Category: Law
Did the ACA reduce mortality?
Many of us brought up related points at the time, but basically we were booed off the reservation:
While recent research has provided evidence that the Medicaid expansions of the Affordable Care Act (ACA) reduced mortality, there is no evidence on the effect of the Affordable Care Act (ACA) net of the Medicaid expansions on mortality. This is an important gap in knowledge because the ACA significantly increased health insurance coverage in non-expansion states. In this article, we exploit the large increase in health insurance coverage brought forth by the ACA to examine the effect of the ACA and Medicaid expansions on mortality. Unlike prior studies that relied solely on geographic variation in Medicaid expansions to estimate the net effect of the expansion, we use a novel empirical approach that allows us to investigate the effect of the ACA net of Medicaid expansion on mortality, the incremental effect of the Medicaid expansion, and the overall effect of the ACA including Medicaid expansion. We use longitudinal data from the NHIS Linked Mortality Files (LMF) and a nationally representative sample of 40 to 58-year-olds combined with a difference-in-differences and a difference-in-differences-in-differences research design to obtain estimates of the effect of the ACA on mortality. We find no evidence that the Medicaid expansions had a beneficial effect on mortality but do find that the ACA net of Medicaid expansion reduced mortality.
That is from a new NBER working paper by
AI, Redistribution, and the Size of the Pie
Anthropic’s economic team, including Anton Korinek and Chad Jones, have a valuable new paper, Economic Scenarios for Transformative AI. They make their assumptions explicit and provide a scenario explorer that lets you change them. How capable will AI become? How quickly will firms adopt it? Will it augment workers or automate their tasks? You can see what different answers imply for growth, wages, and unemployment.
In their extreme scenario AI takes on a lot of tasks, GDP is 32.4% higher by 2030 than without AI and labor share declines from 60% to 45.2% but they make this striking point:
“Total labor income in 2030 in the extreme scenario is almost exactly what it would have been without AI: the labor share falls by a quarter while GDP rises by a third, and 0.45×1.32 ≈ 0.60.”
Exactly. That is the central point of my paper, How Much Redistribution Will AI Require. A falling labor share does not necessarily mean falling labor income. Workers can receive a smaller share of a much larger economy and still earn as much as they would have without AI.
Using the code behind their scenario explorer I updated their results to a 10 year horizon and plotted them on my redistribution graph. Only under the modest scenario is some net labor transfer required to make AI Pareto improving at the aggregate level.
Aggregate labor income, of course, conceals differences among workers. Korinek et al. find that cognitive occupations lose income while other occupations gain. In their extreme scenario, restoring the cognitive occupations’ wage bill to its no-AI level would require about 9% of GDP. They argue that compensation on this scale in response to technological change has no precedent.
I think this makes the adjustment problem look too pessimistic.
First, adjustment happens through retirement and entry. A retiring accountant need not retrain as a nurse. A young person enters nursing rather than accounting. Neither becomes unemployed even as labor reallocates. Korinek et al. understand these channels but give them limited scope in their model. Admittedly, those margins don’t do much work by 2030 but they matter over ten years.
Second, compensation can take the form of shifting taxes from labor to consumption. In the long run labor’s share of consumption tends to equal its share of GDP, so the lower labor’s share becomes, the more relief a given tax shift provides. At a 45% labor share, each dollar shifted from labor taxation to consumption taxation reduces labor’s net burden by 55 cents. Shifting taxes worth 5% of GDP would thus provide net relief to labor of 2.75% of GDP, without increasing total tax revenue. Unemployed workers would still need payments, but compensation need not come entirely through additional government spending.
Third, we do have experience expanding income support rapidly. U.S. unemployment benefits reached approximately 2.5% of GDP in 2020, and that during a contraction. Britain’s compensation to slaveowners following abolition amounted to roughly 5% of GDP in a one-time settlement. These episodes show that governments can mobilize substantial resources for compensation. Moreover, the extreme AI scenario brings an enormous increase in output from which to finance compensation.
Preserving aggregate labor income does not protect every worker. But even the extreme Korinek scenario reinforces the point that a dramatic decline in labor’s share can coexist with stable or increasing aggregate labor income. To the extent labor income does decline, growth makes compensation affordable and attrition, entry, and tax shifting can make the intra-labor task smaller than it first appears.
Does AI assistance enhance or erode expertise?
From a new NBER working paper:
Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.
