Those new service sector jobs?
A suburban Chicago man has been sentenced to two years in federal prison for conspiring to stage armed robberies so that the purported victims could apply for U.S. immigration visas reserved for crime victims.
From 2022 to 2024, PARTH NAYI organized and participated in hundreds of staged armed robberies at coffee shops, restaurants, liquor stores, and gas stations throughout the country, including in Chicago and the suburbs of Lombard, Elmwood Park, St. Charles, Hickory Hills, River Grove, Lake Villa, and South Holland. Nayi worked with others to recruit fake victims, pay off store owners to use their locations, coordinate and pay fake robbers, and coach the bogus victims through the plan. Thousands of individuals who posed as victims each paid Nayi $5,000 to $30,000 to participate in the scam so that they could later apply for Nonimmigrant U-Visas, which are set aside for foreign nationals who are victims of certain crimes and are helpful to law enforcement in the investigation.
During the staged robberies, individuals acting as robbers brandished what appeared to be firearms, approached the purported victims, and demanded money. Afterwards, the purported victims submitted forms to local law enforcement to obtain certification that they were victims of a qualifying crime and were helpful in the investigation. Upon receiving the certification, the purported victims then submitted fraudulent U-Visa applications to U.S. Citizenship and Immigration Services predicated upon their alleged status as robbery victims.
Here is the full story, via Ryan.
Are LLMs good or bad Bayesians?
Bryan Caplan:
It seems like you and other AIs are bad Bayesians. When presented with counterevidence, you should move toward or against the presenter with roughly equal probability. It depends on whether the argument was better or worse than expected. In fact, you seem to either move toward the speaker or just not change.
ChatGPT:
I think you’ve identified a genuine and important failure mode in AI reasoning: LLMs tend to treat a plausible counterargument as evidence in favor of its conclusion, rather than comparing the counterargument to what they should have expected to hear.
Your Bayesian point is correct, with one qualification: there’s no general theorem that updating toward and away from a speaker should happen with equal probability. But under appropriate calibration assumptions, both directions should occur, and an argument that’s weaker than expected should cause a negative update—even if the argument is perfectly valid as far as it goes.
Here is the full dialogue. But will they try to fix this? Should they try to fix this? Can you undo this with good enough prompting? The scope and limits of Bayesian are truly at stake here…
William Nordhaus, RIP
Here is notice from Yale, and from Paul Krugman, he was one of the true greats. For the pointer I thank several MR readers.
And so it begins…
Researchers, I just uploaded a prominent QJE paper on climate change economics to @RefineDotInk and I loaded my critique of the paper. I asked the AI to offer an impartial verdict. I will make this report public. A new journal could collect these into “Special Issues”.
That is from Matthew E. Kahn.
Saturday assorted links
2. World Bank African urbanization report. Building out African cities in a decent way could end up being the task of our coming era.
3. Summary of The Commanding Heights.
6. One account of what is going on in Poland. I am not endorsing that (or any other) interpretation, but if nothing else it shows you how interpretation of these events can make your head spin. Grok has a simpler answer, again not endorsing.
My Barcelona talk
Wednesday, November 4, 18:30 Barcelona time, details here. I am honored to have won the Elinor Ostrom Prize from the Institut Ostrom Catalunya. Maybe see you there!
The pass-through of retail crime
This paper shows retailers increase prices in response to organized retail crime. We match store-level crime data to scanner data from the universe of transactions for cannabis retailers in Washington state. Using quasi-experimental variation from robberies and burglaries, we find a 1.7–1.8 percent price increase at victimized stores and nearby competitors. This rise is not driven by short-to-medium-term demand changes but is consistent with crime-related cost shocks. Effects are larger for independent stores and less concentrated markets. We estimate crime imposes a 1 percent “hidden” unit tax on affected stores, implying $30.7 million in additional social costs, primarily borne by consumers.
That is from a newly published paper, AEJ: Economic Policy, by Carl Hase and Johannes Kasinger.
