After forty-three months of forbearance, the pause on federal student loan payments has ended. Originally enacted at the onset of the COVID-19 pandemic in March 2020, the administrative forbearance and interest waiver lasted until September 1, 2023, and borrowers’ monthly payments resumed this month. As discussed in an accompanying post, the pause on student loan payments afforded borrowers over $260 billion in waived payments throughout the pandemic, supporting borrowers’ consumption and savings over the last three years. In this post, we analyze responses of student loan borrowers to special questions in the August 2023 SCE Household Spending Survey designed to gauge the expected impact of the payment resumption on future spending growth, the risk of credit delinquency for borrowers, and the economy at large. The findings suggest that the payment resumption will have a relatively small overall effect on consumption, on the order of a 0.1 percentage point reduction in aggregate spending from August levels, and a (delayed) return of student loan delinquency rates back to pre-pandemic levels. Across groups, we see little variation in spending responses but find that low-income borrowers, female borrowers, those with less than a bachelor’s degree, and those who were not in repayment before the pandemic expect the highest likelihood of missed student loan payments.
“Kitchen table” issues were on the minds of our readers in 2022, though what was labeled as such was perhaps a bit broader than in the past. Supply chains—now firmly placed on the radar of Main Street—were the subject of the year’s top post by number of page views and accounted for three of the top five (we’ll consider them as one for this roundup). Student debt forgiveness and inflation were also in the news, drawing readers to our preview of various possibilities for the (subsequently announced) federal student loan forgiveness program and a quarterly update of a New York Fed economic forecast model. Posts on more technical topics were popular as well, including an update on the Federal Reserve’s balance sheet “runoff” and a discussion of stablecoins. Underscoring their broad appeal, the year’s top two posts rank among the top five in the history of Liberty Street, which dates back to 2011. Read on to see which posts resonated most with readers.
On August 24, 2022, the White House released a plan to cancel federal student loans for most borrowers. In April, we wrote about the costs and who most benefits from a few hypothetical loan forgiveness proposals using our Consumer Credit Panel, based on Equifax credit report data. In this post, we update our framework to consider the White House plan now that parameters are known, with estimates for the total amount of forgiven loans and the distribution of who holds federal student loans before and after the proposed debt jubilee.
Today, researchers from the Center for Microeconomic Data released the 2022 Student Loan Update, which contains statistics summarizing who holds student loans along with characteristics of these balances. To compute these statistics, we use the New York Fed Consumer Credit Panel (CCP), a nationally representative 5 percent sample of all U.S. adults with an Equifax credit report. For this update, we focus on individuals with a student loan on their credit report. The update is linked here and shared in the student debt section of the Center for Microeconomic Data’s website. In this post, we highlight three facts from the current student loan landscape.
The pandemic forbearance for federal student loans was recently extended for a sixth time—marking a historic thirty-month pause on federal student loan payments. The first post in this series uses survey data to help us understand which borrowers are likely to struggle when the pandemic forbearance ends. The results from this survey and the experience of some federal borrowers who did not receive forbearance during the pandemic suggest that delinquencies could surpass pre-pandemic levels after forbearance ends. These concerns have revived debates over the possibility of blanket forgiveness of federal student loans. Calls for student loan forgiveness entered the mainstream during the 2020 election with most proposals centering around blanket federal student loan forgiveness (typically $10,000 or $50,000) or loan forgiveness with certain income limits for eligibility. Several studies (examples here, here, and here) have attempted to quantify the costs and distribution of benefits of some of these policies. However, each of these studies either relies on data that do not fully capture the population that owes student loan debt or does not separate student loans owned by the federal government from those owned by commercial banks and are thus not eligible for forgiveness with most proposals. In this post, we use representative data from anonymized credit reports that allows us to identify federal loans, calculate the total cost of these proposals, explore important heterogeneity in who owes federal student loans, and examine who would likely benefit from federal student loan forgiveness.
Federal student loan relief was recently extended through August 31, 2022, marking the sixth extension during the pandemic. Such debt relief includes the suspension of student loan payments, a waiver of interest, and the stopping of collections activity on defaulted loans. The suspension of student loan payments was expected to help 41 million borrowers save an estimated $5 billion per month. This post is the first in a two-part series exploring the implications and distributional consequences of policies that aim to address the student debt burden. Here, we focus on the uneven consequences of student debt relief and its withdrawal. With the end-date of the student loan relief drawing near, a key question is whether and how the discontinuation of student debt relief might affect households. Moreover, will these effects vary by demographics?
The onset of the COVID-19 pandemic brought substantial financial uncertainty for many Americans. In response, executive and legislative actions in March and April 2020 provided unprecedented debt relief by temporarily lowering interest rates on Direct federal student loans to 0 percent and automatically placing these loans into administrative forbearance. As a result, nearly 37 million borrowers have not been required to make payments on their student loans since March 2020, resulting in an estimated $195 billion worth of waived payments through April 2022. However, 10 million borrowers with private loans or Family Federal Education Loan (FFEL) loans owned by commercial banks were not granted the same relief and continued to make payments during the pandemic. Data show that Direct federal borrowers slowed their paydown, with very few making voluntary payments on their loans. FFEL borrowers, who were not covered by the automatic forbearance, struggled with their debt payments during this time. The difficulties faced by these borrowers in managing their student loans and other debts suggest that Direct borrowers will face rising delinquencies once forbearance ends and payments resume.
Household debt has risen markedly since 2013 and amounts to more than $15 trillion dollars. While the aggregate volume of household debt has been well-documented, literature on the gender, racial and education distribution of debt is lacking, largely because of an absence of adequate data that combine debt, demographic, and education information. In a three-part series beginning with this post, we seek to bridge this gap. In this first post, we focus on differences in debt holding behavior across race and gender. Specifically, we explore gender and racial disparities in different types of household debt and delinquencies—for auto, mortgage, credit card, and student loans—while distinguishing between students pursuing associate’s (AA) and bachelor’s (BA) degrees. In the second post in this series, we investigate gender and racial disparities in delinquencies across these various kinds of consumer debt. We close with a third post where we try to understand some of the mechanisms behind differences in debt and delinquencies across gender and race.
Today, the New York Fed’s Center for Microeconomic Data reported that household debt balances increased by $206 billion in the fourth quarter of 2020, marking a $414 billion increase since the end of 2019. But the COVID pandemic and ensuing recession have marked an end to the dynamics in household borrowing that have characterized the expansion since the Great Recession, which included robust growth in auto and student loans, while mortgage and credit card balances grew more slowly. As the pandemic took hold, these dynamics were altered. One shift in 2020 was a larger bump up in mortgage balances. Mortgage balances grew by $182 billion, the biggest uptick since 2006, boosted by historically high volumes of originations. Here, we take a close look at the composition of mortgage originations, which neared $1.2 trillion in the fourth quarter of 2020, the highest single-quarter volume seen since our series begins in 2000. The Quarterly Report on Household Debt and Credit and this analysis are based on the New York Fed’s Consumer Credit Panel, which is itself based on anonymized Equifax credit data.
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 groups, as evident when we introduce proxies for demographic group using the income and racial composition of the students’ home neighborhoods of origin.