Currency market intervention accomplishes little on its own. Unless a change in monetary or fiscal policy follows, its effects fade within weeks. That is one reason the United States intervenes so rarely. Intervention is effective only when it signals a policy change to come.

Even so, on July 31 the Treasury bought yen alongside Japan's Ministry of Finance. It was the first joint purchase of yen by the two governments since 1998. Two days later, Treasury Secretary Scott Bessent posted on X that the Fed should raise the $60 billion per-counterparty limit on its FIMA repo facility. He wrote that the limit should “be upsized in the coming months.” Shortly thereafter, Japanese authorities announced that they planned to draw on the facility to fund future intervention.

The request raises a serious question about central bank independence. Should the size of the Fed's balance sheet respond to a foreign government's exchange rate objective, at the request of the U.S. Treasury Secretary? We think it should not.

In this post, we describe how a FIMA loan works, explain its impact on the Fed’s balance sheet and compare it to alternative mechanisms for funding currency intervention. Our main message is that raising the cap hands short-term control of the Fed's balance sheet to a foreign government, and it would do so in the least visible way available.

Congress mandates the Federal Reserve to achieve maximum sustainable employment and price stability. Artificial intelligence (AI) affects both. The role of Chair Warsh’s Productivity and Jobs Task Force is to help the Federal Open Market Committee (FOMC) understand those effects. Its charge is to “assess the economic impact of new general-purpose technologies, including artificial intelligence, to inform the Federal Reserve's policy judgments.”

So how should the Task Force address this challenge? More to the point, what does the FOMC need to know about AI? The Task Force should start by conceding that nobody can speak with authority about the size or scope of AI's eventual impact on the economy. The Committee's problem — and by extension the Task Force's — is more manageable:  

• Enumerate a range of plausible paths for the economy.

• Identify the indicators that reveal which path we are on.

• Establish how much the FOMC can trust its usual policy guides as AI advances.

In what follows, we take a very rough first pass at what the Task Force should do — and, more importantly, of what it should be telling the FOMC to do routinely. We start with three paths the economy might take, built on three Karger et al. scenarios for AI progress by 2030. We ask what evidence would distinguish these paths, how soon that evidence could arrive, and how much the FOMC can rely on the usual unobservable policy benchmarks in the meantime.

Every day, Harvard Business School researchers collect the posted prices of roughly 350,000 items sold by five large U.S. retailers and sort them by country of origin. Their tariff tracker showed what the 2025 tariffs did to prices U.S. residents actually pay. It also showed something no official series reports: how the increases split between foreign- and U.S.-made goods. Even within a few days, the increases were not confined to imported goods — weeks before the official series would show anything at all.

Researchers are now exploiting “big data” – broader samples observed at high frequency – to improve inflation measurement. For example, PriceStats tracks more than 500,000 U.S. prices daily compared to the 90,000 prices that the Bureau of Labor Statistics (BLS) field staff collect monthly. The NBER’s Economic Measurement Research Institute is exploring how to use item-level transactions data to value quality changes in retail goods, a source of changing bias in price measurement (see, for example, Cafarella et al.). And the research keeps coming: Cavallo, Lippi, and Miyahara show that the frequency with which firms change prices rises sharply after a large shock, so big shocks pass through to consumer prices faster than standard models assume.

Given all the new data and the rapidly growing body of related research, the Fed's Data Task Force, charged with improving the quality and timeliness of real economic signals, will find no shortage of material. The question is what all of these data can actually do for the Fed. The answer depends on what the FOMC needs.

Federal Reserve Chair Kevin Warsh has announced the broadest reconsideration of U.S. monetary policy since the FOMC adopted a formal 2 percent inflation target in 2012. One of the five task forces launched, the Inflation Frameworks Task Force, is to “revisit how the Federal Reserve understands and responds to the drivers of inflation.” This post takes up how the FOMC responds. Identifying the drivers is largely a measurement problem that we address in later posts.

The review is overdue. Five years of above-target inflation threaten to erode the credibility that the Fed spent several decades building. Drawing on nearly 70 years of Fed history and scholarship, we argue that the Inflation Frameworks Task Force should steer toward a cleaner, well-specified inflation-targeting strategy — and away from the vague strategies that allowed inflation to reach a 40-year record just a few years ago.

