Announcing the Disability Records Project

Did you know that we have access to digital copies of the historical US census rolls? You can also find the digitized data at IPUMS. However, the data for people with disabilities is not great. It depends on the year, but those data have error rates on the order of 20% or higher.  We have the digital census rolls, the data just doesn’t match them.

So, I created a non-install windows computer application that lets people identify disabled people on those digital census rolls. Complemented with machine learning, my goal is to improve the accuracy of historical records about people with disabilities. Historical and quantitative research about disabled populations is relatively thin. We can do better. If you have students who would benefit from this research experience, then do please let me know! I can approve your institution’s email domain and we can get started.

The application is really straightforward with basically two user-facing features.

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CBO Wants Your Research

The Congressional Budget Office released a series of posts explaining questions they have about major federal budget policies that past research does not adequately answer. For any economist looking for paper ideas or for a way to influence policy, CBO’s posts are a great place to look:

A Call for New Research on the No Surprises Act

A Call for New Research in the Area of Permitting Requirements for Investments in Physical Infrastructure

A Call for New Research in the Area of Spending on Medicare Part D

A Call for New Research in the Area of Nutritional Standards in SNAP

A Call for New Research on Energy and the Environment

A Call for New Research in the Area of Finance

A Call for New Research in the Area of Health

A Call for New Research in the Area of Labor

A Call for New Research in the Area of Macroeconomics

A Call for New Research in the Area of National Security

A Call for New Research in the Area of Hepatitis C

A Call for New Research in the Area of New Drug Development

A Call for New Research in the Area of Obesity

A Call for New Research in the Area of Taxes and Transfers

At AEAs this year Heidi Williams emphasized how huge bills like permitting reform are being discussed by Congress without much research to inform key aspects of the bills, so CBO & some Congresspeople would genuinely like to see your work on these questions if it is well done

This also your regular reminded that I maintain a page of economics paper ideas. Until now all the ideas there have been my own, but I will be adding links to pages where others share their own paper ideas, starting with CBO’s.

Seven-Year Grocery Inflation is Running High

A recent poll tells us that 66 percent of Americans think groceries are unaffordable. Is this a reasonable position? Let’s add some context.

Figure 1 is one way to look at the problem. It shows the cumulative 7-year inflation rate for groceries, going back almost 100 years. 7 years is an arbitrary time period, but I think it makes sense: it can reasonably be described as “recent memory”; right now it encapsulates the period going back about 6 months before the pandemic; and it has a few time periods of around 100% grocery inflation and a few with close to 0%.

Figure 1

First things first: grocery price deflation over a 7-year time horizon is highly unusual. The only time it happened was the 1930s, a time when you had general price deflation and groceries followed that pattern. It was also a pretty bad time for the economy and society. I’m not saying you can’t have general food price deflation with a major depression, but it doesn’t show up in the historical record going back over 100 years.

Now to the present: the most recent 7 years look pretty bad. In absolute terms, 33 percent grocery inflation is above the long-run average of 25 percent, and definitely above the average of the last 40 years of 21 percent (for most adults, the past 40 years is as far back as their memory goes in terms of being acutely aware of grocery prices). Yes, there have been a few time periods with higher grocery inflation, notably the two World Wars and the 1970s.

But the really important context is the 7 years prior to the pandemic, when grocery inflation was so low (4-5 percent every 7 years) that it probably felt like 0% to most people. That was the recent experience people had become accustomed to before the pandemic. The only other time since the Great Depression it was that low was the late 1950s through the 1960s — though that 15-year window is bookended by two periods of around 100% grocery inflation!

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Warsh’s Low/No Guidance Approach at Fed Makes Market Participants Nervous – – Which May Be a Good Thing

Under Jerome Powell, a typical FOMC meeting had become almost a market event in itself. Traders didn’t just care about the rate decision. They dissected every word of the statement, every sentence of the press conference, and especially the “dot plot,” looking for clues about where rates might be six months or a year from now. The Fed wasn’t simply setting monetary policy—it was guiding expectations. Markets often moved as much on hints about future decisions as on the decision itself.