That is by David Autor, et.al. Do note that over time the allocation of humans to tasks will evolve so that more of the humans become more productive, not less. RCTs somehow have the odd disadvantage of requiring too many things to be held constant, and so they can miss the benefits of longer-term adjustments.
Diversity Is Our Strength?
Diversity is our strength is a common motto. Indeed it is one of GMU’s core values but what is the scientific evidence for this thesis? A new scoping review:
Recent years have witnessed many strong claims that ‘diversity’ leads to more original and impactful science, which is a science-focused form of what we call “The Diversity Hypothesis.” However, what evidence supports the claim that diversity enhances scientific output or impact? This pre-registered rapid scoping review seeks to collate and evaluate the scientific evidence for the Diversity Hypothesis…
…Based on over 100 scientific articles, we find that only between 15% and 28% of results reported in the literature are consistent with the hypothesis, with the balance of the results not being consistent with it.
…Overall, the results of the analysis section indicate that there is little empirical evidence that diversity improves scientific output and/or impact. In fact, with the possible exception of disciplinary diversity—a type of informational or viewpoint diversity operationalized at the team level—the majority of the evidence seems to point in the other direction. These findings are robust regardless of how the data is parsed and analyzed. The same conclusions can be drawn looking at the full data set, only the population-adjusted results, or only the results of high quality based on the MMAT analysis.
Hat tip: Colin Wright.
Markets do respond to AGI news
Here is the link. What are the changes in the other market prices telling you?
My very interesting Conversation with Jared Diamond
Here is the audio, video, and transcript. Here is part of the episode summary:
So which leaders do make a difference?
Tyler and Jared debate that question, including conditions what a religious founder needs to succeed, whether destructive leaders matter more than constructive ones, why individual leaders have mattered so little in Papua New Guinea, whether we undervalue preventers, how much Rachel Carson and the Resnicks really mattered, whether Botswana’s success will endure, Jared’s own place in intellectual history, and what he’d tell an ambitious 25-year-old about how to become consequential. They go on to cover whether New Guineans are smarter, his reasons for why the Industrial Revolution started in England, what he learned from Jim Robinson about natural experiments, why he’s more optimistic about the environment than when he wrote Collapse, what he’d do about nuclear risk, the 189 bird species on his street in Los Angeles, why the MacArthur “genius grant” left him depressed for a week, why he’s listening to one Bach cantata a week, what he hopes to work on as he enters his nineties, and more.
Excerpt, we start with this:
COWEN: Who do you think is the least replaceable leader in human history?
DIAMOND: Wow. Wow. The least replaceable leader in human history. Frankly, my answer is none. There has been no unreplaceable leader in human history because over a long time, things wash out. A candidate, of course, would be Jesus Christ, but there were half a dozen other prophets in the Roman Empire at that time discussed in the New Testament. If Christ had died in childbirth, one of those other prophets might have taken over. I would say that in the long run, there is no leader that has made a difference.
COWEN: Well, you say “might have taken over,” but that’s probabilistic, right? Say with a 40 percent chance, someone else would have taken over. It still seems in expected value terms, Christ made an enormous difference.
DIAMOND: Certainly, his religion made an enormous difference. Christ himself was the charismatic founder, but a religion needs three things: It needs a charismatic founder, it needs an organizer, and often it needs a general because new religions often face opposition. The organizer for Christianity was St. Paul. In the case of the Mahdi’s religion of the Sudan in the late 1800s, the Mahdi was the charismatic founder, and he was the organizer, and he was the general. In the case of Islam, Muhammad was the founder and the organizer and the general. Religions may have three roles, some of which are sometimes played by the same person.
COWEN: Without the figure of Muhammad at exactly the time he came along, is it not quite easy to imagine a Middle East not unified under Islam, not unified with the common language of Arabic? In some ways, maybe a bit more like the Caucasus, which remains a crazy quilt of different patterns. Thus, Muhammad is one of the three or four most consequential leaders ever.
DIAMOND: That’s possible. That’s a big question. It’s harder to become clear about Muhammad than it is about Christ, because we don’t know what the weather was like in Arabia at that time. Weather makes a big difference. If it was rainy, that would mean lots of plant growth for lots of babies and for feeding an army. We just don’t have information on the weather in Arabia at that time.
