that their graduates earn more than the typical high school diploma holder…[and] graduate programs will be required to demonstrate that their graduates earn more than the typical bachelor’s degree holder. (emphasis added).

Think about how low that bar is. The comparison group for an undergraduate program is working adults aged 25-34 with nothing more than a high school diploma. A college program that can’t beat that has almost certainly made its students worse off. For graduate programs the bar is the lowest of several bachelor’s benchmarks, including bachelor’s holders in the same field. A master’s in social work need only beat people with a bachelor’s in social work. A program must also fail in two out of three years before it loses loan eligibility. The Department estimates that about 5% of programs will fail in the first year.

I mocked the term “predatory lending” when it first became common in the financial crisis but in this case predatory lending fits the bill because the real borrower isn’t the individual student. Under income-driven repayment, the taxpayer is a forced co-signer, and it’s the taxpayer who gets predated.

Most expansions of the student loan program have been motivated by the picture of an enterprising student who works hard and wants to major in mechanical engineering or nursing but because of their poor circumstances they can’t afford college. “Credit constraints, asymmetric information, you can’t collateralize human capital,” said the economists. Nice theory, what’s the practice?

The economists wanted loans for good investments and insurance against bad luck but the economists can’t swing the vote and once the government is lending, colleges want more tuition money and students want more forgiveness. The result is a subsidy for programs whose graduates are never likely to repay. As Looney and Yannelis document:

Starting in the late 1990s, policymakers weakened regulations that had constrained institutions from enrolling aid-dependent students. This led to rising enrollment of relatively disadvantaged students, but primarily at poor-performing, low-value institutions whose students systematically failed to complete a degree, struggled to repay their loans, defaulted at high rates, and foundered in the job market. As these new borrowers experienced similarly poor outcomes, their loans piled up, loan performance deteriorated, and with it the finances of the federal program.

Indeed, the program worked in reverse of what was promised. The biggest subsidies went to programs whose graduates were least able to repay, rather than programs with the strongest case for public support. As I wrote earlier:

Looney does a back of the envelope calculation and estimates that typical graduates in Mechanical Engineering will on average get a 0% subsidy but graduates in Music will get a 96% subsidy, in Drama a 99% subsidy and Masseuses a 100% subsidy on average. This of course is exactly the wrong approach. If we are going to subsidize, we should subsidize degrees with plausible positive spillovers not masseuses.

The courts later blocked Biden’s Save plan but the problem is built into income-driven repayment. If music, drama and masseuses are promised a 95%+ subsidy who is paying? The taxpayers. Moreover, it’s even worse than this because the very existence of these loans incentivizes the creation of expensive, useless programs. It’s not just the drama colleges, however. Not surprisingly, the law schools have proven adept at using Public Service Loan Forgiveness (PSLF) to rip off the taxpayer. The school raises tuition, then covers the student’s small income-driven payments for ten years, and the taxpayer forgives the rest. In short, protecting students from the cost of failure rewards colleges for producing it.

Fortunately, the same bill limiting loans ended Grad PLUS loans and capped graduate borrowing. You can see the logic: if taxpayers are going to insure the loans, they need some say over which programs qualify and how much is borrowed. I don’t like giving government that power, but this is the Mises–Higgs intervention ratchet in action: subsidize the loans, absorb the losses, then regulate the programs to limit the losses.

My ideal program would get the government out of the student loan business altogether but until then this is a good first step at limiting one of the most expensive and wasteful programs of the federal government.

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Shade is not part of L.A.’s modern identity.  In the 1930s, the city was rezoned to Federal Housing Administration design standards and banned high-density developments like row houses.  Although apartments were once common, city leaders bowed to a prevailing wisdom that L.A. should not resemble a dark and cramped East Coast city.  Freestanding single family-homes that were touched by sun on every side became mandatory.  In came the cars.  L.A.’s curbside trees were removed to accommodate shrinking sidewalks and expanding roads, and new rules that require parking minimums dealt another below to the urban forest.  Mediterranean-style courtyards became endangered species as the shaded commons were converted to outdoor car storage.  For decades, no building could be taller than the twenty-seven-story city hall…

Since the 1970s, an individual right to sunshine has been practically enshrined in state law.

The book also serves as an alternative history of Los Angeles (though it covers much more than that) through this alternative lens.

The post *Shade* appeared first on Marginal REVOLUTION.

       

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• In reply to Crikey. Perhaps so, but I wonder what the ... by Vivian Darkbloom
• In reply to patrick. “Deum verum de Deo vero, genitum, non ... by Cecil Rokestam-Bourke
• In reply to patrick. It's a miracle! by dearieme
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I do not think the skeptics would put it this way, but as I read Conti, I find he has a pretty bleak fundamental view of humanity. Are we all really just looking to veg out and abandon curiosity and inquiry, at least once the machines have taken care of both the basic functions of life and certain higher aims such as scientific research? I think some people are like that—indeed you might say many people—but it does not reflect what I take to be the general human condition.