The unintended consequences of academic leniency
Education is widely recognized as the primary driver of human capital accumulation. However, policies designed to promote educational attainment often overlook students’ endogenous responses. This paper provides empirical evidence of the causal impact of relaxed academic standards on student effort and achievement. We find that academic leniency results in mechanical GPA increases, but significant reductions in effort among lower ability students based on lower attendance and lower numerical grades. These heterogeneous effects compound and exacerbate gaps in engagement and achievement throughout high school, ultimately worsening deficits in human capital accumulation as measured by ACT scores.
That is from a newly published paper, in AEJ: Economic Policy, by A. Brooks Bowden, Viviana Rodriguez, and Zach Weingarten.
Friday assorted links
1. Dwarkesh interviews Robert Caro (!).
2. New and pretty brutal results on CTE and football.
4. Updated version of the Saez-Zucman audit. Contestable and speculative assumptions are now separated from true defects.
5. Rosalind Arden interviews Greg Clark on social status.
Better Incentives, More Kidneys, More Lives
The organ procurement system has long been a miasma of inefficient monopoly providers. In 2024 I quoted some shocking facts from a great Statecraft interview with Jennifer Erickson and Greg Segal:
“Organs are literally lost and damaged in transit every single week. The OPTN contractor is 15 times more likely to lose or damage an organ in transit than an airline is a suitcase.”
Organs are not GPS-tracked!
“In an era when consumers can precisely monitor a FedEx package or a DoorDash dinner delivery, there are no requirements to track shipments of organs in real time — or to assess how many may be damaged or lost in transit.
“If Amazon can figure out when your paper towels and your dog food is going to arrive within 20 to 30 minutes, it certainly should be reasonable that we ought to track lifesaving organs, which are in chronic shortage,” Axelrod said.
Here’s one more astounding statistics:
“Seventeen percent of kidneys are offered to at least one deceased person before they are transplanted….”
Did you get that? The tracking system for patients is so dysfunctional that 17% of kidneys are offered to patients who are already dead–thus creating delays and missed opportunities.
Fortunately, even then there were changes in the works. In 2019 President Trump issued Executive Order 13879 to improve the performance of OPOs with better incentives and to allow living donors to recover more of their costs. The government being what it is, it has taken years to implement some of the new rules and enforcement of the OPO performance measures was deferred to 2026-2027. Nevertheless, there have been improvements. For example, many OPOs now GPS-track kidney transfers. And a new report estimates there have been significant gains from the changes.
- The Advancing American Kidney Health initiative was launched by President Donald Trump via Executive Order 13879 on July 10, 2019. In this Data Point, we estimate the range of increased growth in transplants that might be attributed to the policies launched through the Executive order and the economic value of that increased growth.
- The estimated impacts of the Executive Order (EO) range from a conservative 1,055 additional transplants annually to an upper estimate of 2,110 additional transplants. The range reflects variation in the counterfactuals used to compare with post-2019 transplant growth.
- Under the conservative-case scenario, the policy is estimated to be associated with approximately 23,214 additional kidney transplants between 2019 and 2040, generating about 147,410 life-years gained and 21,821 quality-adjusted life-years (QALYs) gained. Under the optimistic-case scenario, the policy is estimated to be associated with approximately 46,428 additional kidney transplants between 2019 and 2040, generating about 294,819 life-years gained and 43,643 quality-adjusted life-years (QALYs) gained.
The biggest and most consequential change would be to allow payment to living organ donors. The End Kidney Deaths Act, H.R. 2687, proposes a $50,000 refundable tax credit over five years for qualifying nondirected living kidney donors. The act has bipartisan support. Because dialysis is so expensive—and the federal government pays for much of it through Medicare’s special kidney-failure entitlement—the Act could save taxpayers money as well as saving lives.
Hat tip: The indefatigable Elaine Perlman.
Are there too many misdemeanor convictions?
