Category: Economics

Hayekian Literary Criticism

In economics, Marx is relegated to the history of thought as his ideas were an economic dead end and a political disaster. Yet Marx-influenced literary criticism is a dominant mode of analysis in nearly every English department in the country. It’s not that the English professors are all Marxists, it’s that even the non-Marxists reach for Marxian concepts–class, ideology, alienation, material conditions, commodification–when analyzing texts. These concepts may be useful for analyzing a Victorian novel of the landed classes but they have become a default economics for all of literature. That default is odd. Class analysis predates Marx and society can be divided into more than one set of classes; material conditions do not supersede all artistic agency; and capitalism contains figures—entrepreneurs, speculators, intermediaries, innovators, discoverers—who are great subjects for art yet fit poorly into the Marxist moral geometry. Not surprisingly, Marxism handles capitalism’s protagonists badly.

Is Marxian economics the only economic lens one can apply to literature? What would a Hayekian literary criticism look like? The place to start is the great Paul Cantor’s pioneering essay on Thomas Mann’s “Disorder and Early Sorrow,” a slight-seeming story set in Weimar Germany during the hyperinflation. Cantor shows that when one reads the novella through Hayek and Mises rather than Marx, the story opens up.

Start with inflationary psychology and its ramifications. Inflation shortens time horizons. When money loses value by the hour, saving is foolish and the rational move is to spend as fast as you earn—Mises’s “flight into real goods.” Prudence, discipline, and respect for the past become maladaptive. Speed, improvisation, risk-taking, and a certain youthful irresponsibility become survival traits.

Thus, Cantor/Mann tell us that inflation changes psychology and inverts the authority of age over youth. The old are set in their ways and often living on fixed incomes that inflation has wiped out; they cannot adapt. The young have known nothing but instability and go with the inflationary flow effortlessly. So the conservative virtues that once commanded respect are in decline while youthful recklessness starts to look like competence. Thus, Mann’s world has “gone mad in the worship of youth”: the children call their father by his first name, the teenagers are “the big folk,” and Professor Cornelius literally crouches down to his children’s height as the hierarchy collapses around him.

Money is a society’s primary measure of value, so Cantor/Mann argue that when you shake a people’s faith in their money, you shake their other faiths. Thus Cantor ties the conviction-less skepticism of Cornelius—and the broader Weimar nihilism and disequilibrium that helped feed the rise of Nazism—to monetary disequilibrium.

In short, inflation converts economic disorder into moral, social, psychological, and finally ontological disorder. Prices become unstable, then values, then identities, then reality. The modern feeling of absurdity and inauthenticity that critics reflexively pin on capitalism, Cantor/Mann argue is due to government-created inflation and paper money.

A Marxist could read the same story and find the inevitable contradictions of capitalism. Cantor reads it and finds the consequences of the state debasing the currency. Both are economic readings of literature. Only one of them has the economics correct.

Cantor is the place to begin but a Hayekian literary criticism could go much further. Atavism, the impossibility of social justice, products of human action but not of human design, spontaneous order, the fatal conceit, subjectivism, the sensory order–there is a lot of Hayekian ideas that literary interpretation could draw upon.

A Hayekian criticism would ask questions like how do characters acquire and process knowledge? Which institutions transmit information successfully, and which corrupt it? How do money, law, language, and custom function as social coordination mechanisms? Why do some attempts at rational redesign end in disaster? Read War and Peace as a critique of the great-man theory of history, Brazil and The Lives of Others as the fatal conceit degenerating into ignorance, fear, and absurdity. The Wire as a Hayekian epic of spontaneous order that demonstrates the illusion of social justice. Cantor’s essay on Mann shows the method, the broader project remains underdeveloped.

Hat tip: Hollis Robbins for discussion.

Addendum: Don’t forget my earlier WSJ piece, Capitalism: Hollywood’s Miscast Villain which gives an economic, one might even say Marxist, explanation for why film directors in particular disdain capitalists.

Might AI hurt corporate profits? (from my email)

From Clifford Sosin:

I loved your talk about AI and wanted to bounce an idea off you.

I think AI may be bad for corporate profit margins.

A lot of companies make money because their customers can’t be bothered to monitor them more closely, or to insource something. Customers let the company make some money in exchange for doing a decent-enough job and making the problem go away.

Bank of America has $2 trillion of deposits, not a penny of which is optimized. Most enterprise software vendors could be switched out far more often, or displaced by home-built software, but it’s too much of a pain. I could run a 12-party RFP for an Uber ride or a pair of socks, but I don’t.

