In the past year American markets have digested the largest-ever initial public offering (SpaceX), equity raise by a public company (Google) or a foreign firm (SK Hynix), private-funding round (OpenAI) and private-debt deal (Broadcom), as well as most of the biggest bond issue in history (Amazon). There was the first $1trn exchange-traded fund, or ETF (Vanguard), a record-breaking cash pile (Berkshire Hathaway) and stock-buyback programme (Nvidia), and the consummation of the largest-ever leveraged buyout (Electronic Arts). Then there are the enormous mergers in railways (Union Pacific and Norfolk Southern), utilities (NextEra and Dominion Energy) and media (Paramount and Warner Bros Discovery)—the last of which led, this week, to the largest ever junk-bond offering, beating a record set only last week (SoftBank). New markets have been created out of thin air (for compute) or become much bigger (for predictions). Worries about massive corporate scandals (First Brands) and hedge-fund blow-ups (Situational Awareness), which would have once occupied investors’ attention for months, are steamrollered by the relentless, totalising and extraordinarily flexible machine that is American finance.”

An excellent passage, from The Economist.

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In this issue:

“China shock” fragility: According to David Autor, David Dorn, and Gordon Hanson (2013), the “China shock” hit the United States from 1990 to 2007. When corrections by Kirill Borusyak et al. (2022) are fully applied, Joseph Francis argues, the harm of Chinese imports to unemployment, labor force participation, wages, and household incomes disappear. “China shock,” Francis argues, illustrates how the “credibility revolution” very much leaves the con in econometrics. (The commented-on authors are hereby invited to reply in a future issue.)

The predictions did not come true: A June 2023 decision, SFFA v. Harvard, held that race-based admissions are unconstitutional. Thirty-three selective colleges forecast Black enrollment declines of 50 to 70 percent. Fifteen research universities assured the Court that race-neutral admissions “would undercut” the diversity they seek. David Card estimated that eliminating racial preferences would cut the Black share of Harvard’s admitted class from 14 percent to 6 percent. Three years on, David Kane reports that the predictions did not come anywhere close to true.

Science confections: Following up on his critique of Jonathan Pekar et al. in Science, Michael Weissman now criticizes Michael Worobey et al. in Science, who claim that case locations and nucleic acid samples showed that SARS-CoV-2 had first been introduced to humans at the Huanan Seafood Market. Several lines of evidence suggest the introduction to humans was earlier and elsewhere. (The commented-on authors are hereby invited to reply in a future issue.)

The fall of Fama-French and momentum factors: Commenting on Clifford Asness, Eugene Fama, and Kenneth French, Michael O’Connor argues that momentum’s circa-2003 demise and that of market capitalization and the book-to-market ratio about a decade later represent a structural break or ‘regime change.’ (The commented-on authors are hereby invited to reply in a future issue.)

All in the family: Kenneth Judd and Karl Schmedders take issue with an article in Journal of Economic Literature by Jesús Fernández-Villaverde. They say that he overstates the newness of neural-network or “deep learning” solvers and that such solvers should be understood as inside the family of projection and weighted-residual solvers. (The commented-on author is hereby invited to reply in a future issue.)

Is chatbot validation valid? Alexis Akira Toda reports on Gemini, Refine, Claude, and ChatGPT on errors in four published papers in mathematical economics. The chatbots generated false positives—endorsing flawed proofs—and varied in how much guidance they needed to find the error. ChatGPT Pro performed best, Claude was middling, and Gemini worst. Corrections of the original errors had already been published at the time of the experiment, and so a chatbot’s success might reflect its canvassing for published commentary. Since the chatbots generated false positives, however, we know that they are fallible one way or another.

How are so-called digital public goods provided? The United Nations and its Digital Public Goods Alliance have constructed an ambitious framework for certifying, funding, and deploying open-source software in developing countries. The soundness of the UN’s approach is, according to Vidar Andre Utvik and coauthors, illustrated by the rapid development of Covid-19 surveillance tools in Sri Lanka and Norway. Here, John Levendis argues that their own evidence reveals that local professionals cooperated voluntarily and that community innovation preceded WHO guidance, not the reverse. (The commented-on authors are hereby invited to reply in a future issue.)