A counter-example is Genghis Khan, who developed the largest land empire in world history, all the way from China to Poland. Recently, why did Genghis Khan do it when there were other steppe leaders like Adolf Hühnlein and Timur the Turk? We’ve learned recently that the wettest time on the steppes in the last 2,000 years was around the time of Genghis’s birth. He was born at exactly the right time at the right place. If he had been born 200 miles further away and 20 years earlier, we wouldn’t know anything about him. In the case of Muhammad, we just don’t know whether the time of his birth was a particularly propitious time to found a religion in that area or not.
COWEN: The very notion that the two first names we’re talking about, they’re both religious leaders, right? They’re not kings. They’re not inventors. Do you think that suggests a vision of the world where it’s ideas that really matter and somewhat at variance with your own geography-based theories?
Interesting throughout, and we debated pomegranate juice as well. I enjoyed reading Jared’s new book Profits, Prophets, Coaches, and Kings: (When) Do Leaders Matter?
No Doing, No Learning
A regulation that raises gasoline prices makes people angry but when regulation prevents an industry from ever existing, most people never learn what they lost.
Consider nuclear power. Overnight construction costs for early U.S. demonstration reactors fell 81 percent between 1954 and 1968. For reactors begun between 1967 and 1972, costs rose 187 percent. The 1971 Calvert Cliffs decision and then the reaction to Three Mile Island in 1979 accelerated the cost increases and slowed construction even more.
What might have happened had the earlier learning and deployment trends continued? Peter Lang’s 2017 paper in Energies estimates that nuclear power would have cost about one-tenth as much by 2015. The additional generation could have avoided as many as 9.5 million premature deaths.
The number is staggering even if quartered. Millions of deaths. Yet, the graveyard was both invisible and silent.
(Furthermore, climate change would not be a problem today had nuclear power not been handicapped.)
The nuclear story you probably know but Niko McCarty has an excellent new Works in Progress piece on an invisible graveyard of technology that I knew almost nothing about.
In the 1980s, officials decided to regulate engineered microbes under the Toxic Substances Control Act, a law written for industrial chemicals. Microbes swap genes all the time, but EPA treats them as “new” whenever DNA is introduced from another genus. As a result, even a marker gene used simply to identify successfully edited cells can trigger review if it remains in the organism. McCarty reports that researchers filed more than 240 applications between 1987 and 2018: “Only a few were ever approved for widespread use.”
What have we lost? We can’t know for sure but among the plausible losses McCarty discusses are engineered bacteria that detect buried explosives, microbes that extract rare-earth metals and improved plastic-digesting enzymes. Similarly, Chernia et al. write:
Promising many societal benefits, emergent products of biotechnology involve releasing genetically modified microbes (GMMs) into the environment. However, regulatory challenges limit their use. So far, GMMs have mainly been tested in agriculture and environmental cleanup, with few approved for commercial purposes. Current government regulations inadequately address modern genetic engineering and limit the potential of gut therapeutics, skin products, self-repairing materials, ocean pollution treatment, anti-corrosion coatings, etc.
And those are just some of the plausible first-stage losses. Perhaps even more importantly, we learn by doing. Thus, no doing, no learning. The first approved product is rarely the safest or the best but when we fail to approve the first we don’t get the much better 5th. Forty years of that process could have taken us well beyond the applications McCarty describes. We must build to build better.
Nuclear power at least left us enough reactors to get some idea of what we lost. With microbes, I needed McCarty to tell me there was something missing even though I study these issues for a living. And note one reason new microbe loss was invisible. No vote was ever taken. No debate was ever had. Officials in the 1980s reasoned that genomes are made of chemicals and chemicals are covered, and that doctrine has governed the field ever since despite being overly broad and excessively costly.
How many other gaps in our technology have explanations buried in the Federal Register?
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Here is a video version of this post. Which do you like better?
And it begins…(a good start…)
Wall Street banks are pushing large law firms to cut fees, arguing that the business model that has enriched top lawyers for decades is not sustainable in an era of AI.
Morgan Stanley and Citigroup have told major law firms they want to set up new payment arrangements that would save them money, the banks’ in-house lawyers told the FT…
“If the number of hours they’re working on a matter has come down because of AI . . . our expectation is for costs to come down significantly per transaction,” Adam Meshel, global head of legal at Citigroup, told the FT.
He said the bank had started asking law firms to bid for work, explaining during the bidding process how much they were saving using AI…
The ability to complete tasks more quickly could mark “a fundamental altering of the revenue foundation for these mega firms”, he said.