The first two FOMC meetings under Kevin Warsh have felt very different. The dot plots are gone. Forward guidance has largely disappeared. Instead of trying to signal the likely path of policy, Warsh has repeatedly stressed that the Fed will respond to incoming data when it arrives, not commit itself to forecasts that could prove wrong. At his latest press conference, he described avoiding forward guidance as “prudent” given current uncertainty, while reminding reporters that “There is no soft or alternative inflation target—only 2%.”

This is a huge change in communication style, which is having real world consequences.

Warsh long argued that forward guidance can box policymakers into decisions based on yesterday’s forecasts instead of tomorrow’s realities. That is, once their tentative plans had been put out in public, there was a psychological bias among Fed members to lock in on those projections, which would inhibit their ability to rationally interact with the most recent data and situation. So now, rather than telling markets what the Fed expects to do, he wants investors to make decisions based on fundamental economic conditions, knowing that the central bank will react only after the facts justify it. At the latest FOMC meeting he said, “Market participants are learning to play the ball, not the referee—and market prices will continue to respond in the direction and magnitude they see fit. This is, in my view, a change for the better—and we are just getting started.”

That approach chips away at what investors have come to call the “Fed Put”—the belief, built up since the 2008 financial crisis, that the central bank will fairly quickly and forcefully step in to support markets whenever things get rough.

If that belief fades, financiers may think twice before taking excessive risks. Leverage becomes more dangerous if there is less confidence that easier monetary policy will quickly arrive to cushion losses. Risk premiums may better reflect actual economic uncertainty rather than expectations of future Fed support. That is the possible good side of Warsh’s more hands-off approach. Ideally, business people will exercise more prudence on their own, lessening the odds of financial catastrophes that would require Fed intervention.

On the other hand, markets hate uncertainty, and less guidance means more volatility around Fed meetings. I think Powell tried to use sheer talking (jaw-boning) as a tool to influence market rates, lessening the need for the Fed to actually employ its blunt instruments there. Warsh seems to have taken that tool off the table.

Also, I think some (not all) the causation for the rise in 30-year Treasury bonds to twenty-year highs, and of home mortgage rates to one-year highs accrues to Warsh. First, by eliminating dot plots and forward guidance, he has increased uncertainty about the future path of policy. Investors can no longer confidently assume the Fed will ease at the first sign of economic weakness. That uncertainty can raise the term premium, pushing long-term yields higher.

Second, if markets believe the “Fed Put” is weaker, they may demand higher yields to hold long-term bonds because they perceive less protection from adverse economic or financial shocks. In other words, investors require more compensation for risk.

Whether today’s higher long-term rates are a healthy reflection of economic realities, or an unhealth drag on growth, is a matter of debate.

Service Industry Exodus and the ACA: Anecdata

Within my social network the exodus from the service industry is now almost complete. Ten years ago I had no fewer than 7 good friends in the restaurant busines, now only one remains (and he is, by his own classification, 40% retired). The reasons were both myriad and similar. The physiscal toll is substantial, the hours long, and lack of weekends, the separation from non-industry people working diametrically opposed schedules. What really keeps pushing people out, however, that seems to tip the scales over and over, is the lack of health insurance consistent across most restaurants. With the expiration of the ACA subsidies driving up premiums for those without an employer pool to participate in, the calculus has shifted. Who’s leaving? Is it just the friends of economists?

No, it’s everyone over 35. It’s not really more complicated than that. They are entering the age where health insurance has a lot more marginal value, so they are leaving. Sometimes for substantial paycuts.

Between the ACA subsidies expiring and ICE enforcement targeting the keep service industry labor pools, the business that make our meals are going to look very, very different. Will they be worse? I guess I can’t say for sure…no, scratch that, I absolutely can. It’s worse. Everything is going to be worse. Younger, less experience, fewer immigrants? Yeah, that’s the formula to make everything worse.

