Why Are Credit Card Rates So High?

Credit cards play a crucial role in U.S. consumer finance, with 74 percent of adults having at least one. They serve as the main method of payment for most individuals, accounting for 70 percent of retail spending. They are also the primary source of unsecured borrowing, with 60 percent of accounts carrying a balance from one month to the next. Surprisingly, credit card interest rates are very high, averaging 23 percent annually in 2023. Indeed, their rates are far higher than the rates on any other major type of loan or bond. Why are credit card rates so high? In our recent research paper, we address this question using granular account-level data on 330 million monthly credit card accounts.
Interoperability of Blockchain Systems and the Future of Payments

In a previous post, we introduced a three-pillar framework for interoperability of payment systems and discussed how technological, legal, and economic factors contribute to achieve interoperability and aid in the “singleness of money”—that payments and exchange are not subject to volatility in the value of the money itself—in the context of legacy systems. In this post, we use the framework to characterize the interoperability of blockchain systems and propose a methodology for evaluating interoperability. We show evidence of limited interoperability and draw insights for the future of payment systems.
An Interoperability Framework for Payment Systems

Novel payment systems based on blockchain networks promise to redesign financial architecture, but a notable concern about these systems is whether they can be made interoperable. This concern stems from the concept of the “singleness of money”—that payments and exchange are not subject to volatility in the value of the money itself. Volatility and speculation can arise from the payment medium, which may have speculative characteristics, or from frictions that undermine the ability of one or more payments systems to interoperate. In this two-part series, we outline a framework for analyzing payment system interoperability, apply it to traditional and emerging financial architectures, and relate it to the ability of the payment systems to maintain singleness of money.
Credit Score Impacts from Past Due Student Loan Payments

In our companion post, we highlighted how the pandemic and subsequent policy actions disrupted trends in the growth of student loan balances, the pace of repayment, and the classification of delinquent loans. In this post, we discuss how these changes affected the credit scores of student loan borrowers and how the return of negative reporting of past due balances will impact the credit standing of student loan borrowers. We estimate that more than nine million student loan borrowers will face significant drops in credit score once delinquencies appear on credit reports in the first half of 2025.
Student Loan Balance and Repayment Trends Since the Pandemic Disruption

This month marks five years since the start of the COVID-19 pandemic, after which subsequent policy responses upended most trends underlying student loans in the U.S. Beginning in March 2020, executive and legislative actions suspended student loan payments and the accumulation of interest for loans owned by the federal government. In addition, federal actions marked all past due and defaulted federal student loans as current, driving the delinquency rate on student loans below 1 percent by November 2022. Payments on federal student loans resumed in October 2023 after forty-three months of suspension. This post is the first of two highlighting trends in balances, repayment, and delinquency for student loans since the beginning of the COVID-19 pandemic and how trends may shift without pandemic supports.
The New York Fed DSGE Model Forecast—March 2025

This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since December 2024. As usual, we wish to remind our readers that the DSGE model forecast is not an official New York Fed forecast, but only an input to the Research staff’s overall forecasting process. For more information about the model and variables discussed here, see our DSGE model Q & A.
When the Household Pie Shrinks, Who Gets Their Slice?

When households face budgetary constraints, they may encounter bills and debts that they cannot pay. Unlike corporate credit, which typically includes cross-default triggers, households can be delinquent on a specific debt without repercussions from their other lenders. Hence, households can choose which creditors are paid. Analyzing these choices helps economists and investors better understand the strategic incentives of households and the risks of certain classes of credit.
Firms’ Inflation Expectations Have Picked Up

After a period of particularly high inflation following the pandemic recession, inflationary pressures have been moderating the past few years. Indeed, the inflation rate as measured by the consumer price index has come down from a peak of 9.1 percent in the summer of 2022 to 3 percent at the beginning of 2025. The New York Fed asked regional businesses about their own cost and price increases in February, as well as their expectations for future inflation. Service firms reported that business cost and selling price increases continued to moderate through 2024, while manufacturing firms reported some pickup in cost increases but not price increases. Looking ahead, firms expect both cost and price increases to move higher in 2025. Moreover, year-ahead inflation expectations have risen from 3 percent last year at this time to 3.5 among manufacturing firms and 4 percent among service firms, though longer-term inflation expectations remain anchored at around 3 percent.
Comparing Apples to Apples: “Synthetic Real‑Time” Estimates of R‑Star

Estimates of the natural rate of interest, commonly called “r-star,” garner a great deal of attention among economists, central bankers, and financial market participants. The natural interest rate is the real (inflation-adjusted) interest rate expected to prevail when supply and demand in the economy are in balance and inflation is stable. The natural rate cannot be measured directly but must be inferred from other data. When assessing estimates of r-star, it is important to distinguish between real-time estimates and retrospective estimates. Real-time estimates answer the question: “What is the value of r-star based on the information available at the time?” Meanwhile, retrospective estimates answer the question: “What was r-star at some point in the past, based on the information available today?” Although the latter question may be of historical interest, the former question is typically more relevant in practice, whether in financial markets or central banks. Thus, given their different nature, comparing real-time and retrospective estimates is like comparing apples to oranges. In this Liberty Street Economics post, we address this issue by creating new “synthetic real-time” estimates of r-star in the U.S. for the Laubach-Williams (2003) and Holston-Laubach-Williams (2017) models, using vintage datasets. These estimates enable apples-to-apples comparisons of the behavior of real-time r-star estimates over the past quarter century.