In a sense, many professionals are an extension of the same idea. I could research my own real estate law, or my own insurance, whether business or personal, but I don’t because it would be too hard.

Google Search might be the biggest example. It makes money because advertisers know they need to be at the top of the results to be found. But my agent will happily search all the results across multiple search engines.

AI agents should change all this. By acting as incredibly rational and vigilant sourcing agents, CFOs, and experts for their users, they will take rents previously collected by these toll-takers and redistribute them to consumers.

And I don’t think the AI stack itself necessarily makes much profit. Commodity and open-weight models are hot on the heels of the major model companies, and competition in GPUs should intensify. Indeed, making a GPU is in some ways similar to making software, so perhaps it can commoditize substantially. Chip manufacturing may remain high-margin, but there are now plenty of entrants drawn in by the shortage who could make TSMC’s market more competitive over time.

Some companies will win. Low-cost providers may gain share as customers switch more often. Richer consumers may consume more high-end goods. Companies with genuinely advantaged business models and limited competition will be able to become more efficient. But my overriding sense is that the equilibrium outcome is lower margins for companies.

Of course, people will build new businesses, and maybe they will use AI to generate very high margins in ways I haven’t considered. That would prove me wrong.

But if this lower-margin hypothesis is true, the knock-on effects are probably positive for AI adoption, since it will make the models more popular with consumers.

And if your view is that AI drives GDP growth to be only 5–10% higher over the next decade, it’s possible that a 100–200 bp decline in corporate margins from roughly 12% would mean companies in aggregate don’t see much benefit — or in fact lose — even as consumers are better off.

How High-Skill Immigration Restrictions Eroded Regional Productivity: Evidence from the 2017 BAHA Executive Order

This paper estimates the regional economic impact of high-skill immigration restrictions by analyzing the 2017 “Buy American, Hire American” (BAHA) policy as a quasi-experimental policy shock. By significantly tightening H-1B visa adjudication, BAHA caused new employment petition denial rates to double from 7% to 17%, while STEM-specific rejections tripled to 31%. Using a difference-indifferences framework, this study finds that states highly dependent on H-1B talent experienced a statistically significant 2.8% relative decline in value-added output. This implied a productivity loss totaling roughly $218 billion across the most affected regions. While concurrent tax cuts and deregulation likely offset the impact on employment and wages, the loss of specialized STEM expertise adversely impacted total factor productivity. These findings suggest that policies based on conventional employment metrics may overlook the “hidden damage” to productivity and innovation that drives the broader economy, thereby underestimating the true economic cost of immigration restrictions.

That is by Caroline Y. Su of McLean High School.  Via the excellent Kevin Lewis.

Let Me Disinherit My Children, S’il vous plaît

Following John Arnold, I posted earlier about how European laws often require wealthy people to give most of their wealth to their children. Here is an example:

Pierre-Edouard Sterin, founder of Smartbox and worth about €1.4 billion, told French senators he wants to disinherit his five children and donate everything to charity. French law, under the Napoleonic Code, mandates that with five children, three-quarters of his estate must go to them, leaving only one quarter freely disposable. Sterin argued for complete freedom to decide the fate of one’s assets, saying it is ‘a real freedom to start with nothing in life’.

Law professors prefer AI over peer answers

Large language models (LLMs) are increasingly promoted as educational tutors, yet most evaluations focus on domains with a single ground truth. Many disciplines, however, hinge on judgment: reasoning, weighing ambiguity, and reaching defensible conclusions. Law provides a sharp test. We conducted a blinded evaluation of short-answer tutoring in contracts courses with sixteen U.S. law professors. Participants created 40 representative questions, wrote answers, and judged 2,918 anonymized comparisons between human and LLM responses. Professors rated LLMs far higher than their peers (average win rate = 75.33%), with models performing similarly to the best instructor. LLM responses were also rarely flagged as harmful (3.53%, vs 12.06% for professors). Preferences for LLM answers were consistent across evaluators and reflected shared professional standards. Our evaluation can be reliably extended to additional models by employing a separate LLM as a judge, rendering expert agreements an effective, scalable method to evaluate AI tutors in judgment-rich domains.

“far”.  That is from a new paper by Alejandro Salinas, et.al.  Via Andrew Curran.  And via John Chamberlain:

Artificial intelligence (AI) and large language models (LLMs) tools are capable of mass-producing academic finance papers that are nearly indistinguishable from human-authored research, according to a new study published in the Journal of Economic Literature.