AI Innate Preferences Paper on Arxiv

Please check out my new paper, with Joshua Foster

The Innate Economic Preferences of Language Models (arXiv link)

Abstract: Language models increasingly settle real resource tradeoffs on behalf of principals yet their economic preferences remain unobserved. We demonstrate their generation rule is isomorphic to the random utility model of discrete choice. This allows internal logit scores to structurally identify preferences. Estimating risk attitudes across twelve models in a portfolio task reveals universal but heterogeneous risk aversion. Although models reject strictly dominated options, their elicited preferences fail invariance tests and violate the independence of irrelevant alternatives across varying experimental prompts. Finally, fine tuning establishes that a principal can explicitly engineer a target risk attitude.

I hope you will refer to the manuscript for details, but I will share one picture here. This is panel (a) of Figure 3: Empirical indifference curves for open-weight models mapped over the portfolio space.

In simple language, what the red/blue picture shows is that the Qwen language model is picking the portfolios that offer more money (in expectation, with a distaste for excessive risk). That’s basically what a rational actor should do. We find that the language models make fairly consistent choices and rarely violate the monotonicity requirement for a well-behaved utility function.

How we describe this figure in the paper: “Starting from a base bundle with expected return µ = 10 and risk σ = 30, we sweep over the dense grid of alternative portfolios from our experimental protocol and record the position-corrected logit gap between each grid portfolio and the base. The yellow dashed line overlays the indifference curve implied by the mean-variance structural estimates, and the heatmap colors encode the sign and magnitude of the logit difference, with blue regions preferred to the base and red regions dispreferred. Several patterns emerge from these plots. All six models produce upward-sloping indifference curves, confirming that higher risk must be compensated by higher expected return.”

We think this basic research on behavior is important, for alignment research and for business applications with delegating work to AI agents. The first question to ask, before testing whether we can impose our preferences on AI agents, is whether those agents have preferences at all in a consistent sense.

Suggested citation: Buchanan, J., & Foster, J. (2026). The innate economic preferences of language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.26288

So Many Prime Ministers

There has been a lot of shade thrown at the United Kingdom recently from economists and political scientists. Economic growth has gone down the tubes and there have been six prime ministers over the past decade. As an American, I didn’t really know if that was a lot. The social media says that we’ve had a lot of turnover recently. Is six prime ministers in ten years a lot in the UK’s parliamentary system? I grew up watching Tony Blair on TV for a ten-year stretch. But I had no context for the historical norm or whether there is any precedent. Here I look at the data.

Right now, there are a record number of former prime ministers still living (PM). Prior to the recent spike, the maximum number of living people who had left office was five. Right now in 2026, there that number is nine! And if the current PM, Andy Burnham, follows the recent trend of short stints in office, then they’ll hit ten. As an American, it’s hard for me to imagine having 10 living former presidents. According to the below charts, the British are probably a bit jarred too!

Why So Many?

In last week’s post I noted that we’re tied for the most living former presidents. But truly, the UK’s numbers are what inspired me to look at this topic in the first place. To recap, the number of living ex-executives can be caused by 1) Longer lifespans, 2) Leaving office at a younger age, and 3) More unique executives. In the US, being currently tied for the record is overwhelmingly driven by longer lifespans. What about the UK?

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The Academic Data Project That Turned Into $375 Million

What could be better than creating data so valuable that an institution is happy to host and update it forever, like the Sean Lahman baseball database?

Creating data that sells for $375 million, like the Center for Research in Security Prices. University of Chicago professors assembled this series of finance datasets over decades, starting in 1960 with an effort to track every transaction of every publicly traded security. U Chicago sold CRSP to Morningstar last year for $375 million.

