The US Exports Intelligence
Most Americans work in the service sector so it’s not surprising that most export-related jobs are in the service sector (The U.S. exports about $2.2 trillion of goods and $1.2 trillion of services, but services are more labor intensive than manufacturing so they support more export jobs per dollar.)
Richard Baldwin writes:
In 2022, US service exports supported 8.9 million American jobs.
US manufacturing exports supported 2.2 million.
That’s four-to-one in favour of services. Yet in the national narrative, ‘export jobs’ almost always means things done in steel mills and factories.
…When a household in Germany pays for Netflix, that is an American export. When a Brazilian retailer buys Microsoft cloud capacity, that is an American export. When JPMorgan structures a financial deal in London, or an American consulting firm advises a company in Singapore, those are American exports too.
None of these is shipped in a container. No customs official records them as they clear the customshouse. Yet they are exports since they earn foreign income for America just as surely as the ‘Boeings, Beans and Beef’ that President Trump sold on his recent China trip.
Need I remind you that when OpenAI sells intelligence to people abroad, that is a US export? N.B. this is the future.
World trade in goods expanded roughly five-fold between 1990 and 2020. Trade in digitally enabled services expanded more than eleven-fold over the same period. These are the modern services.
The trade debate is fixated on manufacturing—where America is doing fine—while largely ignoring services, where America is crushing. Increasingly, our most valuable exports travel not on container ships but at the speed of light over fiber.
Europe Demands Family Dynasties
In the US, someone with wealth is free to give it away more or less as they see fit (spousal claims excepted, which partly reflect marital co-ownership). In much of Europe, however, there is forced heirship–a large fraction of wealth must be handed down to children which makes it harder to direct large portions of wealth to charities, foundations, or non-family causes compared to the US. (Louisiana, with its French-Spanish civil law roots, is the one state with forced heirship and even it mostly gutted it in 1995.)
Here is an excellent post by John Arnold who, if he were European, would be required to give 75% of his wealth to his three children instead of spending it on philanthropy as he and his spouse are now doing.
America’s cultural ideal has been the self-made entrepreneur while Europe’s was rooted in aristocracy, with status inherited rather than earned. Europe’s inheritance laws show this divide.
Many European countries have “forced heirship” laws that require people to leave 50-75% of their estates to their children. Want to leave the majority of your wealth to charity? not allowed. Your kids are estranged from you, struggling with addiction, or irresponsible? still required to give them the money. Want your kids to avoid a life of entitlement? tough.
Incredibly, these laws look back at transfers made during your lifetime. If you have 3 children in France, you’re required to bequeath them a minimum of 75% of your estate. Because French law calculates this based on your assets at death plus all lifetime gifts, giving away more than 25% of your wealth while alive means your heirs can legally sue to force charities or foundations to return the funds. This has limited the development of the nonprofit sector on the continent.
The cultural gap between an entrepreneurial society and one shaped by dynastic wealth is enormous. If you make it yourself, you tend to want your kids to do the same. If you inherit it, the primary goal is protecting the estate for the next gen.
Countries like Spain, France, and Italy legally entrench family dynasties, while America has historically sought to limit them through estate taxes. The result is not only a weaker culture of philanthropy and civil society in Europe, but also less economic dynamism.
It’s interesting that in Capital Piketty discusses required equal division to children as an egalitarian legacy of the revolution but, as far as I recall, never reflects on the fact that forced heirship prevents a French entrepreneur from giving his fortune away to charity. A case for laissez-faire, no?
80,000 Hours: The Book
Forty hours a week, fifty weeks a year, forty years: a career is about 80,000 hours. Yet it’s striking how little serious thought goes into career decisions relative to, say, choosing a mortgage. Indeed, you are almost supposed to tell a story about how a random incident changed your life. One summer a circus came to town—and that’s the whole reason I became an economist! (True story!). Career advice, when it exists, often amounts to the platitude of “follow your passions!” Ugh. If you ask people what their passions are, music, arts and sports top the list but guess what? There aren’t enough jobs in those categories to go around.