On 17 June, Federal Reserve Chair Kevin Warsh established five task forces to reconsider how the Fed operates. They cover communications, balance sheet policy, data, productivity and jobs, and inflation frameworks. Together they amount to the most consequential review of U.S. monetary policy at least since the FOMC adopted a formal inflation target in 2012.

The review is overdue. Five years of above-target inflation are eroding credibility the Fed spent decades building. Meanwhile, mounting threats to Fed independence may have made self-examination harder: any institution under threat is less inclined to ask publicly what needs repair.

This post introduces a series addressed to the five task forces. Three are already up: one on communications and two on the balance sheet (here and here). Four more follow. All three of the existing posts first appeared before the announcement of the task forces, so we have added a postscript to each.

Chair Kevin Warsh arrived at the Federal Reserve last month with an agenda for change in the central bank’s communications. Talk less. Replace granular forward guidance with a durable policy framework. And, according to news reports, scale back or even scrap the dot plot – the policy-rate projections in the quarterly Summary of Economic Projections (SEP), which also collects FOMC participants’ forecasts for inflation, unemployment, and growth.

Chair Warsh’s diagnosis of the Fed’s shortcomings raises a question: if the Fed needs a new, clear policy framework, how does a prescription for saying less make sense? In our view, transparency and communication are not the same thing. Transparency is how much the Fed discloses; communication is whether what it discloses conveys an understanding of its reaction function — the relationship between economic conditions and the path of the policy rate. The two can move in opposite directions — if the extra material is noise, a central bank can disclose more yet communicate less. In that sense, Warsh is right that more talk is not the goal in itself.

But the best remedy for an imperfect policy framework is clearer communication of the actual reaction function. That requires disclosing more of what matters and less of what does not, allowing Congress and the public to hold the Fed accountable for its legal mandate.

Money is a confidence trick. When you tap a card, swipe a phone, or send a wire, you move a claim on a commercial bank. You expect others to accept that claim at par — you send a dollar, euro, pound, or yuan, and they receive one. The claim settles and life goes on.

This apparent simplicity conceals an elaborate architecture. Commercial bank deposits circulate at par with central bank money (reserve liabilities) because a complex legal and institutional framework makes the parity credible. Prudential regulation constrains bank risk-taking. Supervision enforces the constraints. Deposit insurance reduces the incentive to run (or to panic). The lender of last resort keeps solvent banks liquid when private funding evaporates. Settlement in central bank money anchors the payment system. Each element reinforces the others. Start stripping them away and private liabilities like bank deposits will stop functioning as money.

Stablecoin advocates ignore these essential foundations. They point to asset backing — in some cases, with quality determined by legislation like the GENIUS Act — and a programmable ledger, and stop there. That is not enough. General acceptance of private money — digital or otherwise — depends on functions that only a central bank can provide. Tokenized deposits inherit this institutional support; stablecoins do not. That is why tokenized deposits will likely dominate stablecoins outside the crypto ecosystem (see here).

The history of payments is an arms race that pits law enforcement against criminals, with criminals typically maintaining an edge. The newest technology is crypto and stablecoins. Milton Friedman saw them coming. Nearly a decade before Bitcoin launched, he described the technology that stablecoins have become: e-cash that is a digital peer-to-peer bearer instrument. He knew gangsters would make it their own, and they have.

Friedman's observation has a counterpart in an older principle about money. Gresham's Law — an observation associated with the Elizabethan financier Sir Thomas Gresham and later formalized by economists — holds that “bad money” drives out “good money.” In the era of metallic coinage, the rule was simple: spend the lighter coin, keep the heavier.

In this post, we argue that a modified version of Gresham’s Law applies to stablecoins. Stablecoins are private digital tokens that promise to maintain a fixed value — typically one U.S. dollar each — backed by reserve assets whose composition varies by issuer. Users who value anonymity gravitate toward what Gresham might have called the bad money: the coin with weaker oversight that makes identification easier to avoid.

Put differently: in stablecoins, as in metallic coinage, bad money drives out good — where “bad” means less transparent and less certain in value.