2026-03-31 15:26:36
Yes, there are skills AIs haven’t mastered. But if your skill still appears to be the exclusive province of humans, that might mean the major AI companies do not yet consider it very important to master right away. Eventually it will rise to the top of the list.
Here is more from my Free Press essay on AI. If not for the copied passage, it seems no one was noticing this book review? (NYT, read the emendation)
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2026-03-31 12:45:44
Specification Searching in the Race between Education and Technology: Joseph Francis criticizes a canonical model of the American labor market, which has been used to advocate for more funding for education to reduce inequality. He shows how the model has routinely failed to predict the evolution of the college wage premium. Ad hoc econometric adjustments have been necessary to make the model fit the data, most notably in Claudia Goldin and Lawrence F. Katz’s well-known book. (The commented-on authors are hereby invited to reply in a future issue.)
Globalization and the China Shock: A Reassessment: David Autor, David Dorn, and Gordon Hanson estimated the effect of imports of manufactured goods from China from 1990 to 2007 on employment, wages, and social welfare payments in the USA, concluding that imports from China reduced manufacturing employment and lowered wages of workers in non-manufacturing industries. Robert Kaestner argues that the authors’ focus only on Chinese imports, which are correlated with imports from other countries and likely other omitted variables, muddles the interpretation and usefulness of their results. Kaestner argues that their estimates do not measure the effect of Chinese imports on employment and wages holding all other things equal, and do not even measure the broader equilibrium effect of Chinese imports on outcomes that includes changes in imports from other countries. Overall, the evidence suggests that omitted variable bias is likely, which renders their estimates uninformative. (The commented-on authors are hereby invited to reply in a future issue.)
Learning on machine learning on the housing supply impact of land use reforms: An Urban Studies article reports relatively modest housing-stock gains from liberalization, based on a dataset of reforms identified via machine learning applied to newspaper coverage. Researchers at the American Enterprise Institute challenge the article’s methodology and conclusions, and the Urban Studies authors respond.
An Article in Science on Covid Origins Contains a Fundamental Error: An influential article claimed that Bayesian analysis of the molecular phylogeny of early SARS-CoV-2 cases indicated that the likelihood that two successful introductions to humans had occurred was greater than the likelihood that just one had occurred. After correcting a fundamental error in Bayesian reasoning, the results presented in that paper imply larger likelihood for a single introduction, reducing the plausibility of the wet-market zoonosis account of Covid’s origins. (The commented-on authors were invited to reply and the invitation remains open.)
A Critique of Synthetic Control Method Studies on Covid-19 Policy—Evidence from Sweden: Five studies employing the Synthetic Control Method (SCM) conclude that Sweden would have experienced lower mortality had it imposed a mandatory lockdown in early 2020. Dividing Sweden into four hypothetical countries based on winter holiday timing—a proxy for pre-lockdown viral seeding—Jonas Herby shows that the estimated lockdown effect varies dramatically across regions with identical policies, suggesting SCM captures variation in viral spread rather than a causal policy effect. Sweden’s low excess mortality in the end suggests that Sweden’s state epidemiologist, Anders Tegnell, was right all along. (The commented-on authors are hereby invited to reply in a future issue.)
Central Banking Research Is Increasingly Directed to Environment, Inequality, Gender, and Race: Radu Șimandan and Cristian Valeriu Păun use the Scopus database to show how environment, inequality, gender, and race have soared as topics in research outlets supposedly focused on money and banking. They discuss the hazards of subverting price stability and other traditional central bank mandates.
Power Analysis Is Essential—A Case Study in Rounded Shapes: A Journal of Consumer Research article reported an A/B test where simply rounding the corners of square buttons increased click-through rate by 55 percent, but provided no power analysis. Ron Kohavi and coauthors show that the original study was highly underpowered. They report that three high powered A/B replications, each over two thousand times larger, had estimated effects approximately two orders of magnitude smaller than initially claimed. (The commented-on authors are hereby invited to reply in a future issue.)
“Impartial spectator” in Adam Smith’s The Theory of Moral Sentiments: In the previous issue, a critique alleged that numerous scholars flatten Smith’s “impartial spectator.” Jack Weinstein responds with “Adam Smith’s Impartial Spectator Is Neither Divine Nor an Ideal Observer,” and the critics renew their case against flattening “impartial spectator.”
The Ideological Profile of France’s Economic Bestsellers: Alexis Sémanne inspects the 100 economics bestsellers for 2024, as listed by a leading French bookseller. He develops seven categories and evaluates each book for its ideological tendency. Quite few of the books offer a freedom-oriented perspective.
Green Vanities in Europe: John Constable reviews A Green Entrepreneurial State? Exploring the Pitfalls of Green Deals, edited by Magnus Henrekson, Christian Sandström, and Mikael Stenkula, a book which reveals more than the fact that green deals in Europe have been failures.
EJW thanks its referees and others who contribute to its mission.