If I look at most people who might fit into the “middle class” when it comes to intellectual pursuits or educational status, I observe they have a lot of strong interests. This might play with their pets, improve their performance at sports, or learn how to cook better. You do not have to identify those preferences with “the new Athens” or “the next Mozart” to think they are perfectly good and noble ways for people to spend their time.

Most of us want to do something interesting and stimulating with our leisure time, and if we do not, it is often because our jobs are so busy and stressful that we just wish to decompress. Of course, in this radical vision of our AI future, fewer jobs will be so all-consuming and so more of us will use vacations and leisure time to explore and learn rather than to just sit on the beach scrolling our phones. And to the extent some jobs do remain hectic, or become even more so (such as cybersecurity), they will continue to be challenging and intellectually stimulating.

A related worry is that humans may feel they simply cannot compete with the AIs, and thus they might turn away from creative pursuits. It is true that I, more than ever, have given up all hope of proving new theorems in mathematical economics. But many of my intellectual and creative pursuits do not involve competition at all. For instance, I use AI to understand classical music better, asking the models questions before I sit down to listen to a piece. (Such as “which are the best recordings?” and “what should I listen for in the second movement?”) As the models get better and smarter, I am not going to be discouraged in this endeavor, as I was not “competing” with the models to see which of us knew more. Rather, I will gratefully end up much better informed about classical music—my increasing knowledge has already induced me to see more live concerts.

Recommended, and AI saved me time on the proofreading and fact-checking (not the writing!), so I could return to reading China Mieville…

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• In reply to Grizzled Econ 101 Prof. The idea of 'cheating' is ... by Todd K
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We study the macroeconomic and trade-policy implications of disruptions to U.S.-bound shipping routes. Standard models treat them as iceberg-cost shocks, conflating the shock with the response to it. Using satellite vessel-tracking data, we construct route-level measures of potential and effective capacity for all U.S.-bound container ships from 2016 to 2025. Utilization losses in recent disruptions ran 20 to 40 percentage points, and began months before port congestion became visible. We embed these measures in a general equilibrium model in which firms reallocate a common fleet without internalizing the congestion they create and price above marginal cost, while importers’ sourcing responds to route profitability. The reallocation triggered by a disruption then has first-order welfare effects, and the route’s Domar weight is not a sufficient statistic for its welfare cost. The 2021 West Coast crisis and the 2023-2024 Red Sea attacks cost 0.69% and 0.35% of output. Naval protection of Red Sea shipping generated benefits of 0.04-0.08% of output at a fiscal cost of 0.02%. Tariffs decongest the routes they tax, offsetting or even reversing their conventional welfare cost.

That is from a new paper by Xiwen Bai, Jesús Fernández-Villaverde, Yiliang Li, Ricardo Marto & Francesco Zanetti.

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• In reply to Student. A lot of that is country scale ofc. In ... by M
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2. Claims about New Zealand.

3. Good Bryan Caplan post on Effective Altruism.

4. Reported female bisexuality is in retreat?

5. Sadly, now a fringey view, yes.  But not with me.

6. Fewer and fewer are defecting from North Korea.

The post Tuesday assorted links appeared first on Marginal REVOLUTION.

       

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• In reply to bazza. That wasn't Dad's experience. Rather the ... by dearieme
• “1. Comment section arbitrage.” Imagine if teenagers ... by mkt42
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Development accounting is the search for proximate sources of cross-country income differences. This article describes how knowledge in this field has evolved over the two decades since the influential work of Caselli (2005). There have been large advances in the measurement of production inputs (labor, physical capital, and human capital). These advances have raised the estimated contribution of inputs, mostly human capital, in development accounting. Our preferred estimate is that inputs account for 55–70 percent of gross domestic product (GDP) per worker differences, versus 30 percent using the classic specification. The literature has also made progress in moving away from Cobb-Douglas production functions and measuring factors such as management quality that were previously bundled into total factor productivity (TFP). Our review highlights the new implications of these advances, areas where future research would be beneficial, and the limitations of development accounting.

That is from a new NBER working paper by David Lagakos & Todd Schoellman.

