Wednesday assorted links
1. The cheese collateral in Italian banks is melting.
2. Denmark fact of the day: “…half of Denmark’s growth has been due to essentially one company”
4. Noah is right.
Fast Company covers New Aesthetics
A good piece, it is good to see the artists themselves front and center of the story.
What makes people happy?
Measuring politically sensitive attitudes in authoritarian settings is difficult because expressions of political support may reflect repression and social pressure as much as genuine belief. We propose a complementary approach: using responses to questions that are not themselves politically sensitive to provide indirect evidence about politically sensitive attitudes. Using subjective well-being (SWB), we ask whether within-person changes in life satisfaction, after accounting for changes in material and personal circumstances, can provide evidence of incorporation into a wartime national project. Drawing on more than a decade of panel data from the Russian Longitudinal Monitoring Survey (2013–2024), we find that life satisfaction rises significantly following Russia’s 2022 invasion of Ukraine. Gains are initially larger among ethnic Russians and smaller in Moscow and St. Petersburg, consistently larger in military-industrial regions, and sharply lower among those nearest the front. This theoretically patterned heterogeneity is difficult to reconcile with generalized social-desirability bias and is consistent with differentiated wartime incorporation. By 2024, rising non-response and attenuating subgroup differences suggest that the informational value of SWB may deteriorate over time.
That is from recent research by Sinikka Parviainen and William Pyle. Via Pippa Norris.
Immigrant Earnings Assimilation, 1981–2021
In this paper, we characterize trends in the earnings assimilation of immigrant workers from 1981 to 2021. We use administrative longitudinal data that contain the earnings of workers beginning in their first year of residence in the United States and in each year thereafter, allowing us to identify immigrants who eventually leave the United States (referred to here as return migrants). We use those data to produce the first examination of trends in earnings assimilation over a 41-year period and to estimate earnings assimilation separately for return migrants versus those who stay in the United States. We document several new facts about immigrants who arrived between 1981 to 2010. First, roughly one-fifth to one-third of immigrants return migrate from the United States within 10 years after arrival. Second, return migrants have entry earnings similar to those of permanent migrants but experience slower rates of earnings growth. Third, earnings assimilation occurs relatively quickly for cohorts arriving since the mid-1990s: The earnings of permanent immigrants converge, or come close to converging, with those of native-born people within 10 years after arrival. Migrants from earlier arrival cohorts experience significant earnings growth but generally do not converge to that of the native born. We discuss how changes in the labor market quality of immigrant cohorts (measured by their relative earnings upon entry) and selective return migration play an important role in determining whether repeated cross-sectional data over- or underestimate earnings assimilation.
That is from a new NBER working paper by
How much will cybersecurity costs rise?
Here is one (partial) estimate of the higher cyber costs. I am not sure how many incidents they have in mind, but this seems to me realistic as an estimate but also rather modest relative to the Angst I am seeing? Not quite “a Gilda Radner thing,” but if nothing else shows the usefulness of trying to be concrete. Here is an estimate of $100 billion extra for the world annually. That is estimated as a twenty percent increase in cyber costs. Brad Carson uses AI agents (hurrah!) to produce estimates. He thinks I disagree with him, but I do not. I just want to see exact numbers, and presented as a share of gdp, and then everybody can calm down just a wee bit. And here are some superforecaster estimates.
On this topic, I am seeing so many mistakes in financial economics across social media, but I will not go through them all. It suffices to note that very smart people have their minds turn to mush when this topic comes up, especially the “sell short” side of the matter.
There is also the “beware isolated demands for rigor” response. I should not have to demand anything. If you are pretty worried about AI, you should be dying (yes pun intended) to provide that rigor, whether or not it is provided elsewhere.
How about a new policy?: “All AI safety expressions of despair, grief, panic, anger, or insinuations thereof, with or without gifs, shall be accompanied by an estimated percentage of gdp footnote.” Would inject some real sanity into the discussion and drain out some of the all too rampant emotions.
Tuesday assorted links
Impedimenta Developerum!
NME: Harry Potter fans have succeeded in moving a construction project that would have originally gone through the “grave” of the character Dobby.
The 125-mile Greenlink power connector, which costs £430million, is intended to link power between the UK’s National Grid and Ireland, running between County Wexford and Freshwater West in Pembrokeshire.
However, the latter site is known to Potter fans as the site of house elf Dobby’s grave in the film Harry Potter And The Deathly Hallows, and contains a pile of stones with the words “here lies Dobby” that many flock to…
All I can say is that Voldemort would never have put up with this shit.
How well does AI peer review work?
Claude and I planted 100 known errors into 10 open-access psychology papers and then ran them through frontier models and two commercial AI review tools. In brief:
- The best single system caught 71 of 100 errors, while the worst caught 30.
- Pooling every system’s output caught 93 of 100. Models are only partly correlated in the errors they find, making ensembling a big lever for finding issues in papers. Check your papers against multiple models!
- Seven errors could not be caught by any system. All were omissions — information deleted from a paper rather than mistakes inserted into it.
- Refine.ink contributes more unique catches than any other single system, though it’s expensive.
- I didn’t measure false positives and I don’t know how this error distribution compares to the distribution of errors in real papers.
- I’ve made the papers, errors, model outputs, and the full experiment log public. I hope people can build on this work to create a comprehensive eval benchmark across disciplines.
That is from Paul Litvak, here is more. Note that is not even using the very latest generation of models.