C’mon people, get ready.  I know it is difficult to admit when your human capital has been devalued, but that time is upon us.  In particular, being prolific is no longer such a comparative advantage in academia.  You might run to the “but I know what questions to ask” cope, but I implore you to solve for the equilibrium.  What is the equilibrium wage for merely asking questions?

Of course academic life and projects will continue, but the real rewards will go to people doing new, innovative, and hitherto impossible projects with AI.

Big if true

Several important questions — such as the possibility of debt-rollover without primary surpluses — turn on whether the present value of the aggregate endowment is finite, i.e., whether the economic growth rate under the “risk-neutral” measure, lies below the risk-free rate. It is tempting to argue that the endowment must be finitely valued, since there exist finitely-valued, non-depreciating assets whose cash flows are cointegrated with aggregate output. This paper shows why this argument is incorrect. A remarkable historical episode in which French government bonds were indexed to aggregate growth allows direct measurement of the risk-adjusted growth rate, which is found to exceed the risk-free rate.

That is from a new NBER working paper by Stavros Panageas.

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.

The chimera of universal coverage in a large, diverse country

Our findings suggest that policies intended to subsidize health insurance of higher income groups, for example, the enhanced premium subsidies, are far less efficient than policies intended to further expand public insurance to low-income groups, for example, in non-expansion states.

That is from a new NBER working paper by Anuj Gangopadhyaya & Robert Kaestner.

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?

A new American exceptionalism?

From Paul Krugman:

Let me be clear: I am not arguing that European productivity is mismeasured, and never said that. I am, instead, arguing that standard measures of productivity do not have the implications for cross-country comparisons of living standards and economic welfare that many people – including many economists – think they have. To put it a slightly different way: people are using data that is unsuited for the kinds of comparisons that they are trying to make. Thus, the conclusions that they are drawing from the data are misguided. But this is not to say that the data are wrong.

The apparent misunderstanding by Aghion et al of what I am trying to say is also reflected in their discussion. Their presentation mostly centers on arguing that European productivity growth is in fact lower than US productivity growth. This is puzzling, because I am not arguing that European productivity growth matches or exceeds US productivity growth. Like Aghion et al, I am fully aware that European productivity growth is lower than in the U.S. But this is not the actual issue that I am trying to address. My question is whether the standard comparison of European and US productivity growth rates is a good indicator of what is actually going on in the two economies over time.

OK, but if U.S. innovation drives global living standards, is that not a very strong argument for modest capital taxes in the United States, weak labor union privileges, high U.S. pharma prices, and so on?  Imagine a mix of the libertarian and corporatist agendas, rather than the social democratic policies Krugman typically has argued for.  I doubt however if Krugman sees it that way, but I am no longer sure why not.

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.

Supply is elastic, installment #1637

With deadly precision, the Trump administration has launched dozens of attacks on small boats in the waters off South America, killing nearly 200 people in a campaign U.S. officials say is meant to curb the flow of illicit drugs to the United States.

But almost nine months into the operation, epidemiologists, addiction scientists and public health experts say cocaine, by far the top drug smuggled out of South America, is as easy to get in much of the United States as it was before the strikes began.

The findings — based on evaluations of street prices, lethal overdoses, purity of samples and drug seizures at U.S. borders — raise questions about the effectiveness of the largest U.S. military deployment in Latin America in decades.

Here is more from the NYT.  And here is another report on supply elasticity, note that European airlines are still flying.

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.

Are we undermeasuring inflation for lower earners?

We document a new source of fluctuations in inflation inequality. When the cost of upstream inputs rises, varieties within a product category tend to have similar absolute price increases. However, the same absolute price increase constitutes a larger percentage change for low-price products, resulting in excess inflation at the low end (“cheapflation”). Since low-income households tend to buy lower-priced varieties, the inflation rates they face are disproportionately sensitive to upstream costs. Using data on food-at-home purchases, we show that this mechanism generates cycles in inflation inequality and excessive volatility in inflation for low-income households relative to high-income households. This channel parsimoniously accounts for observed fluctuations in inflation inequality over time, including surges in cheapflation and inflation inequality during both the Great Recession and the 2021–2023 post-pandemic inflation. Official statistics mask these within-category differences in inflation and thus understate the differences in inflation experienced by low- and high-income households by 70–90 percent. We provide evidence that this mechanism applies to a range of consumption categories beyond food at home. The same mechanism also leads to systematic differences in inflation across cities and import price inflation across countries in response to nationwide and global cost shocks.

That is from a new NBER working paper by Kunal Sangani.