Benjamin Todd’s newly updated book, 80,000 Hours is a unique examination of careers that runs the numbers in a serious way. The book is framed along Effective Altruism lines and it has some good public policy material. Pandemics, for example,
The world has plenty of religious cults, despots and would-be school shooters who might decide they want to take everyone else down with them…. The world [c]ould be one lab leak away from catastrophe.
Given what we know about the pace and accessibility of bioengineering tools, the chance that there will be a pandemic that kills over 100 million people during the next century seems high, plausibly similar or greater than the risk of large-scale nuclear war or climate change above six degrees. An engineered pandemic could also kill over 90% of the population,suggesting its overall scale is significantly larger.
But risks from pandemics are, even now, far more neglected than either of these. In comparison to $6bn–$10bn of philanthropic funding for climate change, and $1.6 trillion of total climate finance, pandemic prevention only receives $1bn of philanthropic funding, and total spending aimed at reducing the chance of worst-case pandemics is probably under $10bn.
See also my paper Pandemic Preparation Without Romance on what to do about it.
The opening chapters present the EA framing but most of the book has good advice even for the purely selfish–advice on building skills, networking and how to actually get a job. From what I have said so far, one might get the impression that the idea is to rationally choose your career at age 16 and then optimize your life around that plan. Not so! Todd rightly divides career paths into explore, build and deploy categories. Most people under-explore. It’s ok to jump around jobs and places, especially when you are young, so long as you are building skills and not just accumulating items for the CV. There’s evidence, for example, that scientists’ best work tends to follow periods of exploration with exploitation.
I also appreciate that Todd specifically warns about about armchair theorizing. Pro-and-con lists, for example, are ok but far less useful than getting out of the chair and actively exploring. Go talk with people, try something for a week, go somewhere. Look for cheap tests.
Start with what’s easiest. We often find people who want to, say, try out economics, who then apply for a master’s degree. That’s a huge investment of time. Instead, think about how you can learn This could mean first reading an economics textbook, or taking a single course.
You can think about creating a ‘ladder’ of tests. Start with the cheapest ways to test your options, then after each step, re-evaluate. A ladder might look like this:
a. Read our relevant career reviews, all our research on a given topic, and talk to LLMs about what the jobs are like (two to five hours).
b. Speak to someone in the area (two hours).
c. Speak to a friend to get an outside perspective on what’s best (two hours).
d. Speak to three more people who work in the area and read one or two books (twenty hours).
e. Given your findings, look for a relevant project that might take one to four weeks of work – like applying to jobs, volunteering in a related role, or doing a side project in the area – to see what it’s like and how you perform.
f. Only then consider taking on a two- to twenty-four month commitment – like a work placement, internship or graduate study. Being offered a trial position with an organization for a couple of months can be ideal because both you and the organization want to quickly assess your fit.
80000 Hours is The Random Walk Down Wall Street of career advice, the one book that really matters.
Explore, build rare and valuable skills, point them at a meaningful problem, and passion will follow rather than lead. And for those who don’t want to read a book, speak to an 80,000 Hours advisor. It’s a very cheap test.
Why are Murders Down in Baltimore?
In 2015 I wrote Baltimore Arrests are Down and Crime is Way Up and, as I predicted, Baltimore tipped into an high crime equilibrium. After the Freddie Gray riots, arrests declined and crime shot up but crime stayed high even after arrests rebounded. In my view, the surge fed on itself: higher crime strained police resources, and that strain—in and of itself—reduced the probability of punishment, sustaining the high-crime equilibrium, as in my crime wave paper.
Yet, beginning around 2022 crime in Baltimore—most especially murders—began to fall.
In April, Baltimore had four homicides, the lowest total for any single month since at least 1970. So far this year, there were 38, compared with 51 in the same period last year. At the current rate, Baltimore would end 2026 with fewer than 100 homicides. There were 323 just four years ago.