Who enters Congress?
We trace the family origins of Members of Congress (MC) born between 1830 and 1950, linking each MC, their parents, and their brothers to the complete-count censuses. Future MCs have always been economic outliers. By 1940, close to 60 percent of MCs aged 18–40 held a college degree, against under 5 percent of comparable young men outside Congress. In the 19th-century censuses, MCs as adults held roughly three times the wealth of demographically matched controls. They also come from economically elite families: their fathers earn more, attain more education, and hold more wealth. The brothers of future MCs sit between the population and the MCs themselves. Across the socioeconomic measures we compare, we calculate that roughly half of an MC’s adult premium is shared with his brother and the other half is his own. Even as the American economy and political system have changed, the family background gap has widened over the last century and a half. Wealth, household servants, and four separate occupation-based scores all show the gap holding or growing across cohorts. We study four Progressive-Era reforms in a triple-difference design—women’s suffrage, the secret ballot, direct primaries, and the direct election of senators—and none produce detectable shifts in who reaches Congress. Our results document a Congress drawn from the most fortunate families in American society in every cohort we observe.
That is from a new NBER working paper by
Brian Chau at work
I am automating investigative journalism at Effort.News
We’re breaking five stories today based on verifiable financial data which everyone missed for years.
Here is the entire thread, which also describes the five stories.
Monday assorted links
1. Tax land more heavily than structures.
3. Francesco Cabella conversation with (an older) Victor Niederhoffer.
4. New homes in Tatu City, Kenya.
5. Bill Goichberg, RIP (NYT). America’s greatest chess organizer, he was always very nice and encouraging to me.
Do Faculty Affect Student Partisanship?
We study whether Democratic college professors make their students more liberal. We link voter data to salary records from 33 state flagships and show that faculty skew Democratic, especially in the humanities and social sciences. We then use student transcripts from one flagship to estimate causal effects. Students become more liberal during college regardless of major and sort toward ideologically similar instructors. Exploiting plausibly random variation in when instructors teach courses, we find no effect of faculty partisanship on student partisanship. Text analysis shows exposure to liberal topics reflects student demand rather than instructor supply, leaving little room for indoctrination.
That is from a new paper by Micah Baum, Joaquín Endara, and Annaliese Paulson. Via Jay Fan Bavel.
Act like it is science
I am seeing so many doom prognostications, or least severe worries, due to the OAI/HuggingFace incident and related stories. But virtually all of these I find underargued to say the least.
So I have a simple request. If you are worried, and wish to persuade the doubters, try the methods of science. I would like to see the following:
1. Your outline of how, say two or three years from now, we might estimate the additional cybersecurity costs from the AI break-ins. I am convinced that number is not zero, but give me your method please. If it helps, here is an estimate of past cybersecurity costs, done by top economists.
2. Your current numerical estimate of what those costs might end up being, of course to be tested against what actually happens over time. Obviously, you can do this for a few different regulatory/safety scenarios. This is one simple way to prove yourself largely correct, albeit with a lag. (NB: you do not have to take this as a substitute for your preferred safety measurres. But please try to be specific in your predictions.)
3. A list of what stocks or other assets you have shorted, now. Obviously if your answer to #2 is sufficiently low, you could answer here zero, as I would do. I expect costs, but not so high that we cannot muddle through and have expected positive stock returns.
This would all help to make the discourse more scientific and less like an extended exercise in personal anxiety management.
If it is all so important, surely this exercise is worth the effort?
Sunday assorted links
1. African use of Chinese AI (NYT). And what happened to Talenti gelato? (NYT) And the NIMBY brutalists who live in the Barbican (NYT).
2. Modernist Gothic in Copenhagen.
3. AI-generated Odyssey in Vietnam.
5. Time magazine has a special version that only the AIs can read.
6. Tymofiy Mylovanov has a good summary of my greatest fear.
JFK’s AI Voice
From Kennedy’s 1961 inaugural:
- “Ask not what your country can do for you—ask what you can do for your country.”
- “Let us never negotiate out of fear. But let us never fear to negotiate.”
- “United, there is little we cannot do… Divided, there is little we can do.”
- “If a free society cannot help the many who are poor, it cannot save the few who are rich.
and from the 1962 moon speech at Rice University;
- We choose to do these things not because they are easy but because they are hard.
and from Lyndon Johnson’s inaugural speech:
- John Kennedy’s death commands what his life conveyed—that America must move forward.
- On the 20th day of January, in 1961, John F. Kennedy told his countrymen that our national work would not be finished ‘in the first thousand days, nor in the life of this administration, nor even perhaps in our lifetime on this planet.’ But, he said, ‘let us begin.’ Today, in this moment of new resolve, I would say to all my fellow Americans, let us continue.
Sound familiar?
Were John F. Kennedy’s speeches written by an AI? Probably not. But were AI’s trained on the speech writer Ted Sorensen? It would appear so. Or to be more accurate, AIs have absorbed the Sorensen toolkit: symmetry, parallelism, antithesis, repetition, and the neatly turned reversal.
Sorensen is considered a great stylist, and these are iconic lines. The problem with AI voice is not the techniques themselves. It is the overuse of them—their deployment to describe a trip to 7-Eleven with the same rhetorical flourishes once used to send men to the moon.
I suspect that will be an easy problem to fix, so don’t expect AI voice to continue.
Hat tip: murgh