On Haavelmo’s adoption of Neyman-Pearson theory: Trygve Haavelmo’s (1944) monograph “The Probability Approach in Econometrics” is based on a strict adoption of Neyman-Pearson testing theory. Here, Tom Engsted argues that Haavelmo resorted to the notion of a hypothetical infinite population and a distinctly non-frequentist (Bayesian-like) interpretation of probability with the aim of inductive inference, in direct contradiction with Neyman-Pearson theory.

1939–1945: Housing policy in Denmark: In a critique of the primary literature on the subject, Jens Hansen explains the origins of Danish rent control and other housing regulation. The interventions have wrought great hardship and no discernible benefits. The history exemplifies the intervention dynamic.

Where credit is due: Alexis Akira Toda reports on some curious failures to cite predecessors and brings credit to the slighted authors and papers. (The commented-on authors are hereby invited to reply in a future issue.)

Greening on the edge: An article models sustainable product development under crowdfunding and regulation. Here, Marco Sorge argues that the authors disregard corner solutions for environmental quality, which robustly arise across admissible parameter configurations, and conduct comparative statics where no interior solution exists. A complete characterization of the equilibrium corrects, and in some cases contradicts, their conclusions. (The commented-on authors are hereby invited to reply in a future issue.)

Book-manuscript symposium:
Gregory Clark on genetics and social mobility

The third book in Clark’s trilogy is titled To Have and Have Not: The Determinants of Social Status in England, 1600-2026:

• The book manuscript that commenters received.
• Leaders in genetics and social mobility comment:
  • Rosalind Arden and Robert Plomin
  • John Benning
  • Noah Carl
  • Nikolai Haahjem Eftedal
  • Joseph Francis
  • Charles Murray
  • Jan Stuhler
• Gregory Clark’s reply to commenters.
• Symposium webpage

EJW Audio:

• David Kane on SFFA v. Harvard
• Radu Şimandan and Cristian Valeriu Păun on the Social Justice Agenda in Central Bank Research
• Magnus Henrekson on Europe’s Green Vanities

Notice: Heckscher’s Mercantilism lives! — vol. 1, vol. 2.

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Using administrative data from Scandinavian countries, we provide evidence on international migration responses to wealth taxes and evaluate their aggregate economic implications. We find significant migration responses among the wealthy: A 1 percentage point increase in the top wealth tax rate decreases the stock of wealthy taxpayers by about 2 percent. A large fraction of the wealthy are business owners, and their businesses are negatively affected by owner out-migration. The aggregate effects are nevertheless modest: The migration responses to a 1 percentage point increase in the top wealth tax rate reduce employment by 0.02 percent, investments by 0.07 percent, and value added by 0.10 percent.

That is by Katrine Jakobsen, Henrik Kleven, Jonas Kolsrud, Camille Landais and Mathilde Munoz, in the latest issue of the AER.

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2. New game theory paper on AI races.

3. Scott Sumner movie reviews, please note he is always correct and thus the greatest film critic in the world, at least by that metric.

4. What should the AI safety movement be?

5. The frontier models are beating licensed accountants.

6. “Somewhere between 2 and 6 cents of every dollar of World Bank foreign aid disbursements got siphoned off into crypto wallets…”

7. Imbue Studios.

8. War in Ethiopia returns.

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The statement “AI is normal technology” is three things: a description of current AI, a prediction about the foreseeable future of AI, and a prescription about how we should treat it. We view AI as a tool that we can and should remain in control of, and we argue that this goal does not require drastic policy interventions or technical breakthroughs. We do not think that viewing AI as a humanlike intelligence is currently accurate or useful for understanding its societal impacts, nor is it likely to be in our vision of the future.

AI has responded in video format.

that led Kapoor to create his own diss track:

I had way too much fun using Claude to create a reply video.

– Extremely fun to be able to convert scattered thoughts and ideas into a song + video

– Creating this took <1 hour of my time (in occasionally steering the model in between other things)

– But it took ~10 hours of… https://t.co/7sVmKxhzy6 pic.twitter.com/FmJXpQ0QbX

— Sayash Kapoor (@sayashk) October 1, 2026

Both of these are good, great even, but the sneering in the first video is sublime. Keep in mind that this is almost entirely AI, the script, the music, the lyrics everything. It seems like just yesterday when people said AI could never be creative.