How did we get from a city in which the question was how high can crime rise, to one where the question is how low can it go? The answer might be linked to the nationwide decline in murder, spurred by a restoration of policing as the excesses of the George Floyd years recede. But that raises the question of what cities across the country are doing right.
So what caused the decline? We can’t be entirely sure as national trends confound but Charles Fain Lehman has a good piece in the FP arguing plausibly that the answer boils down to carrots, sticks and the non-random nature of murder. Begin with the latter. A significant subset of murders are highly predictable. A gang member gets gunned down today. Next week, you can expect retaliation. Moreover, you know who is going to do the murder even more than you know who is going to be murdered. Namely, a close associate—a fellow gang or family member—will be the one to do the killing. Sometimes pre-Cog is not so hard.
So with this in mind, Baltimore, under a new mayor and tough on crime prosecutor, began to intervene in the murder cycle before it happened, i.e. a focused deterrence program based on Boston’s Operation Ceasefire.
The approach involves a detailed investigation of every shooting that happens in the city. Every week, the Baltimore Police Department and its partners review the week’s incidents….For every shooting, GVRS prescribes reaching out to known associates of the victim.
…At one recent coordination meeting, about 20 people gathered around the table of the conference room at Baltimore’s Doxa Ministries Church Without Walls. Under the direction of Reginald Williams from the Mayor’s Office of Neighborhood Safety and Engagement, they talked through two new “referrals” associated with the victim of a recent shooting. One had a long criminal history and was on house arrest. Another, barely an adult, was himself a victim a few years earlier.
Both men will have their doors knocked on by several of the meeting’s attendees. They will be offered services—job training, tattoo removal, relocation, whatever they need to get out of the “life.” But they will also get a clear message, delivered verbally and in the form of a letter from Mayor Scott: Baltimore is watching them—and will come after them.
Carrots, sticks, and a little Pre-Cog. Together they appear to be working.
Doc in a Box
The first review of the pilot for AI prescriptions refills in Utah is out and it looks very reasonable. In the 72% of cases where the AI recommend a refill at least one of two physicians agreed in 97% of cases.
In the 28% of Cases Where the AI Escalated to a Physician Without Recommending Renewal
o When the AI declined to recommend renewal without further information, a human telehealth appointment was arranged.
▪ For these patients, 69% of physician reviews agreed that the escalation was appropriate, and more information was needed to authorize a renewal.
▪ In the other 31% of cases, the physician determined the escalation was overly cautious.
● For a new system like this, overcaution is appropriate and welcome. In the long term, reducing overcaution without compromising safety would improve patient access to care, but we aren’t rushing to see that happen.
The founders of Doctronic, the firm running the AI doc, write:
The cost of compute drops roughly 10x every five years. At the same time, the demand for care continues to rise. An AI consultation that costs a few dollars today will cost pennies in a few years. So if AI can safely handle even a fraction of care, we’ve turned an unsolvable supply problem into an engineering problem. And engineering problems have solutions.
A Beautiful Theory Falls to Ugly Data
My latest paper, A Test of the Coase Conjecture Using Prices of Electronic Books, with the excellent Tim Groseclose, has just been published. The Coase Conjecture is another one of Coase’s little ideas — the original paper is six pages — that has spawned hundreds of follow-up papers and thousands of citations.
The idea is simple. A monopolist of a durable good has a time-inconsistency problem. Set the monopoly price in period 1 and he will be tempted in period 2 to cut the price and mop up the customers whose valuations sit between the period-1 price and MC. But the same logic applies in period 2, and again in period 3, and so on — eventually the price unravels to MC. Consumers see this coming, the monopolist knows the consumers see it coming, and so the monopolist cuts price to MC in period 1. And since a “period” is just the interval between price changes, the whole unraveling happens — in Coase’s phrase — “in the twinkling of an eye.”
