Category: Current Affairs
The Pernicious Trade Account
The trade accounts are among the most pernicious statistics ever collected. It’s long been remarked, for example, that merely by calling something a “deficit” it seems bad even though a current account deficit is matched by a financial account surplus. Put that issue aside, however, because the real problems are much deeper. The international accounts make it appear that individuals, in their ordinary buying and selling, bind us all in a collective endeavor. The accounts take millions of voluntary, mutually beneficial transactions between individuals and firms and repackage them as a relationship between nations—as if “America” were buying from “China”. Many, many experts get this wrong—not just non-economists who are misled by terms like “deficits.”
Don Boudreaux at Cafe Hayek gives a truly excellent example in replying to a reader who asks:
The USA ran trade deficits for 50 years. Those were offset by foreigners’ investments in the USA. Foreigners expect returns on these investments. Doesn’t it mean Americans eventually have to pay those returns to foreigners?
Don’s answer:
No.
The only Americans who are obliged to pay anything to foreigners are Americans who borrowed money from foreigners. (This number includes U.S. citizens-taxpayers whose government borrowed money from foreigners.) But no such obligation exists for other investments that foreigners made in the U.S. – those other investments being equity investments in the U.S. (for example, foreigners buying a restaurant in Houston), purchases of real estate in the U.S., and holding U.S. dollars.
If, for example, the foreign-owned restaurant in Houston goes bankrupt, the loss is fully borne by its foreign owners; no American is obliged to pay anything on that account to foreigners.Of course, foreigners do expect positive returns on all of their U.S. investments, regardless of form. But with the exception of Americans’ repayment of principal and interest on funds that they borrowed from foreigners, no returns that foreigners earn on their investments in America are paid by Americans. If the foreign-owned restaurant in Houston is profitable, those profits are newly created wealth – wealth that’s created by that restaurant’s foreign owners.
In the international commercial accounts, when the restaurant’s foreign owners realize returns on their restaurant – say, by being paid dividends drawn on that restaurant’s profits – it appears that Americans are paying foreigners. This appearance comes from the fact that dollars flow from the U.S. to abroad, and so are recorded as payments from America to a foreign country or countries. But this appearance is misleading. America, as such, doesn’t pay those returns to the restaurant’s foreign owners. Nor do any flesh-and-blood Americans pay those returns. Those returns, again, are new wealth created by the restaurant’s foreign owners; economically, those returns are paid to the restaurant’s foreign owners by the restaurant’s foreign owners.
But the international commercial accounts mask this economic reality. What appears in the commercial accounts as payments by America to foreign countries are no such thing. This accounting mistakes geography for economic reality. Untold confusion is unleashed by supposing that, just because these dollar-denominated returns are created in the U.S. and then sent abroad to foreigners, these dollar-denominated returns are necessarily paid by Americans to foreigners.
As Don says, the trade accounts commit a kind of category error: they categorize geographic location, a where, and treat it as a who, as if “nations” traded. But nations don’t trade, people trade. This confusion wouldn’t matter too much if the statistics stayed in the back pages of government reports. But they don’t. They land on the front page, they shape policy, and they frame negotiations. When a president claims that “we lost $500 billion” to “crazy trade” with China, he is reading the international accounts as a story about nations in competition. The accounting creates the narrative. the narrative creates the policy. Bad accounting leads to bad policy. We would, in fact, all be better off if the trade accounts simply disappeared.
From the UAE
Under the directives of the President of the UAE, we launch a new government model.
Within two years, 50% of government sectors, services, and operations will run on Agentic AI, making the UAE the first government globally to operate at this scale through autonomous systems.
AI is no longer a tool. It analyses, decides, executes, and improves in real time. It will become our executive partner to enhance services, accelerate decisions, and raise efficiency.
This transformation has a clear timeline. Two years. Performance across government will be measured by speed of adoption, quality of implementation, and mastery of AI in redesigning government work.
We are investing in our people. Every federal employee will be trained to master AI, building one of the world’s strongest capabilities in AI-driven government.