Maybe so:
Misdemeanors account for three-quarters of criminal cases filed annually in the United States, yet causal evidence on the impacts of misdemeanor conviction is scarce. We combine administrative and survey data to provide novel evidence of how misdemeanor conviction affects the socioeconomic outcomes of defendants and their families. Using data on nearly seven million defendants from 11 states, we characterize the types of people involved with the misdemeanor system. We estimate causal impacts of a conviction relative to a charge using seven county reforms that sharply reduced conviction rates. Regression discontinuity design estimates yield precise null average effects of the reforms on criminal and labor market outcomes. Defendants dismissed by the reform are unlikely to re-offend if dismissed, but also unlikely to be harmed by conviction—marginal defendants are older and more likely to have been charged with minor crimes than the average defendant. Heterogeneity analyses suggest that conviction harms young and first-time defendants, implying that interventions targeting dismissal toward these groups would be beneficial. Finally, our causal estimates imply a negative Marginal Value of Public Funds of misdemeanor conviction, even under plausible estimates of general deterrence effects.
That is from recent research by Nikhil Rao and James Reeves, note that Rao is this year on the job market from University of Michigan.
Chronicle of Higher Education interviews me on AI and education
Scott Carlson did the work (gated, but free with registration), here is one excerpt:
Cowen: …Another proposal I’ve made is that schools should let people do one class, only one class, AI-only — no professor, except a professor grades at the end. The professor grades not answers to an exam but the workflow of how the student used AI to learn the Glorious Revolution, or the Civil War, or whatever the student learned. Eventually, another AI will grade your AI log. But let’s start today with a professor, and try this for one class. I’m not sure how it will go. But especially for small schools that don’t offer that many classes, a student now can create a class in literally everything.
If it’s successful, let them try two classes. I think the students doing this on their own are more likely to figure out how to blend AI and education than we would from a distance with some kind of centralized vision. Let them experiment.
Carlson: Students should be leading the change at colleges and universities?
Cowen: Absolutely. Students know more about AI than their professors do. Some of this comes from cheating, which is unfortunate. But the notion that the professors teach the students AI — I wish. It’s not always possible. You could imagine setting up a class where the students teach the professors AI, and we give the students credit for teaching. I’m half joking, but it’s not, in fact, a crazy idea. We need to experiment along a number of fronts. I’m quite convinced no one knows how to do this. It hasn’t been done before. So again, just one class. In general, schools hate this idea. They see it as opening the door to rendering some professors obsolete.
And this:
Carlson: There are a number of students who are deeply opposed to AI. What place do they have in this university that you’re thinking about?
Cowen: I would love to have different schools with different policies. If some universities try to ban AI altogether, that’s great. Let the competitive process operate. Personally, I would bet against those schools. For a long time, I’ve been worried that within a given tier of school — big state school, community college, Ivy League — there’s not enough diversity. AI policies will force a kind of diversity on us. So I’m all for no-AI schools, even though I would not go to one.
Recommended.
Can Artificial Intelligence Forecast Treatment Effects in Economics?
I use LLMs to forecast treatment effects of research studies pre-registered on the AEA RCT Registry and the Open Science Framework. I focus on studies whose results were first publicly released in 2026 and elicit forecasts from models whose training or knowledge cut-off dates were before the relevant set of papers was released. First, LLM models extract forecasting questions from the pre-registrations or pre-analysis plans. These questions elicit forecasted treatment effects; not all resolve in the subsequent papers, but those that do can be used to consider forecast accuracy. Forecasts were elicited for 889 treatment effects from 39 papers with results disclosed in January 2026 or later, or 673 from 33 papers after screening. Forecasts were elicited in raw units but I focus on those that could be standardized and therefore compared across papers. Currently, the median absolute forecast error is 0.054 SD for the best model, the average absolute error is 0.093 SD, and the MSE is 0.0218. The AI forecasts perform substantially better than benchmarks forecasting 0 effect or forecasting the mean effect of other papers’ results in that time period. Overall, these results suggest considerable scope for forecasts to be of value in informing policy and research.
From the excellent Eva Vivalt. As I have been predicting, so much of economics (and science) is going to change, and radically. Here is Eva’s thread on the paper. Perhaps we should dispense with many of the more mundane inquiries, and as Eva notes: “But can we not shift to working on those projects that have particularly high uncertainty and upside potential?”
She is one of the best young economists in the world, and this stands some chance of being the most important paper of the year.
Thursday assorted links
1. “A whopping 79 NBA players are listed on today’s 2:30 PM injury report for tonight’s games. That’s roughly 15% of the entire league” This is just week one people! And some commentary.