Here is the full FT piece by Kaye Wiggins and Joshua Franklin. I have been predicting exactly this in many of my talks…
Who values democracy?
This paper examines the conventional view that redistribution is central to the democratization process using data from stock markets. Consistent with this view, democratizations have a large, negative impact on asset valuations driven by a rise in redistribution risk. Across 90 countries over 200 years, risk premia are substantially elevated— similar in magnitude to financial crises—prior to and during democratizations. A shift in Catholic church doctrine in support of democracy provides causal evidence that democratizations increase risk premia. Successful democratizations lead to substantial redistribution: the size of the public sector grows, income inequality falls, and the labor share of income rises. An extended version of the canonical redistribution-based model of democratization that includes asset prices can quantitatively explain these effects. Reductions in inequality and increased taxes explain approximately half of the results. The rest comes from greater economic competition and equality in government spending. The model also explains the negligible asset pricing response to autocratizations. Neither an increase in macroeconomic risk nor generic political risk can explain the results.
That is by Max Miller, now published in the JPE, ungated copy here.
Russia markets in everything
While Western export bans have severed Russia from much of the auto market, they have not tempered the demand for brand-name SUVs and trucks. That has given rise to a sophisticated network of criminals that steals cars, hides them in shipping containers and sends them to Russia, often by way of the Middle East…
Interpol, the international police organization, received 4,798 reports that stolen Canadian vehicles had been found in Russia from February 2024 to July 2026, according to data obtained by The New York Times and confirmed by two law enforcement officials who spoke on the condition of anonymity because it is considered sensitive.
Pickup trucks like Dodge Rams, Toyota Tundras and Ford F-150s are in particularly high demand.
The Interpol figures significantly undercount the problem, but Russia is by far the top international destination for Canadian cars reported to Interpol, according to the Royal Canadian Mounted Police.
Here is more from Jane Bradley and Michael Swirtz at the NYT.
Scholar Data
In our paper, A Skeptical View of the National Science Foundation’s Role in Economic Research, Tyler and I point out that if the NSF is doing what it should, it ought to be doing very different things than other funders:
Public goods theory tells us that the National Science Foundation should support activities that are especially hard to support through traditional university, philanthropic, and private-sector sources. This insight suggests a simple test: to the extent that the NSF allocates funds to genuine public goods as opposed to subsidies on the margin, we ought to see a large difference in the kinds of projects the NSF supports compared to what the “market” sector supports. But what stands out from lists of prominent NSF grants (like the one provided by Moffitt in this symposium) is how similar they look to lists of “good” research produced by today’s status quo. If we take public goods theory seriously, what areas of economics should be supported?
We suggest replication studies and support for producing public datasets. Which brings me to Scholar Data, a neat new project that won NIH’s competition to create a data-sharing index. Scholar data creates an S-Index, like an H-Index for papers, but instead the S-Index measures a dataset’s ease of access, citations and other mentions. The point is to create a metric to reward the creation of a public good:
Researchers invest years collecting and sharing datasets that underpin reproducibility, transparency, and discovery across every field. Without shared data, findings can’t be verified, built upon, or trusted. And yet, the metrics that define academic careers, such as citations, h-index, and impact factor, only count publications. Data sharing goes unrecognized and unrewarded.
This creates a broken incentive: researchers are expected to share data, but get no credit for doing so. The result is that data sharing is treated as a chore rather than a contribution.
Scholar Data and the S-index are aimed at fixing this. By measuring how impactful your datasets are, the S-index gives data sharing the same visibility and recognition as publishing, turning it from an obligation into a career asset.
Bravo! Your dataset may already be catalogued. You can get credit at ScholarData.
A Big Bet on Explosive AI-Driven Growth
Wow! The excellent Ben Moll has put together a high-stakes bet on US economic growth.
A bet on near-term explosive AI-driven growth: whether U.S. real GDP per capita will grow by at least 15% within a single year (measured relative to prior peak) by the end of 2033. Agreed on X, 15–16 August 2026. This document summarizes the agreed terms.
1. Parties and stakes
Fast-growth side (wins if the growth condition in Section 2 is met):
● William MacAskill https://www.williammacaskill.com/ — $10,000
● Samuel Albanie https://samuelalbanie.com/ — $25,000
● Tom Cohen https://x.com/tomcohen — $25,000
● Total: $60,000
No-fast-growth side (wins otherwise):
● Benjamin Moll http://benjaminmoll.com/ — $40,000
● Andrew Ho https://andrewho.xyz/ — $200,000
● Total: $240,000Odds: 4:1 — every $1 staked by the fast-growth side is matched by $4 from the no-fast-growth
side. Implied probability of the fast-growth scenario: 20%.