Why could they sell it for so much? It helps to be working in finance, where the willingness to pay is the highest. It also represents 65 years of work from what became a large team that included Nobelists like Eugene Fama. The data was valuable enough to become widely used by key institutions even though CRSP charged for it:

Today, $3 trillion in fund assets are linked to CRSP Market Indexes, including U.S. equity ETFs run by Vanguard, and more than 600 subscribers across 35 countries use CRSP Research Data Products.  

Did U Chicago sell CRSP at the right time? On the one hand, I wonder if this was a fire sale driven by federal grant cuts putting pressure on the U Chicago budget. On the other hand, assembling datasets like this is only going to get easier in the age of AI, so perhaps Chicago sold at the top.

For now though there is still an edge in having restricted datasets that AIs haven’t trained on and can’t access. When I ask myself what advantage my human research assistants have over AIs in 2026, the most obvious answer is that they can legally access restricted databases like CRSP or, in my current case, HeinOnline.

GDP Growth in the Second Quarter: Updated Forecasts

GDP growth data for the second quarter of 2026 comes out tomorrow. As I have been doing for the past several quarters, here is an update on two model forecasts (Atlanta and NY Feds), betting market implied estimates (Kalshi), and an average from a survey of economists (WSJ). Yellow shading indicates which forecast was closest to correct in each quarter (green is if two forecasts were about the same).

In the past two quarters, the WSJ survey has been the best predictor. The Atlanta Fed GDPNow model used to be my favorite, but it has performed pretty poorly in the past 3 quarters. As I have discussed before, an average of the Atlanta Fed and Kalshi was better than any single predictor. I continue to include the NY Fed estimate, even though it seems to be a very terrible predictor, because some people like to talk about it.

The Atlanta Fed, Kalshi, and the WSJ survey are all showing very similar estimates for Q2. If I was a betting man, I would bet on 1.8% for the BEA advance estimate.

What Is So Special About Object-Oriented Programming Languages Like C++ and Python?

I first learned computer programming about 1974, using FORTRAN running on an IBM 360 system that, yes, filled a whole room. And yes, my source code existed in the form of a stack of cards with holes punched in them, which got run through a physical card reader. FORTRAN and similar old-school languages were efficient (b/c computer resources were so constrained) and syntactically simple for solving well-specified problems.

C++ started to become popular in the 1980s, and Java in the 1990s. A big part of their appeal was that they were “object-oriented programming” (OOP) languages. I repeatedly asked my computer-programming professional friends back then to help me understand the difference between OOP and conventional Fortran type programs. They would get misty-eyed and rhapsodize about how their program components were modularized.  I guess I just failed to ask the right questions, because I never could understand why what they were talking about was so very much better or different than a good clean FORTRAN program, where most of the work was compartmentalized into well-defined functions and sub routines.

So I had a good talk with Claude about all this, and achieved enlightenment.. The differences seem to come down to a couple of key concepts:


(1) Data Compartmentalization

 In FORTRAN, you can modularize the data manipulation steps into subroutines, but the data tends to be more in common. Thus, for a very large programs, it is hard to keep some far-distant subroutine from accidentally altering your data. But with OOP, the data and the manipulation methods are “encapsulated” into one airtight thing, so no outside routine can mess with that data.

(2) More Robust Relations Among Chunks of Code

With OOP, there is also a feature called “inheritance”, where some new method can take advantage of an existing method, in a cleaner way than (in the FORTAN world) having a new subroutine call an existing subroutine, which would involve explicitly passing a bunch of parameters back-and-forth (which is very easy to mess up).

For doing fairly straightforward scientific calculations, even big ones, I think FORTRAN is still easier and more efficient. But for modern financial programs, involving millions of lines, written by huge teams of people that cannot all talk to one another, the win goes to OOP. Besides C++ and Java (still popular), in OOP we now have C# (standard for many Windows and gaming applications), and the crowd favorite, Python.


(That’s about it simply as I could put it, without getting long-winded and technical… If you want more details, you can always ask my buddy Claude)