Implementation will be overseen by Sheikh Mansour bin Zayed, with a dedicated taskforce chaired by Mohammad Al Gergawi driving execution.
The world is changing. Technology is accelerating. Our principle remains constant. People come first. Our goal is a government that is faster, more responsive, and more impactful.
Here is the link. While there is typically a certain amount of PR in such pronouncements, I do not think this one is only PR.
Those old factory sector jobs
As AI sweeps into white-collar workplaces, old-timey hands-on jobs are getting a new look—and some of those professions even have shortages.
Consider tailors. Sewing is a vanishing skill, much like lacemaking and watchmaking, putting tailors in short supply when big retailers like Nordstrom and Men’s Wearhouse, as well as fashion designers and local dry cleaners, say they need more of them.
The job, which can take years to master, can be a tough sell to younger generations more accustomed to instant gratification. But apprenticeships that offer pay to learn on the job and new training programs are helping entice more people…
For the first semester of its program, which concluded in December, FIT received more than 190 applications for 15 spots. The nine-week course requires prior sewing experience. Nordstrom hired seven students from the inaugural class.
“It’s increasingly becoming more challenging to find people to fill these alterations jobs,” said Marco Esquivel, the director of alterations and aftercare services at Nordstrom, which employs about 1,500 tailors. Similar to other high-end retailers, Nordstrom offers free basic tailoring for garments purchased at the department-store chain and charges a fee for those bought elsewhere.
Tailored Brands, which employs about 1,300 tailors at its Men’s Wearhouse, Jos. A. Bank and other chains, is updating its apprenticeship program to include more self-guided videos with the goal of moving people through the training faster.
Here is more from Suzanne Kapner at the WSJ. Via LJ Fenkell.
The Luddites Were the First to Attack AI
Everyone knows the Luddites smashed looms. What is less appreciated is that the loom was the first serious programmable device — the direct ancestor of the computer. Thus, the Luddites weren’t just the first to resist automation. They were in some ways the first to attack AI.
The Jacquard loom, introduced in France circa 1805, used a chain of punched cards to control which threads were raised for each pass of the shuttle. The ability to change the pattern of the loom’s weave by simply changing cards was an important conceptual precursor to computer programming. Babbage borrowed the idea directly for the Analytical Engine in the 1830s.
The Luddites lost–they were violently suppressed by the UK military–but more generally they lost because programmable looms brought patterned clothes to the masses.
Prior to its invention, the creation of complex patterns required skilled and labour-intensive manual labour, often involving large teams of weavers. With the Jacquard loom, a single operator could control the machine and produce intricate designs with relative ease.
This innovation greatly increased the speed and efficiency of textile production. It also opened up new possibilities for creativity and design, as the loom enabled the production of intricate patterns that were previously unattainable. The Jacquard loom contributed to the democratization of textile manufacturing, making intricate fabrics accessible to a wider audience
By the time Jacquard died in 1834, thousands of his looms were operating in Manchester, an epi-center of the Luddites riots. Moreover, just over 100 years later, Manchester birthed the Manchester Baby and the Manchester Mark 1, the first electronic stored-program computer. And who was hired to program the latter? None other than Alan Turing.
Ada Lovelace had foretold it all beautifully: “the Analytical Engine weaves algebraical patterns just as the Jacquard-loom weaves flowers and leaves.”
Addendum: I thank Claude for assistance on this post.
Zimbabwe facts of the day
Zimbabwe, often considered an economic basket-case because of its history of farm seizures and hyperinflation, is enjoying an idiosyncratic boom. High prices for the metal and other commodities have led to a surge of cash through its highly informal economy. They have made it easier for authorities to stop printing money and meddling in currency markets; inflation is at its lowest in about 30 years. The IMF has repeatedly revised upwards estimates for economic growth, most recently to at least 7.5% for 2025, almost double the African average…
Gold is not the only source of growth. The current tobacco crop will be the largest on record. Lithium, chrome and platinum miners, many of them Chinese, have raised production. Zimbabwe’s diaspora, mainly in South Africa, sent back $2.5bn last year. So overall demand is higher than ever, says a banker.