I’m known for quipping that a bet is a tax on bullshit but I don’t think either side is BSing. We have a genuine and important disagreement. I’d side with Moll, for what it is worth.
What should I ask Yiyang Zhuge (诸葛一杨), also known as Zhong Shu (仲树)?
Yes I will be doing a Conversation with her. She is best known to Western audiences for her interview with Christopher Nolan, but she has numerous other achievements:
Yiyang Zhuge is a political theorist, political columnist, and a translator of German and Greek philosophical texts. She is an instructor at Brandeis University, and a PhD Candidate at Boston College.
Yiyang is a major translator of Hannah Arendt into Chinese. She has translated The Human Condition, Life of the Mind, Men in Dark Times, collected poetry, Die verborgene Tradition, and Rahel Varnhagen: The Life of a Jewess. She has given numerous lectures and published numerous public-facing articles on Arendt.
Yiyang published the first Greek-to-Chinese translation of Plutarch’s Moralia, a best-selling philosophy book in 2025.
She runs Princeton University Press’s Political Philosophy Book Club, where she interviews important Western academics for the Chinese audience.
Yiyang is a public intellectual in China. She reports on American higher ed monthly at print magazine Caixin Weekly. She is a political commenter on Hong Kong’s Phoenix TV (here and here). She hosts great books programs on vistopia and has contributed essays on Machiavelli, Rousseau, Tocqueville, Arendt at vistopia. She hosts the most popular Mandarin political philosophy podcast “Monologues of a Committed Observer” with 1m+ subscribers.
So what should I ask her?
The Antitrust Academy
The Antitrust Academy is an online platform hosting hundreds of videos comprising a complete course in antitrust law and economics. Teachers include Judge Douglas Ginsburg, Jon Klick, Joshua Wright and others. I am an advisor. A great resource whether you are learning this material for the first time or need to brush up on some legal or economic doctrines.
The least bad way to regulate AI?
That is the topic of my latest Free Press column. Excerpt:
The key is to create some basic safeguards, but without stifling broader AI progress. To do so, we must defy the conventional wisdom about public oversight and instead trust the AI labs to be their own primary regulators.
My version of the proposal starts with defining a private not-for-profit body for AI regulation. An ideal body would draw some features from FINRA (the Financial Industry Regulatory Authority): a consortium of financial firms that examines the trade practices of each and makes recommendations, helping the federal Securities and Exchange Commission with oversight and regulation. The AI version would include the major labs and would be authorized and overseen by Washington, perhaps through the now-fledgling Center for AI Standards and Innovation.
This body would periodically audit major AI companies and their models, judging their conduct and safety. In the short run at least, much of this would be focused on issues of cybersecurity, and whether the new models created more cyber risk than they help to solve. If a company passed the audit, it would be exempted from standard liability law, at least provided that it had shown basic, reasonable care, as opposed to extreme or deliberate negligence. That would free the AI labs from the fear that courts might derail their business by granting huge awards to plaintiffs for ill-defined harms that could not reasonably have been prevented. And it would give the labs a strong incentive to meet the safety standards of this body.
It is reasonable to wonder whether such a body, composed of industry players, would issue fair and equitable judgments of safety. Maybe not. Yet there are many upsides and no better alternative.
For one thing, each company knows that a dangerous model from another company could cause a harmful incident and damage the prospects for the entire industry. Consider the Three Mile Island meltdown in 1979, which contributed significantly to the mothballing of the entire U.S. nuclear industry. Few people can name the company (Metropolitan Edison) behind the malfunctioning plant; the reputational penalty attached to the industry as a whole.
Another incentive for safety is that the top companies do not want too much competition from lower-price, lower-quality upstarts. That too will induce those companies to support fairly tough standards, perhaps excessively tough in some cases. Still, we are choosing from imperfect alternatives. The concrete truth, whether we like it or not, is that there is far more expertise within the companies for judging AI safety than we can expect to find in the federal government anytime soon.
I am indebted to some ideas from Dean Ball, noting that his proposal is somewhat different. And here are some comments from Brendan McCord.