Doesn’t seem like a normal debate to me, regardless of which side you think won.

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Thomas L. Griffiths (born c. 1978) is an Australian academic who is the Henry R. Luce Professor of Information Technology, Consciousness, and Culture at Princeton University. He studies human decision-making and its connection to problem-solving methods in computation. His book with Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions, was named one of the “Best Books of 2016” by MIT Technology Review…

Griffiths released The Laws of Thought: The Quest for a Mathematical Theory of the Mind in 2026. Siobhan Roberts describes it as “a rigorous and captivating account of how cognition can be modeled via three mathematical frameworks: logic, artificial neural networks (“mathematical systems that emulate the operation of the brain”) and probability theory.”

Here is his research page, here is his new book on Amazon, I thought the book was excellent.

So what should I ask him?

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• *Shade*
• Bundesrepublik Deutschland

 

Tyler and Luis start their conversation with Spain — housing, NIMBYism, and the productivity crisis; Spanish literature and why the Civil War still looms so large; and what Chicago taught Luis about party discipline in European politics. Then to the EU’s unanimity problem, capital markets, and Denmark’s flexicurity model; a round of overrated-versus-underrated on Rosalía, Penélope Cruz, and Sgt. Pepper’s; and finally into Messy Jobs — why retraining programs fail, whether AI’s leisure dividend has arrived, and whether Spain could ever achieve AI sovereignty.

Excerpt:

COWEN: Why is there a current productivity crisis in Spain?

GARICANO: Productivity indeed hasn’t grown for three decades, more or less. We’re currently having extensive growth. We’re having immigration, we’re having tourism, but we don’t really have productivity. A lot of it has to do with the political economy. I think if you want to explain the West, not just Spain, you have to understand who is voting and who is this being governed for. Spain is a particularly low-fertility, high life-expectancy country. We have the fifth-lowest fertility and the fifth-highest life expectancy in the world grosso modo, and very high pensions.

Essentially, all the GDP growth we’ve had has gone, 100 percent of the GDP growth we’ve had since 2008 has gone to pensions, to the pensioners. In terms of investment, there is very little in terms of productive investment. We have this fantastic highway network and this high-speed rail network. It’s not really getting the maintenance it needs. Just as one example, the country is basically being governed by and for the older retired people.

COWEN: What does the optimistic scenario look like? You don’t have to predict it’s going to happen, but lay out for me how it could all go well. You would get productivity growth of 1.5 percent a year, and economic growth a bit higher than that.

GARICANO: Spain has amazing fundamentals for the current situation, meaning we could easily be very electricity-energy rich due to solar, wind, and nuclear. We have all this empty space where you could put nuclear plants without much resistance. In fact, we are closing them for political reasons. The energy could be a big advantage. It’s a really amazing place to live. There is no more diverse geography and climate and nature anywhere in Europe, I think, or close to, and beautiful. You could easily see a situation where Spain becomes Florida, or Austin, Texas. Think of Texas. It attracts technology, attracts talent who wants to live there, attracts energy, builds the data centers, et cetera. That scenario is not impossible. The political economy is the tricky part.

COWEN: Doesn’t that mean you actually don’t have good fundamentals? You said you don’t even have a YIMBY movement. Life there really is quite good. I’ve been many times. It’s one of my favorite countries to visit. Isn’t that like a resource curse where there’s no sense of crisis? Old people live for a long time. It’s very comfortable. The weather’s great. The food is amazing. Aren’t those, in fact, liabilities in a time of very rapid change?

And another, on a very different topic:

COWEN: On Messy Jobs, your new and excellent book, you argue very persuasively, “In my view, AI will not lead to anything like mass unemployment, maybe not even to a rise in unemployment, because jobs will become messier and the AIs won’t be able to do them.” Is that a fair description of part of your argument?

GARICANO: I think that’s completely right. Yes.