The theorists, most notably Gul, Sonnenschein and Wilson and Fudenberg, Levine and Tirole, formalized Coase’s insight and showed that under quite general conditions the logic goes through. Which is rather surprising, since, as Tim and I point out, Coase’s conjecture implies that many patents and copyrights are essentially worthless — a prediction wildly at variance with the facts. Other theorists, including Stokey, Ausubel and Deneckere, and Board and Pycia, have offered variants under which the Coase outcome does and does not obtain.
For all this theory, there have been almost no direct tests of the Coase Conjecture apart from a handful of lab experiments. Ours is one of the first papers to take the conjecture to the real world. We look at e-books, an unusually clean setting: digital goods are durable, marginal costs are low, resale is limited, and prices can be changed quickly. Using the prices of e-books that are in the public domain as a proxy for marginal cost, we ask: (a) do prices rapidly fall to MC, and (b) does the market clear in the first period? The answer to both is no. E-book prices begin well above MC, sales continue over many periods, and prices don’t even decline monotonically.
We reject the Coase Conjecture decisively.
The paper has an interesting history. The theorists (or the referees we guessed were theorists) praised the paper for taking the theory seriously but inevitably had a fillip to offer, distinguishing the world of pure theory from empirical tests. The empiricists, on the other hand, said our tests were too simple since no one takes the theory that seriously. It’s good to see the paper find a home!
We reject the Coase Conjecture decisively, but it remains to say why. We can rule out some explanations — it’s not rising MC, and it’s not the finiteness of buyers (which can support a perfectly price-discriminating Pac-Man equilibrium).
Two theories remain: 1) sellers can commit not to lower prices, and 2) the outside-options model of Board and Pycia. I prefer the former, my co-author prefers the latter. To me, commitment just isn’t that hard. The standard story is that profits are like cookies on the table and the monopolist can’t resist — but at least the people tempted by cookies get to eat the cookies! The Coase profits are illusory: the monopolist races to MC in period 1 precisely because they know they won’t resist later and as a result they don’t even get a taste of profit! Too clever by half. I say, show some backbone. Firms are *all about* commitment — to workers, consumers, contractors. Why not to a price? My co-author points out, however, that this is more Tabarrok-vibe than carefully laid out theory.
Tim likes the Board and Pycia model which begins with the plausible idea that consumers have outside options — if they don’t buy the book today, they will buy another book, rent the movie, or borrow from the library — and crucially, once they take the outside option, the consumer never returns to the market. You might think outside options would make it *harder* for the firm to set a high price, but Board and Pycia show in a very clever but extended argument that when you carefully work out the full equilibrium the opposite holds: outside options give firms a time-consistent incentive to set and keep a high price. Tim explains the argument further here (see also our paper for an intuitive breakdown).
In any case, the Coase Conjecture — at least as modelled by the theorists — fails in an environment most conducive to it.
A beautiful theory falls to ugly data.
Liberal Economists Score an Own Goal Against Bezos
Jeff Bezos tweeted:
Yes, the United States has the most progressive tax system in the world. The top 1% pay 40% of taxes, the bottom 50% pay 3% of taxes. We can make it even more progressive by zeroing out taxes on the bottom half. It’s a small amount of the total tax revenue but very meaningful to people in this group.
Strangely, a chorus of liberal economists rushed to attack Bezos. Gabriel Zucman replied:
Contrary to what you claim, working-class people contribute significantly to funding American society today. Payroll taxes and consumption taxes absorb a high fraction of their income.
Justin Wolfers piled on:
If you only count the progressive taxes the U.S. levies, then the U.S. system is quite progressive. But if you also count regressive taxes (payroll taxes, sales taxes, etc), it’s not very progressive.
Bezos called for cutting taxes on the bottom half to make the tax system more progressive and the redistributionists came out swinging–to argue he was wrong about how progressive the current system already is. Own goal. Heretics are worse than unbelievers.
