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This edition was written by Sarah Ahmad, Tristan Loa, and David Wessel.
How do Treasury yields respond to increases in federal debt and changes in Treasury maturity? Using high-frequency data on Treasury price movements around auction announcements, Huixin Bi of the Federal Reserve Bank of Kansas City, Maxime Phillot of the Swiss National Bank, and Sarah Zubairy of Texas A&M find that unexpected increases in Treasury supply raise yields across all maturities. A shock that raises the debt-to-GDP ratio by 1 percentage point over two years increases the 10-year Treasury yield by 1.7 basis points on impact and 2.8 basis points at its peak, tightening financial conditions and crowding out private investment. They also find that an unexpected increase in the weighted average maturity of Treasury issuance by one month over two years steepens the yield curve, widening the spread between 10-year and 2-year Treasuries by 1.7 basis points. Unlike debt volume shocks, maturity adjustment shocks lower credit risk premia and fiscal uncertainty—even as they raise long-term yields—supporting near-term investment and production. The effects of both types of shocks depend on the fiscal environment: they are muted when the debt-to-GDP ratio is stable but intensify during periods of rapid debt growth.
Simone Lenzu of the Federal Reserve Bank of New York examines three channels through which artificial intelligence (AI) could influence monetary policy: cyclical transmission, structural transition, and financial stability. In the short run, AI can change production costs, pricing behavior, and expectations in ways that can either raise or lower inflation even when economic slack is unchanged. In the long run, AI may shift potential output and the natural rate of interest in either direction, making policy calibration harder. On financial stability, AI improves credit allocation and risk assessment. But it also may lead many firms to rely on similar AI models, increasing the risk of synchronized behavior, and may make asset valuations highly sensitive to shifts in expectations about future AI gains, both of which can heighten systemic vulnerabilities. The author also notes that a cost-push inflation risk could arise alongside increasing financial fragility because AI could cause stagflation by temporarily depressing productivity while firms adjust to using the technology and simultaneously elevate asset prices—a combination that interest rates alone may not be able to address. The author argues that AI does not require redefining central bank objectives but calls for greater reliance on cost-side diagnostics and less reliance on Phillips curve relationships.
How damaging is a trade war versus a military war or a war of words? Joshua Aizenman of the University of Southern California, Jamel Saadaoui of the University of Paris 8, and Rodolphe Desbordes of SKEMA Business School decompose hostility between countries since 2015 into military conflict, military posturing, sanctions-driven tensions ("trade wars"), and routine diplomacy. They find that military conflict causes immediate and severe trade disruption, while trade wars cause substantial trade damage that builds over time as retaliation unfolds. Routine diplomatic tensions, despite dominating the headlines, have no effect on trade. While military posturing provides early warning of escalation, it occurs too rarely to quantify its effect on trade. The authors estimate that more than $334 billion in trade is at risk due to geopolitical deterioration between 2015 and 2023, with the U.S.-China relationship accounting for half of that. Trade wars account for nearly 97% of those losses, while actual military conflict accounts for just 3%. Finally, the authors document that governments have increasingly substituted economic weapons for military ones over the past decade, with the trade-weighted share of economically driven hostility nearly doubling since 2015.
“The data that’s being used to judge inflation is quite imperfect data. And among the projects that the economics profession—and if I’m confirmed as chairman of the Fed, the Fed needs to do—is to try and use our new understanding and new data sources to see what’s really the inflation rate in the economy,” said Kevin Warsh, nominee for Federal Reserve Chair.
“We used to use core PCE measures (so we’d exclude food and energy) because it was sort of a rough swag as to what was going on. We don't have to do a rough swag anymore. What I’m most interested in is what’s the underlying inflation rate: not what’s the one-time change in prices because of a change in geopolitics or a change in beef, but what’s the underlying generalized change in prices in the economy.
“My broad sense is that these inflation risks and the inflation damage the last several years is improving somewhat… The measures I prefer are looking at things that are called trimmed averages, where we take out all of the tail risks, all of the one-off items, and we ask ourselves whether the generalized change in prices is having second order effects on the economy. Again, they’re not where they should be, but I think that the trend is quite favorable…
“The president never once asked me to commit to any particular interest rate decision, period, and nor would I ever agree to do so if he had. But he never did. I was honored he nominated me. Like everyone else in the committee and in the world, I've heard his view on interest rates. It sounded very similar to me to every other president in economic history that I've studied.”
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