COWEN: Now, I agree with you, but I think my worry is the opposite, that I know a lot of people, they want simple jobs, they’re okay with some measure of tedium, and that if most jobs become messy jobs, for them, that’s quite stressful, and they’re upset and disoriented. Do you worry about that?

GARICANO: I think that we will have to have a tolerance for human relations. Is that what your friends don’t want? We have a tolerance for relational work that is complex, that has politics in it, that has a big human component, and that’s the part that is going to stay. If people just want to be in their desk typing away, I think two good ways to think about it is work from home and outsourcing. If you think of which jobs were offshored, let me say offshored, in the big offshoring wave to India, those are jobs that are not messy. They’re clean. The company specifies the jobs. They say, “Okay, we can specify this job perfectly. Let’s ship you up.”

Those jobs are the same exact ones that are under complete threat of disruption. A lot of the work from home, when it doesn’t involve a lot of submittings, I guess, has the same feature. Those jobs are clean, single-task, and very often verifiable. You can just see how the performance is going and have your RL, your reinforcement learning loop work on those. I think those are gone.

The messy component that I want to emphasize, many people will say, “Oh, AI will tend to the messiness as well.” I think that there is some messiness that is contingent and that AI can streamline, but there is a lot of messiness that is both relational and has to do with a deeper aspect of the knowledge problem that doesn’t really go away. As AI advances, many aspects of Hayek, Polanyi, and all these knowledge problems are still there.

COWEN: How much retraining will be required and how frequent will that retraining have to be? If I think of me working with agents, I have to retrain myself every month or two. That’s difficult for me. It’s not stressful given my position, but I can imagine it would be stressful. Can we really just put a big chunk of the labor force through that?

Definitely recommended.

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Singapore has launched a dating platform, the latest social-engineering experiment by the city-state’s government to tackle its fast-declining fertility rate.

The initiative, known as FirstDate, opened under a pilot scheme this month for public sector employees aged 21-35 and uses a Nobel Economics Prize-winning matchmaking algorithm. An additional tool suggests date activities and allows users — who receive only one match at a time — to rate their experience in a survey.

The platform is the product of the annual hackathon held by the Singapore government’s technology agency earlier this year.

“FirstDate started with a question among a group of GovTech officers: does having more potential matches necessarily make it easier to find a suitable match?” the website said.

The app, which joins a crowded field of dating apps as well as more bespoke matchmaking services, is Singapore’s latest effort to reverse its falling birth rate, which has made the city-state one of the world’s fastest-ageing countries.

Here is more from Owen Walker at the FT.

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2. Gross output signals an economic surge (WSJ).

3. Echo, a new AI site to mimic the styles of particular writers or writing styles.  Thread on it here.

4. “Open USD (OUSD), the new stablecoin from Coinbase, Mastercard, Stripe, Visa and others launches…”

5. Are men or women more tolerant of differing views?

6. On AI desires.

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Moxie Marlinspike is an American entrepreneur, cryptographer, and computer security researcher. Marlinspike is the creator of Signal, co-founder of the Signal Technology Foundation, and served as the first CEO of Signal Messenger LLC. He is also a co-author of the Signal Protocol encryption used by Signal, WhatsApp, Google Messages, Facebook Messenger and Skype.

There is much more at the link, for instance he is also an anarchist of some kind or another.  So what should I ask him?

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Here is the link.

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We introduce an open-source workflow that enables an LLM to reproduce, improve, and extend an economics article using the article’s published replication package. First, the workflow attempts to reproduce the original calculations, checks for discrepancies with published findings, and performs automated sensitivity analysis. Across 4,452 published replication packages for five economics journals, the workflow flags discrepancies in 3,460 articles or their appendices. Second, the workflow improves the original calculations by using a different implementation or algorithm. In 496 articles, the workflow is able to reduce a calculation’s computation time, at similar or greater accuracy, by more than a factor of 10. Third, the workflow extends the original analysis. In 923 articles, the workflow develops an extension that does not appear in the original article and that is aligned with the original article’s goals and assumptions.

That is from a new paper by Matthew Schwartz, Isaiah Andrews & Jesse M. Shapiro.  At some point, in some cases, the paper will just fade into the backround…

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Police in Singapore have charged a man who is accused of posting an AI-generated image of a saltwater crocodile in a popular reservoir.