But there’s a second, more interesting thing going on. To make the regressivity case, Zucman and Wolfers have to count payroll payments as taxes. That cuts directly against eighty years of liberal doctrine. Beginning with FDR, the argument on the liberal side has always been that payroll taxes are not taxes but contributions or premiums entitling the payer to benefits as an “earned right.” Here’s FDR to Luther Gulick in 1941:
We put those payroll contributions there so as to give the contributors a legal, moral, and political right to collect their pensions and their unemployment benefits. With those taxes in there, no damn politician can ever scrap my social security program.
That framing isn’t a historical curiosity. It runs straight through liberal social security stalwarts like Arthur Altmeyer, Wilbur Cohen, and Robert Ball, and it’s alive today in Nancy Altman and Eric Kingson’s Social Security Works!, which attacks billionaires and insists Social Security benefits are “earned compensation.” The whole political durability of the program–the third rail–rests on this framing.
So the modern left wants it both ways. When the question is whether to cut Social Security, FICA is a premium and benefits are earned compensation. When the question is whether the tax system is progressive, FICA is suddenly a regressive tax. Pick a lane.
Is there a principled way to resolve this? Yes, and it follows Jim Buchanan (see my earlier post here) and Larry Summers who laid out the economics in his classic paper Some Simple Economics of Mandated Benefits. The principled test is whether a payment reduces labor supply. The wedge between marginal product and the worker’s reservation wage isn’t the statutory rate–it’s the gap between the mandated payment and the worker’s marginal benefit. Sylvain Catherine made exactly this point in reply to Wolfers:
Payroll taxes are not regressive! They are mandatory contributions to a retirement system that offers higher rates of returns at the bottom than at the top.
Consider a forced savings program: everyone must pay 12.4% of income into a 401(k). Is this a tax? For someone who was going to save 15% anyway, not at all. For someone who was going to save 10%, only the extra 2.4% bites. Mandatory does not mean tax. The marginal valuation of the mandated benefit is the key.
Now apply this to the two payroll taxes.
Medicare (HI): Every marginal dollar buys zero marginal benefit. Thus, it’s a tax. Part A eligibility is binary–40 quarters gets you in–and once in, your benefit is whatever Medicare spends on your care. No relationship on the margin. (Moreover, the raw HI schedule is unambiguously progressive: 2.9% flat, rising to 3.8% above $200K/$250K thresholds, plus the NIIT.)
Social Security (OASDI): The 90/32/15 Primary Insurance Amount bend points mean a low earner gets a much better return than a high earner. So the gross statutory rate is flat-then-regressive; but the net rate is progressive. In short, OASDI isn’t a tax for low earners but it is a tax for higher earners, thus the tax is progressive.
So: HI is a progressive tax. OASDI is a contribution at the bottom and a tax at the top. Either way, the Zucman-Wolfers framing—payroll payments as straightforward regressive taxes—is wrong and rhetorically it abandons the framing the left has spent eighty years building to protect these programs.
Personally, I’d prefer a system truer to the old rhetoric–a forced savings program with a closer connection between marginal payments and benefits. But if the left wants to reframe Social Security contributions as taxes, and thus make Social Security all about redistribution to the poor, rather than a wise savings program, roll the dice. Just remember that Altmeyer, Cohen, and Ball spent decades building the “earned right” framing precisely because they understood it was the program’s structural defense against means-testing and privatization. Drop the framing and you drop the defense. I suspect the privatizers at AEI and Cato will happily take that trade but the left may come to regret making it for them.
The AIs are “One of Us”
A general purpose AI model from OpenAI has produced a (dis)proof of an important conjecture. Tim Gowers writes:
AI has now solved a major open problem — one of the best known Erdos problems called the unit distance problem, one of Erdos’s favourite questions and one that many mathematicians had tried.
A number of prominent mathematicians comment. I enjoyed Thomas Bloom’s comments:
This was one of Erdős’ favourite problems – he first asked it in 1946 [14] and returned to it many times. (The site www.erdosproblems.com, on which it is Problem #90, currently lists 14 separate references, and there are no doubt more.) The influential collection of ‘Research Problems in Discrete Geometry’ by Brass, Moser, and Pach [8] describes it as ‘possibly the best known (and simplest to explain) problem in combinatorial geometry’. For an AI to produce a solution to a problem of this calibre is both surprising and impressive.