Here is more from The Economist. We are told that the private vault sector is booming too.
Eight Rules to Regain Public Trust in Academia
The Yale Report was quite good but for concision I prefer Kevin Bryan’s Eight Rules:
1. Produce and Teach Useful Knowledge
Universities exist to generate and teach useful knowledge. This knowledge is grounded in skeptical inquiry, empirical evidence, and logical deduction. “Useful” includes not only practical applications but also fundamental discoveries that expand our understanding of the world, even if their benefits are long-term.
2. Be Useful to All of Society
Universities are subsidized only if society at large finds them valuable. Research may take time to bear fruit, but its insights should ultimately serve the public good, communicated openly and accessibly, and presented with epistemic humility. Teaching should be done with care and draw on up-to-date research.
3. Attract Talent from All of Society
Useful knowledge can be created by people from any social or economic background. Do not waste talent. Do not select talent based on who knows “how to play the game”. Avoid insular language or norms that deter people from entering research.
4. Neutral, Objective Research Produces Useful Knowledge
Research must be neutral and objective. It is true that everyone has their individual background and preferences; nonetheless, unbiased research is still possible. Tradition, folk knowledge, and storytelling all play an important roles in society, but they are not the purpose of universities. There is no “Western science” or culturally-determined “ways of knowing”. Rather, research is open to all and can be performed identically regardless of background.
5. Hire, Promote, and Cite Based on Knowledge Contribution
Hiring, promotion, and citation must be based on an individual’s contribution to knowledge. Nepotism, group preferences, and adherence to specific “schools of thought” corrupt this process. When advancement is not based on merit, the public rightly questions our integrity and the objectivity of our findings.
6. Keep Personal Views Out of Research and Teaching
A scholar’s personal politics should be invisible in their research and teaching. If a finding is predictable based on the author’s identity or known views, the process has failed. Objectivity is the hallmark of credible science. Academics may hold private beliefs like anyone else, but their academic work must stand apart from them.
7. Research Fraud is Unacceptable
Fraud destroys trust. Misrepresentation of results, selective reporting, or methods designed to publish rather than to discover are also harmful. Proven fraud must bring immediate dismissal, as it violates the core purpose of academia.
8. Scientific Institutions Should Be Apolitical
Universities, journals, and scientific societies must remain non-partisan. Their public statements must be rare, restricted to issues of direct expert consensus, and made only when silence would be a greater threat to their integrity than speaking. Activism sacrifices credibility for influence – or worse yet, sacrifices credibility and influence alike.
I would add 9) Grades must be objective and useful discriminators of talent.
Rescind Davis Bacon
The Davis-Bacon Act requires that workers on federally funded construction projects be paid at least the “prevailing wage” for their trade in the local area.
Mike Schmidt, Director of the CHIPS Program Office, has an excellent piece on how Davis-Bacon impacted the CHIPS program. My initial understanding was that it simply required paying construction workers more—an unnecessary transfer from taxpayers to a politically favored group, but not one that would impede efficiency. I was wrong.
Start with the complexity. Davis-Bacon’s prevailing wage isn’t a simple minimum wage: plumbers are not electricians are not fitters, and the required rate varies by locale. The Department of Labor maintains a list of more than 130,000 (!) wage rates to implement it.
That’s complicated enough. But it gets worse. Some firms building fabs used their own employees rather than contractors—and Davis-Bacon applies regardless but it covers only the portion of time an employee spends on “construction” work:
[A]pplying Davis-Bacon to company employees rather than contractors proved to be a big hurdle. Davis-Bacon required tracking every hour each employee spent on covered construction activities — by trade classification, with a different prevailing wage applying to each — and paying a wage differential for that portion of their work as distinct from fab operations work or non-Davis-Bacon construction work. The company also relied heavily on profit-sharing (where a portion of employees’ pay was tied to the firm’s profits) and Davis-Bacon’s guaranteed wage floor was difficult to reconcile with a pay structure that was inherently variable. Moreover, Davis-Bacon has a statutory requirement to pay wages weekly, meaning the company would need to change its payroll systems for a portion of the pay for a portion of its workforce.
