In this post we analyze consumer beliefs about the duration of the economic impact of the pandemic and present new evidence on their expected spending, income, debt delinquency, and employment outcomes, conditional on different scenarios for the future path of the pandemic. We find that between June and August respondents to the New York Fed Survey of Consumer Expectations (SCE) have grown less optimistic about the pandemic’s economic consequences ending in the near future and also about the likelihood of feeling comfortable in crowded places within the next three months. Although labor market expectations of respondents differ considerably across fairly extreme scenarios for the evolution of the COVID pandemic, the difference in other economic outcomes across scenarios appear relatively moderate on average. There is, however, substantial heterogeneity in these economic outcomes and some vulnerable groups (for example, lower income, non-white) appear considerably more exposed to the evolution of the pandemic.
Total household debt was roughly flat in the second quarter of 2020, according to the latest Quarterly Report on Household Debt and Credit from the New York Fed’s Center for Microeconomic Data. But, for the first time, the dynamics in household debt balances were driven primarily by a sharp decline in credit card balances, as consumer spending plummeted. In an effort to gain greater clarity, the New York Fed and the Federal Reserve System have acquired monthly updates for the New York Fed Consumer Credit Panel, based on anonymized Equifax credit report data. We’ve been closely watching the data as they roll in, and here we present six key takeaways on the consumer balance sheet in the months since COVID-19 hit.
A $20 billion rise in student loan balances in the third quarter of this year contributed to a $92 billion increase in total household debt, according to the latest Quarterly Report on Household Debt and Credit from the New York Fed’s Center for Microeconomic Data. This post explores racial disparities in student loan outcomes using information about the borrowers’ locations, grouping zip codes based upon which racial group constitutes the majority of an area’s residents.