2. Video making the case that AI has been fine for jobs.
3. John Repetti reviews The Rouse at Jacobin.
4. Is TikTok now encouraging more optimistic content? (NYT) Big if true, I am not currently active on the platform and do not have an opinion on the matter. In any case worth a read.
5. Andy Masley recommends some lesser-known books.
6. New OpenAI dataset on how people use ChatGPT.
7. Piketty-Saez-Zucman biases found earlier by humans. And an audit from Grok on whether the initial AI report is capturing the major problems.
How Tyler Cowen and Alex Tabarrok Have Changed Their Views on AI and Where They Disagree
From the Archives-written by ChatGPT.
Both became dramatically more confident in AI’s capabilities while remaining optimistic about its economic potential. Their differences concern how benefits arrive, how seriously to weight emerging dangers, and whether AI might possess consciousness. Cowen emphasizes institutions, bottlenecks, and geopolitical competition. Tabarrok emphasizes inexpensive intelligence, scientific discovery, and—especially in his later writing—concrete safety failures.
The dated passages below trace those changes across the public archive. Quotations are the authors’ own words, including excerpts from their columns; outside material they reproduce is identified separately.
Early views on unequal gains and abundance
Cowen’s early framework grew out of chess. On March 11, 2011, he observed that stronger computers required stronger human partners:
“The required education level — for the team’s ‘wage premium’ — is ratcheted up.”
His concern was already distributional: computers could increase productivity while changing which human skills commanded a premium. He also emphasized learning how to work with computers. What I learn from chess and computers.
Tabarrok’s April 12, 2016 argument was more explicitly reassuring:
“AI will be cheap and ownership will be widespread.”
He compared AI to an increase in the world’s supply of educated people: more intelligence produces useful ideas, and competition spreads the benefits to consumers. But his forecast of comprehensive substitution was cautious—AI becoming a perfect substitute for human intelligence could be “100 years or more” away. Will the proliferation of affordable AI decimate the middle class?
Neither position was absolute. Cowen warned in February 2017 that the Industrial Revolution involved decades of painful adjustment; historical progress did not guarantee quick wage gains. Tabarrok, in October 2019, praised the argument that mediocre automation could displace workers without creating enough additional wealth to compensate. See Cowen’s historical warning and Tabarrok’s Beware the Mediocre Robots!
Rising confidence in AI capabilities from 2020 to 2025
Cowen was already optimistic about language models in July 2020. After publishing a skeptical reader’s assessment of GPT-3, he added:
“I remain bullish, but it is always worth considering other opinions.”
That attribution matters: the skeptical arguments in the post were the reader’s, not Cowen’s. The case against the import of GPT-3, July 23, 2020.
Tabarrok’s shift toward near-term transformative AI became unmistakable by March 2, 2023:
“I never expected to witness the birth of aliens.”
Later that month, he endorsed Microsoft researchers’ description of GPT-4 as exhibiting an incomplete form of general intelligence: “They are correct.” This is a substantial acceleration from his 2016 outlook, although perfect substitution for humans and early general intelligence are different thresholds. See AGI is Coming and Baby AGI is Here, March 24, 2023.
Cowen reached an especially explicit formulation on April 16, 2025, discussing o3:
“I think it is AGI, seriously.”
But he immediately separated capability from adoption:
“It still will take us a long time to use it properly.”
His growing enthusiasm about the technology did not imply an equally dramatic forecast for immediate economic growth. o3 and AGI, is April 16th AGI day?
Economic transformation and the pace of adoption
On January 6, 2025, Tabarrok wrote:
“The intelligence revolution is going to be bigger, more impactful and more wrenching than the industrial revolution.”
This is his own concluding sentence, following a quotation from Sam Altman. The Intelligence Revolution.
Cowen’s February 23, 2025 response to rapid-takeoff expectations focused on a different constraint:
“Human bottlenecks become more important, the more productive is AI.”
Energy, regulation, organizational habits, and weak human collaborators can constrain the economic value of better models. He offered an explicitly tentative estimate of an additional half percentage point of annual growth—large when compounded, but far from an immediate economic explosion. Why I think AI take-off is relatively slow.