Thus, DB required that two salaried employee with equal salaries and profit-sharing plans be paid differentially depending on whether one of them did “construction” work. This created internal strife.
Davis-Bacon was passed in 1931, when a carpenter was a carpenter. How does it apply to building a semiconductor factory?
The construction tasks involved in building and modernizing semiconductor fabs don’t always map cleanly onto DOL’s Davis-Bacon classifications, so applicants must go through a construction plan line-by-line to determine which rate applies to which activity. In traditional Davis-Bacon contexts this is less burdensome because contractors know the system and have processes in place. But semiconductor construction was a novel application, and all of our applicants — and most of their contractors — were navigating Davis-Bacon for the first time.
For large recipients, the administrative cost of this work was real but manageable relative to project scale: they could hire consultants, procure software systems, and build internal compliance capacity….
Perhaps the biggest fiasco involved timing. The government wanted firms to move quickly and encouraged them to break ground before the Act’s rules were finalized. But when Davis-Bacon was added to the Act it required that the firms pay the prevailing wage *retroactively*:
The financial and operational implications of retroactive application were significant. A leading-edge project might have 10,000–12,000 construction workers on site at peak, with a rotating workforce totaling perhaps 30,000 individuals over the project’s life. Working through 300-plus subcontractors across multiple tiers, retroactive application could require identifying wages paid to 20,000 workers who had already cycled off the project, determining what each worker should have been paid under Davis-Bacon, and paying the difference — resulting in hundreds of millions of dollars in additional cost.
The retroactive pay exposes the law’s true nature. Firms and workers had already struck voluntary agreements; the work was done, the wages paid. No one can pretend this has anything to do with incentives. Workers received a pure windfall (“DB Christmas!”) for one reason only: “construction workers” are a politically favored class. Janitors and scientists got nothing extra.
Moreover, a large fraction of the cost wasn’t the higher wages at all—it was compliance. Firms likely spent as much reworking payroll systems and hunting down thousands of former workers in this Byzantine classification system as they spent on the wage premiums themselves. Every dollar transferred to workers may have cost firms—and ultimately taxpayers—two dollars or more. A very leaky bucket indeed.
If the Trump administration is serious about cutting regulatory costs and reviving industrial competitiveness, Davis-Bacon is an obvious target. It delivers little to workers, plenty to lawyers and consultants, and a bill to taxpayers for both. Rescind it.
Moonsteading
Charles Miller, a space entrepreneur and head of the Trump transition team on NASA, has a good piece proposing a Lunar Development Authority:
I propose the development of an international Lunar Development Authority (LDA), chartered and led by the United States, that would serve as a quasi-governmental regulator. The base on the Moon would be managed as a master-planned infrastructure development project, with NASA as the key strategic partner, emphasizing commercial methods and an investor mindset to drive economic viability in both the near and long term. The LDA would prioritize development of lunar resources to lower costs and serve customers, and treat the United States government and the governments of our allies as anchor tenant customers. The LDA would leverage public-private partnerships and cooperation among both governmental and private industry tenants from many countries to finance and develop lunar infrastructure in a commercial manner.
The model is New York’s famous Commissioners’ Plan of 1811, which imposed a simple, legible order on what was then mostly undeveloped land. The plan coordinated future development around a grid with standardized lots and clearly demarcated spaces for public and private infrastructure. Miller proposes a similar sequence for the Moon: first survey, standards, shared infrastructure, and a governing authority; then private tenants, resource extraction, construction, and finance.