EJW Audio:
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2026-03-31 02:53:15
This matters for the AI question, and the book leaves it unfinished. If the breakthroughs of the past required social conditions, not just cognitive capacity, then what does it mean when the next breakthroughs are produced by systems that have no social conditions at all? A neural net does not need a university chair or financial independence from the church. It does not need to reorganize its commitments. It does not, in any recognizable sense, have commitments. The machine that replaces the marginalist is not a better marginalist. It is a different kind of thing entirely.
That is from Jônadas Techio, presumably with LLMs, this review of The Marginal Revolution is interesting throughout. And this:
Maybe the book demonstrates only that Cowen personally remains good at something the field no longer needs.
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2026-03-31 00:33:29
1. Was there a great Philadelphia cheese steak stagnation?
2. David French on the enemies of free speech (NYT). And yes it is Indonesian censorship, nothing to celebrate.
3. Profile of Hussein Aboubakr. Good piece on one of today’s best thinkers and writers. Link to Twitter and Substack. Unlike many writers on these topics, it is not about your opinion of Israel, rather each piece is interesting and substantive. Try his essay on Mahfouz.
4. Lab Leak is somewhat declining in plausibility.
5. “China is cracking down on families who opt to bury their dead in empty high-rise properties — known as “bone ash apartments” — rather than pay skyrocketing costs for cemetery plots.” (FT)
6. Do developing countries still need to industrialize?
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2026-03-30 19:17:49
It’s well known that grade inflation has “degraded” the informational content of grades at many colleges. At Harvard, two-thirds of all undergraduate grades are now A’s—up from about a quarter two decades ago. In response, a Harvard faculty committee has proposed capping A grades at 20 percent of each class (plus a cushion for small courses). That may give professors some cover to resist further inflation, but it doesn’t solve the real problem.
The real problem is not inflation per se. It’s that students are penalized for taking harder courses with stronger peers. A grade cap leaves that distortion intact—and can even amplify it. As Harvard economist Scott Kominers argues:
A grade cap systematically penalizes ambitious students for surrounding themselves with strong classmates. Perverse course-shopping incentives ensue as a result. A student who is prepared for an advanced course but concerned about landing in the bottom 80 percent may choose to drop down preemptively—seeking out a pond where they are a relatively bigger fish. As strong students move into lower-level courses, competition for A grades increases there while harder courses continue to shrink—reducing their A allocation further and driving more students away.
The underlying issue is informational. A grade tries to capture two things—student ability and course difficulty—with a single number. Gans and Kominers show that in general this is impossible: if some students take math and earn B’s while others take political science and earn A’s, there is no way, from grades alone, to tell whether the difference reflects ability or course difficulty.
There is, however, a solution in some cases. Clearly, if every student takes some math and political science courses, informative patterns can emerge. If math students tend to get B’s in math but A’s in political science, while political science students get A’s in their own field but C’s in math, you can begin to separate course difficulty from student ability.
Students don’t all overlap the same classes. But full overlap isn’t necessary—you just need a connected network. If Alice just takes math courses, Joe takes math and political science courses, and Bob just takes political science courses, then Alice and Bob can be compared through Joe. With enough of these links, the entire system can be stitched together. The more overlap, the more precise the estimates.
Valen Johnson proposed a practical method along these lines in 1997. Gans and Kominers embed the same intuition in a much more general framework, showing exactly what can and cannot be inferred, and under what conditions.
The great thing about achievement indexes based on relative comparisons is that they are robust to grade inflation and do not penalize students for taking hard classes or subjects. A political science student who chooses to take a tough math class instead of an easy-A intro to sociology course won’t be penalized because their low math grade will, in effect, by boosted by the difficulty of the course/quality of the students. That’s good for the student and also good for disciplines that have lost students over the years because they held the line on grade inflation.
One final point. Harvard’s cap proposal appears to have been developed with little engagement with researchers who have studied problems like these for decades in the mechanism and market design literature—people like Kominers, Gans, Budish, Roth, Maskin, and Sönmez, some of them at Harvard! Moreover, this isn’t a case of ignoring high-theory for practice. The high-theory of mechanism design has produced real-world systems including kidney exchanges, school choice mechanisms, physician-resident matching, even the assignment of students to courses at Harvard, as well as many other mechanisms. Mechanism design is practical.
Grade inflation is a mechanism design problem—and we know a lot about how to solve it, if we want to solve it.
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2026-03-30 14:48:52
The subtitle of the piece is “Evidence From Chinese and Swedish Twins.” Abstract:
Economists have long studied how parental behavior shapes within-family inequality, yet empirical findings remain mixed. Using twins data from China and Sweden, we examine the predominant mechanisms reported in the literature. Parents in both countries invest similarly during childhood. Inter vivos transfers, however, differ: Chinese parents reinforce income inequality, whereas Swedish parents distribute wealth equally; the reinforcing pattern reflects exchange motives. Bequests are divided equally in both countries. Parental education plays a key role: less educated parents reinforce income inequality, whereas more educated parents transfer wealth equally. Cross-country differences in parental education may thus help explain the mixed findings.
By Aiday Sikhova, Sven Oskarsson, Rafael Ahlskog. Via the excellent Kevin Lewis.
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