…On examining the construction, it becomes more clear how people had missed this before – it requires the confluence of several different unlikely events: that a good mathematician is
(1) spending significant time in thinking about the unit distance conjecture in the first place;
(2) seriously trying to disprove it, despite the oft-repeated belief of Erdős that it is true;
(3) believes that there is mileage in generalising the original construction to other number fields,
and so is willing to expend significant time in exploring such constructions; and
(4) sufficiently familiar with the relevant parts of class field theory to recognise that the appropriately phrased question about infinite towers of number fields with appropriate parameters can be solved using existing theory.The AI met all of these criteria, and its success here echoes previous achievements: it often produces the most surprising results by persevering down paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery.
…perhaps some in the area will be a little disappointed with how little this tells us: it does not introduce any powerful new geometric tools, or hitherto unsuspected structural results, that a proof of the unit distance conjecture would likely have called for. Still, while perhaps not the proof of a conjecture that we had hoped for, no doubt this construction and the ideas involved will have a major impact in discrete geometry.
One aspect of this proof should not be overlooked: while the original proof produced by AI was completely valid, it was significantly improved by the human researchers at OpenAI and the many other mathematicians involved in the present paper. The human still plays a vital role in discussing, digesting, and improving this proof, and exploring its consequences.
The frontiers of knowledge are very spiky, and no doubt the coming months and years will see similar successes in many other areas of mathematics, where long-standing open problems are resolved by an AI revealing unexpected connections and pushing the existing technical machinery to its limit. AI is helping us to more fully explore the cathedral of mathematics we have build over the centuries; what other unseen wonders are waiting in the wings?
One way of putting this is that the mathematicians are now acknowledging that the AI’s are “one of us”. Gooble Gobble! Read the AIs chain of thought to understand why. I asked Claude how many people the world could understand the proof:
A rough tiered estimate, treating “understand” as “could read the 42-page note and follow the argument without needing to learn new machinery from scratch”:
Tier 1 — could referee it cold (real working knowledge of class field towers + the Ellenberg–Venkatesh circle): roughly 150–400 people worldwide. This is essentially the active algebraic number theory community working near arithmetic statistics, plus a handful of arithmetic-geometry-adjacent combinatorialists. The author list itself is a decent proxy for the upper crust of this group.
Tier 2 — could understand it with a week or two of focused effort and some Wikipedia/textbook chasing (strong number theorists or combinatorialists outside the immediate subfield, plus sharp grad students past quals at top programs): roughly 2,000–5,000. Think most tenure-track number theorists, the top tier of extremal combinatorics, and arithmetic geometers generally.
Tier 3 — could grasp the structure of the argument from a Quanta-style exposition without verifying the steps: 50,000–200,000+, i.e., most working mathematicians and a chunk of physicists/CS theorists. This is not what you asked, but it’s where most of the public “understanding” will sit.
Repugnant Economics
I spoke on a panel at AEI with Nobelist Al Roth about his new book, Moral Economics, which covers “repugnant markets,” from prostitution to surrogacy to kidney exchange. A fun book!
My case study was acting. Acting was considered repugnant for over 2,000 years. In Rome, actors could not vote, hold office, or be trusted to give an oath in legal proceedings. So why don’t we find acting repugnant today?
One lesson: weighing costs and benefits is not enough. Roth discusses empirical research showing that legalizing prostitution cut STDs and sexual assaults—against prostitutes and others. But evidence alone won’t shift a repugnance norm. You also have to reframe the activity. Acting, for example was reframed from body rental to a skill requiring intelligence, training and ability. So I went out of my way to say that I am a fan of Aella—though not her only fan—and that I see no reason why escorting should not be considered a skill, requiring intelligence, training, and ability. I can think of few better ways of raising social welfare than making sex 10% better!