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We measure how artificial intelligence (AI) affects the economy through its impact on software engineering productivity. We use information from financial markets to develop a forward-looking measure that is available in real time. We estimate the sensitivity of each firm’s stock return to an AI stock market index, and how this sensitivity depends on the share of firm payroll in software engineering. We use a model to map this cross-sectional relationship into software engineering productivity gains. From November 2022 to December 2025, AI increased the market’s expected present value of software engineering productivity by the equivalent of a permanent 32.6% productivity increase. The corresponding effect on the level of GDP is 3.6% in the baseline and 6.5% when higher software engineering productivity also raises R&D productivity. By mid-2026, amid rapid progress in coding agents, the effect of AI on productivity and GDP had more than doubled relative to the end of 2025.

That is a new NBER working paper by Alex Blumenfeld, Jonathon Hazell, Chen Lian & Andreas Schaab.  This is also a simple way of showing that markets do indeed price in the effects of AI.

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When it comes to bear encounters in or near the wild, dogs are the aggressors in 54 percent of incidents, according to a recent study conducted by bear experts at Brigham Young University and other institutions, and published in the Journal of Wildlife Management. In approximately 36 percent of the encounters, dogs did not come to their owners’ defense. And they only successfully alerted their owners to the presence of a bear in about 9 percent of cases.

Here is more from the NYT.

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2. The world’s oldest known peace treaty found.

3. In praise of Joyce, Whitman, and Crane.

4. Background explainer on the Chinese AI ecosystem.

5. YIMBY working in Portland (WSJ).

6. Profile of Helen DeWitt.

7. Human frailty.

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Here is the NYT obituary.

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By the 1980s, living standards had caught up to the United States, with the provision of public goods sometimes superior.  The country was fully democratic, pro-Western, and largely pro-American.

Their rail system and postal service were amongst the best ever created.

For thinkers of note there were Hans Blumenberg, Habermas, Peter Weiss, Gadamer, late Heidegger and late Carl Schmitt, Reinhart Koselleck, the underrated Klaus Theweleit, Niklas Luhmann, and perhaps you value some of the other members of the Frankfurt School.

The visual arts were very strong, with Richter, Polke, Baselitz, Beuys, Penck, Palermo, and much more.

Music produced Stockhausen, Henze, Lachenmann, Rihm, Zimmerman, Kraftwerk, Can, and all of Krautrock, later techno, though right at the time of unification rather than during the BRD per se.  The list of vocalists, instrumentalists, and conductors is strong.  Was there anywhere better for hearing opera?

Perhaps I prefer the fiction from Austria and Switzerland, but at the very least Germany provided a major market for those authors and it was an extraordinarily literate country with amazing bookstores.  For domestic authors there were Böll, Patrick Süskind, Siegfried Lenz, Wolfgang Koeppen, can I count Uwe Johnson?, and Arno Schmidt maybe?  I do not like Grass, but it seems wrong not to list him.

The food could be very good, especially in the southwest.  There were Michelin star restaurants all over the country (still are, to be clear on this point).  So many well-functioning cities, with many of the world’s best transit systems.  Lots of nuclear power and a strong industrial base.  West Berlin was an exciting city with an air of mystery.  Some might say no speed limit on the Autobahn, though I am less sure that was a virtue.  Plenty of beautiful women and reasonable attitudes toward sex.

If you had to choose, what was the worst thing about the country?  No shopping on Sundays?  Workplace and shopping hours discrimination against women?  Too much smoking?  Obsession with Waldsterben?

Are there features of post-unification Germany that can compare to this earlier era?  So much seems not to work well.  So many policy mistakes have been made.  So much leadership lost in the areas mentioned above.  So much pessimism, sadly a lot of it seems to be justified.

Where did all the good performance go?  And why did it leave?  Lack of a communist enemy?  Absorption of East Germany?  The simple accretion of distance from pre-WWII German creativity?

The wonder that was the Bundesrepublik Deutschland.  Johannes, we hardly knew ye.

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Scientific progress is a key driver of economic growth and prosperity. There is great excitement- but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have broad disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. Second, we document evidence that LLMs (proxied through Gemini usage) and specialized models act as complements—LLMs are used for general analysis, coding, and manuscript preparation, while specialized models provide domain-specific predictions, data generation and classification. Third, scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily re-invested in more research. Finally, we show that AI is already changing the scientific process. As some stages of scientific research become easier, bottlenecks shift downstream. Scientists report an increased backlog of untested hypotheses and substantial demand for output verification. Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.

That is from a new paper by Mihai Codreanu, et.al.

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2. Intelligence explosions are social.

3. The speech-processing skills of dogs.

4. Kalshi market in economics Nobel.

5. Why are Indian weddings with dancing gorillas going viral? Pakistan too.

6. Eminem, and some German guy.

7. On the UAP council.

8. On higher interest rates.

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The shortages have gone on for so long that they are aggressively driving down how much carbon is being released into the atmosphere, a Washington Post analysis of data from the International Energy Agency shows. People worldwide are using significantly less oil and gas, which means less climate pollution…

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