Ye Lin was charged with communicating a false message and obstructing the course of justice for allegedly deleting the picture and the application he used.

The fake image caused public concern, authorities allege. The national water agency suspended its work at the city-state’s largest reservoir for two days last month after receiving information that a crocodile had been spotted.

Here is the full story, via Kyle.

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2. We will tell children nasty stories, but mostly only show them positive images.

3. Deregulation in Idaho.

4. Weather risk is reflected in Florida home prices.

5. Have we discovered where Aristotle taught Alexander the Great?

6. How to keep an agent swarm on track?

7. What the mathematicians want.

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that their graduates earn more than the typical high school diploma holder…[and] graduate programs will be required to demonstrate that their graduates earn more than the typical bachelor’s degree holder. (emphasis added).

Think about how low that bar is. The comparison group for an undergraduate program is working adults aged 25-34 with nothing more than a high school diploma. A college program that can’t beat that has almost certainly made its students worse off. For graduate programs the bar is the lowest of several bachelor’s benchmarks, including bachelor’s holders in the same field. A master’s in social work need only beat people with a bachelor’s in social work. A program must also fail in two out of three years before it loses loan eligibility. The Department estimates that about 5% of programs will fail in the first year.

I mocked the term “predatory lending” when it first became common in the financial crisis but in this case predatory lending fits the bill because the real borrower isn’t the individual student. Under income-driven repayment, the taxpayer is a forced co-signer, and it’s the taxpayer who gets predated.

Most expansions of the student loan program have been motivated by the picture of an enterprising student who works hard and wants to major in mechanical engineering or nursing but because of their poor circumstances they can’t afford college. “Credit constraints, asymmetric information, you can’t collateralize human capital,” said the economists. Nice theory, what’s the practice?

The economists wanted loans for good investments and insurance against bad luck but the economists can’t swing the vote and once the government is lending, colleges want more tuition money and students want more forgiveness. The result is a subsidy for programs whose graduates are never likely to repay. As Looney and Yannelis document:

Starting in the late 1990s, policymakers weakened regulations that had constrained institutions from enrolling aid-dependent students. This led to rising enrollment of relatively disadvantaged students, but primarily at poor-performing, low-value institutions whose students systematically failed to complete a degree, struggled to repay their loans, defaulted at high rates, and foundered in the job market. As these new borrowers experienced similarly poor outcomes, their loans piled up, loan performance deteriorated, and with it the finances of the federal program.

Indeed, the program worked in reverse of what was promised. The biggest subsidies went to programs whose graduates were least able to repay, rather than programs with the strongest case for public support. As I wrote earlier:

Looney does a back of the envelope calculation and estimates that typical graduates in Mechanical Engineering will on average get a 0% subsidy but graduates in Music will get a 96% subsidy, in Drama a 99% subsidy and Masseuses a 100% subsidy on average. This of course is exactly the wrong approach. If we are going to subsidize, we should subsidize degrees with plausible positive spillovers not masseuses.

The courts later blocked Biden’s Save plan but the problem is built into income-driven repayment. If music, drama and masseuses are promised a 95%+ subsidy who is paying? The taxpayers. Moreover, it’s even worse than this because the very existence of these loans incentivizes the creation of expensive, useless programs. It’s not just the drama colleges, however. Not surprisingly, the law schools have proven adept at using Public Service Loan Forgiveness (PSLF) to rip off the taxpayer. The school raises tuition, then covers the student’s small income-driven payments for ten years, and the taxpayer forgives the rest. In short, protecting students from the cost of failure rewards colleges for producing it.

Fortunately, the same bill limiting loans ended Grad PLUS loans and capped graduate borrowing. You can see the logic: if taxpayers are going to insure the loans, they need some say over which programs qualify and how much is borrowed. I don’t like giving government that power, but this is the Mises–Higgs intervention ratchet in action: subsidize the loans, absorb the losses, then regulate the programs to limit the losses.

My ideal program would get the government out of the student loan business altogether but until then this is a good first step at limiting one of the most expensive and wasteful programs of the federal government.

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