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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• In reply to Silvanus. Correct, although the article mentions ... by mkt42
• In reply to Pshrnk. So there's a way for a dog to be aware of ... by Rahul
• In reply to boomersooner. People get unnecessarily worked up ... by Biggles f'Tang
• In reply to Dispatching from the Global South. They kind of ... by Moo cow
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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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• In reply to Ted. No strings attached for DeWitt. Very cool. ... by Abraham
• In reply to anonprof. I have worked as an engineer with many ... by Tom Meadowcroft
• In reply to anonprof. I'll grant they aren't jobs for senior ... by Wayfaring Stranger
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Here is the NYT obituary.

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• During my first visit to St Thomas and St John (USVI) in 1974, ... by Nick

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• In reply to Wayfaring Stranger. Yep, and you can live in one ... by Mitchell
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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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• I live in Berlin and from lived experience and our recent state ... by Viktor
• With all due respect – the denazification efforts by the ... by Prof. Emeritus
• In reply to Jan. Still very lovely to visit Germany; still ... by Agorabum
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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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The US federal government owns and administers 472,892,659 acres or 21% of the land area of the lower 48 states, the country’s largest landowner. The resource is held and managed as a collective resource, the Federal Lands, through political and bureaucratic interpretation of the Multiple Use principle and generally, the biological aim of maximum sustained-yield. By contrast, access, exchange, and investment for most other US natural resources are through private property rights and markets. Despite the magnitude of the resource, economists have devoted relatively limited attention to the economic and welfare impact. The objective is to suggest economic implications and to encourage additional economic analyses. The discussion summarizes federal lands privatization through 1891, when withholding of federal lands began. The literature reveals no demonstratable market failure or increased resource scarcity from private exploitation between 1870 and 1957 when most lands were withheld. Because land was nonmobile and observable private property rights could have been assigned and any externalities addressed via Pigouvian restrictions or Coasean exchange. Federal ownership was not obviously required. Progressive Era reformers, driven by concerns of impending resource depletion, called for scientific, sustained-yield management by government officials. The institutional change is economically important. As outlined by Dixit and others, private rights holders have high powered incentives for efficient resource use that are lacking in decision making by agency officials who do not hold exchangeable property rights and do not directly bear the economic costs and benefits of their actions. Consequential public goods delivery could be an offset, but these are not measured for tradeoff calculations. Following Krueger, a rent-seeking framework is presented for comparing outcomes with economic property rights and political management. The analysis suggests that a.) federal lands will have lower production value than comparable private, all else equal; (b). federal lands management will be less responsive to shifts in economic costs and benefits. Public goods may be provided for high amenity, recreation, and ecological areas, but the dominant Multiple Use management principle provides no objective criteria for allocation or for periodic outcome assessment and adjustment. A literature review and data for contemporary federal forests, range, and oil and gas lands are provided.

That is from a new paper by Gary D. Libecap.

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2. Roon as cultural critic.

3. The Greenland deal sounds pretty good for America?

4. The cultures that are New England?

5. Yet newer results on AI-driven labor demand.

6. Ayn Rand as movie extra.

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Here, in five waves of panel data (N = 1,966 US adults), we examined associations between life satisfaction and self-reported use of ten common social technologies measured every 3 months on a six-point frequency scale from ‘I did not use’ to ‘multiple times daily’. At this measurement level and timescale, Bayesian and frequentist random-intercept cross-lagged panel models showed little credible evidence that any social technology use predicts subsequent life satisfaction. In the reverse direction, increases in life satisfaction predicted only modest increases in (video) calling in select demographic groups. In analyses comparing different people, frequency of texting was associated with higher life satisfaction, whereas frequency of YouTube and TikTok use was associated with lower life satisfaction. Despite limited ability to detect within-person change due to temporal stability in responses, the absence of cross-lagged effects is informative: there is scant evidence of a meaningful relationship between social technology use and subsequent life satisfaction.

That is from a new Nature article by Kostadin Kushlev, Kibum Moon, Matt Motyl, Nathanael J. Fast & Juliana Schroeder. Via the excellent Kevin Lewis.

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Sick and elderly Americans will go to Chinese companies for their AI-invented and AI-tested medical devices and drugs. America still will be a wealthy country, so China will charge the highest prices possible, yet prioritize Chinese citizens for treatment. Large numbers of Americans will die prematurely, at least compared to a world in which many of those innovations came from the U.S. My colleague Alex Tabarrok has coined the phrase invisible graveyard to refer to these lost lives, invisible because we do not observe the state of the world where they get treatment readily and cheaply. Over time, this invisible graveyard will swell into the many millions.

Finally, we will ask what went wrong.

The postmortem will be this. Many people panicked about the possibility of strong AI models killing us all. That fear was not based on peer-reviewed scientific research showing a high chance of doom, nor was doom indicated in any market prices of the time, including measures of risk. It was a story, just like this is a story, and it was spread on social media. The key point of the doom story was that, if America keeps the No. 1 spot in AI models, the models will be so strong they will do us all in, or lead to unimaginable catastrophes. We were too afraid to have America keep the lead, forgetting that if truly destructive AI is our fate, the doomer scenario can come from Chinese AI as well.

Today, we cannot say for sure that the AI doom scenario is false. But is it a story we wish to live by? Is belief in it a good way to protect and extend life, liberty, and the pursuit of happiness? Will it help us much, or for long, if it is Chinese AI that turns on us and does us in?

In my view, successful societies accept challenges and meet them. Solving problems, bit by bit, is the best way to ensure that we have the capabilities to meet big and truly existential risks, should those risks come along. Debating the chances of our doom, ex ante, on a highly speculative basis, is unlikely to provide the same kind of expertise and talent cultivation. It is instead more likely to demoralize and immobilize us.

So which America are we going to choose?

Recommended, do read the whole thing.

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