this post, and the staff report it is based on, we investigate the effects of the largest overhaul of health insurance in the United States in recent decades—the Patient Protection and Affordable Care Act of 2010 (ACA) -- on college enrollment choices.

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw



The rising cost of a college education has become an important topic of discussion among both policymakers and practitioners. At least eleven states have recently introduced programs to make public two-year education tuition free, including New York, which is rolling out its Excelsior Scholarship to provide tuition-free four-year college education to low-income students across the SUNY and CUNY systems. Prior to these new initiatives, New York, had already instituted merit scholarship programs that subsidize the cost of college conditional on academic performance and in-state attendance. Given the rising cost of college and the increased prevalence of tuition-subsidy programs, it’s important for us to understand the effects of such programs on students, and whether these effects vary by income and race. While a rich body of work has studied the effects of merit scholarship programs on educational attainment, the same is not true for the effects on financial outcomes of students, such as debt and repayment. This blog post reports preliminary findings from ongoing work, which is one of the first research initiatives to understand such effects.

Rajashri Chakrabarti and William Nober



Economic analysis is often geared toward understanding the average effects of a given policy or program. Likewise, economic policies frequently target the average person or firm. While averages are undoubtedly useful reference points for researchers and policymakers, they don’t tell the whole story: it is vital to understand how the effects of economic trends and government policies vary across geographic, demographic, and socioeconomic boundaries. It is also important to assess the underlying causes of the various inequalities we observe around us, whether they are related to income, health, or any other set of indicators. Starting today, we are running a series of six blog posts (apart from this introductory post), each of which focuses on an interesting case of heterogeneity in the United States.

Rajashri Chakrabarti, Michelle Jiang, and William Nober



In an earlier post, we studied how educational attainment affects labor market outcomes and earnings inequality. In this post, we investigate whether these labor market effects were preserved across the last business cycle: Did students with certain types of educational attainment weather the recession better?