The main legal obstacle is the Outer Space Treaty of 1967, which paired a ban on weapons of mass destruction in space with “anti-colonial” restrictions on national appropriation. The OST, however, doesn’t prohibit economic activity per se—the target was national land grabs, not commercial development. The more recent Artemis Accords address this directly:
The ability to extract and utilize resources on the Moon, Mars, and asteroids is critical to support safe and sustainable space exploration and development.
The Artemis Accords reinforce that space resource extraction and utilization can and should be executed in a manner that complies with the Outer Space Treaty and in support of safe and sustainable space activities.
The Homesteading Act granted title rights in return for development. The likely path forward on the moon reverses that sequence, development first, title later. Ownership of extracted resources is already widely accepted, next will come toleration of exclusive operational zones, then long-duration concessions, then transferable development rights around fixed infrastructure.
The OST may delay ordinary land markets, but it cannot repeal the deeper economic fact that settlement happens only when builders can keep enough of what they create. TANSTAAFL.
Incentives matter, Mexican cartel edition
But the cartel’s interests may prove just as important to security as government efforts, according to a dozen local and state officials and security experts.
The CJNG has much to gain from the regional economic boost of a successful tournament in Guadalajara — akin to its administrative headquarters — and much to lose from drawing authorities’ attention.
“The city is safe because those guys put all their money here, and they stand to make even more,” said one state official who was not authorised to speak on the record. “They don’t want a war here.”
Huge profits earned elsewhere from drug trafficking and other activities are laundered in Guadalajara, experts said, helping to power a real estate boom. A rash of shiny new skyscrapers has popped up, some of which sit empty. The leafy city also boasts luxurious open-air shopping malls and lively nightlife.
Here is more from Ciara Nugent at the FT.
Prediction Market Details
The Guardian has an interesting article on prediction markets. There are the usual worries about betting on death, as if insurance markets don’t already exist and about insider trading, which public markets have long dealt with. But there is also interesting material on who decides what happened when resolving bets about events made in language (as opposed to more objectively verified numbers).
On Monday, anonymous user “Harshad” asked in a Discord channel if there was “any chance” that he could still win his bet about whether US forces would enter Iran by the end of April. His money was on “no”.
But Polymarket appeared to be resolving the market to “yes”, after the US conducted an operation to rescue a crew member shot down on a mission over Isfahan over the weekend.
…At the moment, when there is a dispute, markets on Polymarket are settled by an anonymous group of people who hold a crypto token called UMA.
It’s an unusual way to decide what has happened. Some longtime users suggest it opens the platform to corruption. Different individuals hold different amounts of UMA, and therefore have different voting power.
It isn’t known who the largest UMA holders are, or what might affect how they vote. It is entirely possible that the people who finally settle a bet on UMA have large amounts of money staked on it.
There was also this bit about Prediction Hunt (I am an advisor) which is focused on cross-market arbitrage opportunities:
“I love to gamble,” said Joseph Francia.
Now in his early 30s, Francia counted cards in casinos while studying economics at Berkeley, and spent weekends in Reno, Nevada, playing blackjack. He’s not a thrill-seeking “Yolo” (you only live once) gambler, he said: he likes to bet when he has an edge on the house.
At university, he and a friend decided to collect data from a number of offshore sportsbooks, and start placing arbitrage bets: playing on the discrepancies in odds given by different betting sites.
“If the odds on the Lakers are really good on one site, and the odds on the Pacers are really good on another site, you could bet on basically both teams on different sportsbooks and make guaranteed profit,” he said.
That project was a student lark in 2017. But in 2025, he remembered it when he was suddenly laid off from his full-time job, just as prediction markets were taking off.
“I’m a spiritual, religious person,” he said. “The more secular people would say, this opportunity is coincidence. But in my head, I was like, this is a sign of something to some extent. Let me lean into this.”
So Francia started Prediction Hunt, a Discord channel and online community where thousands of people gather to trade tips and ideas for how to make money – and bet smart – on Polymarket. The Guardian spent roughly three weeks in this Discord channel.