I also spoke on human challenge trials. Roth and I agree: challenge trials could have sped up COVID vaccines and saved tens of thousands of lives. We should be angry this didn’t happen. Why didn’t it? Even though most people think human challenge trials are a good idea, there was a repugnance bottleneck because the minority who did find human challenge trials repugnant were in charge. I discuss how to change this.
Al leads the discussion. My comments start at 25:15.
Dwarkesh in the Datacenter
Dwarkesh tours one of Jane Street’s datacenters. It’s extraordinary how much compute goes into finance. (I once predicted that the finance AIs would be the first to become conscious, since they have the most compute.) More generally, however, this is a peek inside the remarkable economics, technology and physics of a datacenter. Did you know the electrical signal in a copper wire can travel faster than light in fiber…and that matters! Amazing.
Hayek in Jacobin
Here’s something I never expected to write: Jacobin, the magazine of the DSA-aligned left, has a good article on central planning. In an interview, Vivek Chibber lays out essentially the Mises–Hayek–Kornai critique of central planning. Information problems, incentive problems and the consequent failures are laid bare. Moreover, Chibber refuses to lay the blame at the feet of Stalin, poverty, or the Russians. Nor does he wave hopefully at supercomputers and AI, as is fashionable today on the planning-curious left:
The dilemma is this. There is a problem of information. Supercomputers will in fact help process information better. But if the information coming in is junk, and if that junk is built into the system because of the incentives that operators have in workplaces to lie, you will not have a planning system that can be put on its feet through the advent of computers or artificial intelligence or anything like that. I don’t see any reason to think that that strategic misalignment of incentives is simply there because of Russian backwardness or poverty.
Even the pedestrian is shocking coming from Jacobin:
Normally in capitalism, what do managers do? They want to make profits. The way to make a profit is by trying to sell, at the lowest price possible, the best-quality good that you can.
A vivid conclusion:
Melissa Naschek: What do you think leftists should learn from the failure of fully planned economies?
Vivek Chibber: What they should learn is that the burden of proof is on us, on the Left, if we want to continue with this slogan of replacing the market with the plan. The burden of proof is on us to show that it can work. You might say that along with this ought to come a kind of humility about facts and about the world.…it would be criminally negligent to ignore the experience of decades upon decades of planning and say to yourself, “Well, that wasn’t what my vision of socialism is, so I’m going to ignore it.” Because if you do that, I can guarantee 100 percent you will end up repeating many of the mistakes and falling into the same dilemmas that the planners did.
I could offer critiques. Stalin was not an impediment to central planning but a consequence of it. And to warn that ignoring the experience of central planning risks repeating “the same dilemmas that the planners did” is a bloodless way to describe dictatorship, famine, and mass murder. But that would be churlish. Let me end instead by saying that I agree with this:
If we’re actually serious about changing the world, people on the Left … should be the most remorseless and the most merciless when it comes to facts.
Replace “people on the Left” with “we” and the line is exactly right.
Philosophical Ideas Behind Their Time
Justin Weinberg at Daily Nous riffs off my post, Ideas Behind Their Time, to ask for philosophical examples. He nominates Gettier problems–i.e. counterexamples to the idea that knowledge is simply “justified true belief” as a possibility. The classic Gettier paper is from 1963. Wikipedia notes that the Indian philosopher Dharmottara has some clear examples c770 AD but as an element within the Western tradition the idea does seem behind its time.
I would nominate the following as philosophical ideas behind their time:
- Hume’s is/ought distinction: the idea that you cannot derive a normative conclusion from factual premises.
- Hume’s problem of induction: past regularities do not rationally guarantee future regularities.
- Rawls’s Veil of Ignorance: the principles of justice should be derived without knowing one’s own particularities of class, race, gender and so forth. Seems obvious as an idea.
- The Trolley Problem: similar ideas can be found earlier but the clean distinction between killing and let die or more generally omission and commission could have come much earlier. One might also think of the Prisoner’s Dilemma in this category of ideas or constructs that cleanly isolate an otherwise present but opaque idea.