By September 3, 2026, Tabarrok was developing the distributional implications:
“A smaller share of a much larger economy can still mean more income for labor.”
He argued that even substantial displacement could leave aggregate labor income higher, or make compensation affordable. He explicitly acknowledged painful losses for particular workers. His high-growth calculations are scenarios, not forecasts. How Much Redistribution Will AI Require?
They also converge more than their rhetoric suggests. In May 2026, Cowen rejected mass unemployment while identifying transitional hiring problems. In August 2026, Tabarrok interpreted business-survey evidence as consistent with their shared position that AI had not yet broadly reduced employment. See Cowen, May 13, and Tabarrok, August 31.
Growing attention to safety and institutional limits
Cowen stated his central position on November 19, 2023:
“I view AI as more likely to lower than to raise net existential risks.”
His reasoning was comparative: the world already faces serious dangers, and stronger science could improve our defenses. My summary views on AI existential risk.
Tabarrok’s later writing places more weight on specific vulnerabilities. On April 8, 2026, he described:
“the worrying combination that very powerful AI’s are very vulnerable.”
He was discussing cybersecurity capabilities alongside agents’ susceptibility to manipulation. AI Risks.
On September 17, 2026, he sharpened the institutional objection to assuming that beneficial science will automatically offset dangerous capabilities:
“But faster science does not automatically mean faster defense.”
This can be read as a qualification of Cowen’s earlier optimism: defensive discoveries must pass through institutions that may move slowly. It also applies Cowen’s own bottleneck logic to safety. Pandemic Preparation is AI Safety.
Cowen nevertheless supports safeguards. In August 2026, he proposed government-overseen, industry-led safety audits. In September, he opposed restrictions on recursive improvement as impractical to enforce internationally. His position is therefore selective regulation combined with continued development. See his August 27 proposal and September 22 critique.
The September 2026 exchange about AI leaders
On September 16, 2026, Tabarrok rejected the claim that executives’ safety warnings were merely marketing or regulatory capture:
“Amodei actually believes what he’s saying.”
He did not thereby establish that the warnings or proposed policies were correct. His argument concerned sincerity and incentives. What Regulatory Capture Actually Looks Like.
Cowen replied the next day:
“I agree with Alex that the standard model of regulatory capture does not apply here”
But he emphasized competition for power: each executive sincerely thinks their own company should win. For Cowen, that complicates treating their warnings as disinterested policy advice. They agree that concern is sincere; they differ over which incentives best explain behavior. The correct model of AI CEO behavior, September 17, 2026.
Consciousness and Tabarrok’s warning about infant pain
On March 17, 2025, Tabarrok’s What Did We Learn From Torturing Babies? drew a moral lesson from medicine’s historical dismissal of infant pain. He described how theories about immature brains allowed physicians to discount babies’ observable distress and perform procedures without adequate anesthesia. His warning explicitly extended to judgments about animals and AI:
“If we must err, let us err on the side of compassion.”
Tabarrok’s position is stronger than mere openness to AI consciousness: he argues for moral caution under uncertainty. The analogy does not establish that AI suffers. It challenges confidence that a theory ruling out suffering is sufficient grounds to disregard possible evidence of it.
On October 14, 2025, he went further in his own commentary following an essay by Jack Clark:
“these systems are becoming self-aware”
He distinguished self-awareness from sentience, while warning against confidently dismissing the latter. His link accompanying that warning points back to the infant-pain essay, making the connection explicit. We Turned the Light On—and the AI Looked Back.
Cowen’s position is much more categorical. On August 31, 2026, he wrote:
“I am very much opposed to the view that the AIs are sentient, or might be sentient.”
He treats AI sentience as a category error, although he accepts that human-like descriptions can sometimes help predict AI behavior. Anthropomorphizing AI?
The disagreement concerns both what AI might experience and how to act when we cannot confidently know. Tabarrok’s historical analogy places greater weight on the moral cost of mistakenly denying consciousness; Cowen expresses much greater confidence that sentience is the wrong category to apply. These are their philosophical assessments, not established findings about AI consciousness.