There are alerts to track “fade” bets, where you try to follow the smart money: profitable wallets were betting “yes” on the Iranian regime falling by 30 April, for example, while unprofitable wallets were betting “no”.
There are alerts to track potential insiders, so you can copy their bets: one of these appears to have an inside line on interest rate decisions by the US Federal Reserve.
Getting these details right will be important but overall I am pleased that the news now regularly reports prediction market data when reporting stories–this is disciplining news from noise, something I predicted long ago in Entrepreneurial Economics.
My South Africa dialogue with Ann Bernstein
An edited transcript is here.
Orbán concedes
And that is in Hungary, which does not have much of a democratic tradition. People who suggest that democracy seriously is in danger in the United States need to rethink their world views (this claim however is slightly exaggerated). The problem instead is that democracy does not always bring you desired results…
AI, Unemployment and Work
Imagine I told you that AI was going to create a 40% unemployment rate. Sounds bad, right? Catastrophic even. Now imagine I told you that AI was going to create a 3-day working week. Sounds great, right? Wonderful even. Yet to a first approximation these are the same thing. 60% of people employed and 40% unemployed is the same number of working hours as 100% employed at 60% of the hours.
So even if you think AI is going to have a tremendous effect on work, the difference between catastrophe and wonderland boils down to distribution. It’s not impossible that AI renders some people unemployable, but that proposition is harder to defend than the idea that AI will be broadly productive. AI is a very general purpose technology, one likely to make many people more productive, including many people with fewer skills. Moreover, we have more policy control over the distribution of work than over the pure AI effect on work. Declare an AI dividend and create some more holidays, for example.
Nor is this argument purely theoretical. Between 1870 and today, hours of work in the United States fell by about 40% — from nearly 3,000 hours per year to about 1,800. Hours fells but unemployment did not increase. Moreover, not only did work hours fall, but childhood, retirement, and life expectancy all increased. In fact in 1870, about 30% of a person’s entire life was spent working — people worked, slept, and died. Today it’s closer to 10%. Thus in the past 100+ years or so the amount of work in a person’s lifetime has fallen by about 2/3rds and the amount of leisure, including retirement has increased. We have already sustained a massive increase in leisure. There’s no reason we cannot do it again.
LDS fact of the day
The Church of Jesus Christ of Latter-day Saints has grown 66% this century, fueled in part by a record-breaking number of convert baptisms in 2025.
The church had 10,752,986 members at the end of 1999. The church had 17,887,212 at the end of 2025, according to an annual statistical report released Saturday during the church’s 196th Annual General Conference.
Furthermore the growth is coming in every part of the world (as a qualifier I am not sure what the outflow is). Here is the full article, via Tyler Ransom.
AI Risks
Two new papers/initiatives indicate severe risks from AI, interestingly in opposite directions. The first is that the most advanced frontier models are now capable of finding and exploiting software in ways that could be used to crash or control pretty much all the world’s major systems.
Anthropic: We formed Project Glasswing because of capabilities we’ve observed in a new frontier model trained by Anthropic that we believe could reshape cybersecurity. Claude Mythos2 Preview is a general-purpose, unreleased frontier model that reveals a stark fact: AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities.
Mythos Preview has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser. Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout—for economies, public safety, and national security—could be severe. Project Glasswing is an urgent attempt to put these capabilities to work for defensive purposes.
That’s from Anthropic. The irony is that the company that has developed a frontier model capable of infiltrating and undermining more or less any computer system in the world is the one that has been forbidden from working with the US government. It’s as if a private firm developed nuclear weapons and the American government refused to work with them because they were too woke. Okey dokey.
The second paper on AI risks is AI Agent Traps from Google DeepMind. They point out that AI agents on the web are vulnerable to all kinds of attacks from things like text in html never read by humans, hidden commands in pdfs, commands encoded in the pixels of images using steganography and so forth.
Putting this together we have the worrying combination that very powerful AI’s are very vulnerable. Will AI solve the problems of AI? Eventually the software will be made secure but weird things happen in arms races and its going to be a bump ride.