- The analytic/synthetic truths distinction: some things are true by definition, others are empirical. Obvious and it can be found before say Kant, yet a clear earlier statement would have resolved many issues and seems well within say Aristotle’s capability.
- Aumann’s Agreement Theorem, technically, this requires Bayesian machinery and is difficult to formulate with precision, so I would not say the actual theorem was behind its time. But the underlying idea—that disagreement itself, not merely the arguments offered, should cause one to question and refine one’s own beliefs—could have been developed in Athens.
- I’d also nominate a package of ideas like abolitionism, equal rights for women, and religious toleration–each of these is tendentious as examples yet the basic package seems fairly obvious as a category and yet late. (Perhaps if the veil of ignorance had been thought of earlier so would these ideas!) Note, that I am not arguing that abolitionism or equal rights for women could have happened much earlier only that these ideas were behind their time–the ideas were morally obvious even if not institutionally feasible.
Note also that I am not arguing that these ideas are all correct, only that they were philosophical ideas behind their time. More examples?
How Much Has Shale Gas Saved U.S. Consumers?
Every US president since Nixon has called for freeing the US from ‘dependence on foreign oil’ (within ten years!). Every president has failed. Fracking, however, has delivered the goods. Fracking has reduced the price of energy, reduced net emissions of greenhouse gases and turned the US into an energy exporter.
In How Much Has Shale Gas Saved U.S. Consumers? Lucas Davis compare LNG prices in the US ($5.3 Mcf), Europe ($14.4 Mcf) and Japan ($16.1 Mcf) to offer some plausible back of the envelope calculations:
Advances in hydraulic fracturing and horizontal drilling caused U.S. natural gas production to increase significantly, and the U.S. went from being a net importer of natural gas to being the world’s largest exporter. This paper calculates how much shale gas has saved U.S. natural gas consumers. Using price differences between the United States, Europe and Japan, we calculate that U.S. natural gas consumers have saved $4.5-$5.3 trillion between 2007 and 2025, equivalent to $237-$276 billion annually. Access to low-price U.S. natural gas has been particularly valuable during major supply shocks such as the war in Ukraine, and the benefits of shale gas have been experienced broadly across sectors and states.
Ideas Behind Their Time: Part Two
In 2010 I wrote about Ideas Behind Their Time:
We are all familiar with ideas said to be ahead of their time, Babbage’s analytical engine and da Vinci’s helicopter are classic examples. We are also familiar with ideas “of their time,” ideas that were “in the air” and thus were often simultaneously discovered such as the telephone, calculus, evolution, and color photography. What is less commented on is the third possibility, ideas that could have been discovered much earlier but which were not, ideas behind their time.
I gave experimental economics, random clinical trials and view morphing (“bullet time”) as examples. Jason Crawford has a list discussing the wheel, the steam engine and bicycles among other possibilities. In some cases, further exploration indicates that an idea required precursors and so was not as behind its times as first suspected, in rare cases, however, good ideas really could have been invented much earlier.
Using Claude, Brian Potter has significantly expanded the list by looking systematically across a wide range of inventions and asking could they have been invented earlier? Most could not. Put the other way, most useful technologies tend to be invented quite quickly once they are possible–this is reassuring. The airplane, for example, could not have been invented before a high power-to-weight engine, which happened circa 1880 making the late 1880s the earliest feasible date for powered flight. Thus, the Wright Brothers (1903) were only just behind the earliest feasible date–and that is true for many inventions.
The ideas very far behind their time include the stethoscope, general anesthesia and reinforced concrete and quite far behind are the Jacquard loom and canning. Is there a pattern here?
Addendum: Brian’s Github with the full prompt and output for each invention is here.
Public Choice Outreach!
Just a few spots left! Lots of great speakers including Tyler, myself, Bryan Caplan, Robin Hanson, Jon Klick, Shruti Rajagopalan and more.
Please apply and encourage your students to apply.
