2026-04-01 19:17:51
A recent viral tweet, quoted by Elon Musk, points out that bartenders can be fined or even imprisoned if they serve alcohol to patrons who later kill someone while under the influence. Judges, in contrast, enjoy absolute or qualified immunity even when they repeatedly release defendants who go on to kill.
I agree that judges should face stronger incentives to make good decisions, but the obvious problem with penalizing judges who release people who later commit crimes is that judges would then have very little incentive to release anyone—and that too is a bad decision. Steven Landsburg solved this problem in his paper A Modest Proposal to Improve Judicial Incentives, published in my book Entrepreneurial Economics.
Landsburg’s solution is elegant: we must also pay judges a bounty when they release a defendant.
Whether judges would release more or fewer defendants than they do today would depend on the size of the cash bounty, which could be adjusted to reflect the wishes of the legislature. The advantage of my proposal is not its effect on the number of defendants who are granted bail but the effect on which defendants are granted bail. Whether we favor releasing 1 percent or 99 percent, we can agree that those 1 percent or 99 percent should not be chosen randomly. We want judges to focus their full attention on the potential costs of their decisions, and personal liability has a way of concentrating the mind.
One might object that a cash bounty will cost too much, but recall that the bounty is balanced by penalties when a released defendant commits a future crime. The bounties and penalties can be calibrated so that on average the program is budget-neutral. The key is to get the incentives right on the margin.
The structure of this problem is quite general. Ben Golub, for example, writes:
There should be a retrospective reputational penalty imposed on referees who vote no on a paper because the paper is too simple technically — if that paper ends up being important. It’s an almost definitional indicator of bad judgment.
Quite right, but a penalty for rejection needs to be balanced with a bonus for acceptance. Get the marginal incentive right and quality will follow!
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2026-04-01 13:14:09
We completed the most comprehensive study of how economists and AI experts think AI will affect the U.S. economy. They predict major AI progress—but no dramatic break from economic trends: GDP growth rates similar to today’s and a moderate decline in labor force participation. However, when asked to consider what would happen in a world with extremely rapid progress in AI capabilities by 2030, they predict significant economic impacts by 2050:
• Annualized GDP growth of 3.5% (compared to 2.4% in 2025)
• A labor force participation rate of 55% (roughly 10 million fewer jobs)
• 80% of wealth held by the top 10% (highest since 1939)
That is from this very good and very detailed Twitter thread, worth reading in its entirety. Note this:
Only 5.2% of the variance is between scenarios—attributable to disagreement about AI capabilities themselves…
Here is the full paper, over 200 pages long, I will be reading through it. The list of authors is impressive, with Ezra Karger in the lead, also including Kevin Bryan, Basil Halperin, and many more. For some while this will stand as the best set of estimates we have. Here are the related forecasts of Seb Krier.
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2026-04-01 12:03:24
That all sounded wonderful, and that core model and its offshoots dominated financial research for decades. The problem, however, was that it wasn’t true, or at least it wasn’t nearly as true as we had thought and hoped. When financial economists refined the models with more complete specifications, it turned out Beta didn’t predict stock returns much at all. Eugene Fama and Kenneth French delivered one of the final blows to earlier approaches with a 1992 paper that showed Beta didn’t have explanatory power over expected returns at all. Since Fama himself was one of the original architects of CAPM-like reasoning, and French also was a renowned finance economist, these revisions to the model were credible. For all its original promise, marginalism, and the concomitant notion of diminishing marginal utility, no longer seemed to help explain asset returns.
Under one plausible account of intellectual history, you can date the decline of marginalism to that 1992 paper. In the most rigorous, data-oriented, and highest-paying field of economics, namely finance, marginalist constructs had every chance to succeed. In fact, they ran the board for several decades. But over time they failed. In the most prestigious field of economics, marginalism has been in full retreat for over 30 years, and it shows no signs of making a comeback.
We already know that financial practice is dominated by the (non-economist) quants. But how about financial economics research, the parts that are still done by economists? What direction is that work moving in?
I was struck by a 2024 paper published in the Journal of Financial Economics, one of the two leading journals of financial economics (Journal of Finance is the other). The authors are Scott Murray, Yusen Xia, and Houping Xiao, and the title is “Charting by Machines.” The core result is pretty simple, and best expressed in the well-written abstract:
“We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out-of-sample.” Murray, Xia, and Xiao (2024, p. 1). Or consider the new paper Borri, Chetverikov, Liu, and Tsyvinski (2024). They propose a new non-linear, single-factor asset pricing model. In the abstract: “Most known finance and macro factors become insignificant controlling for our single-factor.” Yet you won’t find traditional economic variables discussed in this paper, it is all about the math, in particular a representation of the Kolmogorov-Arnold representation theorem.
In other words, the successful approach to predicting returns is giving up on traditional portfolio theory and using the “theory-less” technique of machine learning. Although this is published in the Journal of Financial Economics, in some significant sense it is not economic reasoning at all. It is calculation, combined with expertise in math and computer science. The modeling is not economic modeling in a manner that has ties to marginalism or standard intuitive microeconomic theory. And the work is predicting excess returns in a pretty robust and successful way…
There is a recent working paper which is perhaps more striking yet, by Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly, and Semyon Malamud. They pick up from Arbitrage Pricing Theory (APT), a well-established idea from financial economics. APT typically looks for “factors” in the data which predict excess returns, and a traditional APT model might have found five or six such factors. Are “inflation” or perhaps “the term structure of interest rates” useful factors? Well, that can be debated, but if so, those results sound pretty intuitive. But those intuitions seem to be disappearing. In a paper by these authors, they apply machine learning methods to look for more factors. As we know, machine learning is very good at finding non-obvious relationships in the data. The largest model they built has 360,000 (!) factors, and it reduces pricing errors by 54.8 percent relative to the classic six-factor model from Fama and French. Bravo to the authors, but what kinds of intuitions do you think possibly can be supported by those 360,000 factors?
That is from my new The Marginal Revolution: Rise and Decline, and the Pending Revolution in AI.
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2026-04-01 02:39:04
Bryan Caplan keeps hammering this point home, it is good to see follow-up work:
In the United States, college dropout risk is sizable. We provide new empirical evidence that beliefs about the likelihood of earning a bachelor’s degree predict college enrollment, and that the distribution of these beliefs exhibits widespread optimism. We incorporate this distribution of beliefs into an overlapping generations model with college as a risky investment that can be financed via federal loans, grants, family transfers, or earnings. We then examine the welfare impact of access to federal student loans. We find that access can reduce welfare for young adults who are low-skilled, poor, and optimistic, due to their mistaken beliefs.
That is from AEJ: Macroeconomics, by Emily G. Moschini, Gajendran Raveendranathan, and Ming Xu. Via the excellent Kevin Lewis.
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2026-04-01 01:58:39
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2026-03-31 23:50:29
1. “… presenting Economics as empirical and socially relevant may broaden the profile of those who consider the field.” But it does not get more people interested.
2. Youth happiness has been rising in many places, possibly most.
3. The NIH as an implicit regulatory body.
4. The Lebron critique of prediction markets.
5. Adam Tooze: “It is a truism of the moment that China is the last adult in the room.”
6. Quantum breakthroughs? And another account. Will the Satoshi wallet remain safe?
7. Shall we organize scientific literatures around claims rather than papers?
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