The medical culture that is Britain
Universities have been told they must limit the number of medical school places this year or risk fines, a move attacked as “extraordinary” when the NHS is struggling with staff shortages.
Medical schools have been told to curtail offers to ensure that there is “no risk” of them accepting more would-be doctors than permitted by a government cap, with universities saying they are likely to offer fewer places than normal to sixth-formers this year.
Ministers have been criticised for holding firm to a 7,500 cap on new medical students in England while also acknowledging that a chronic shortage of doctors and nurses is contributing to long delays for NHS treatment.
Robert Halfon, the universities minister, wrote to vice-chancellors last week telling them to limit their offers to sixth-formers, causing frustration among universities, which face fines of £100,000 per student for persistent over-recruitment. Universities say that in the summer, they were forced to reject students for administrative reasons such as submitting vaccine certificates late to stay within permitted numbers.
Here is more from the Times of London (gated). Perhaps Tyrone approves!
Indoor Air Quality and Learning
More on the surprisingly large effects of air pollution on cognition from Palacios, Eichholtz, Kok and Duran:
Governments devote a large share of public budgets to construct, repair, and modernize school facilities. However, evidence on whether investments in the physical state of schools translate into better student outcomes is scant. In this study, we report the results of a large field study on the implications of poor air quality inside classrooms − a key performance measure of school mechanical ventilation systems. We continuously monitor the air quality (i.e., CO2), together with a rich set of indoor environmental parameters in 216 classrooms in the Netherlands. We link indoor air quality conditions to the outcomes on semi-annual nationally standardized tests of 5,500 children, during a period of five school terms (from 2018 to 2020). Using a fixed-effects strategy, relying on within-pupil changes in air quality conditions and test results, we document that exposure to poor indoor air quality during the school term preceding a test is associated with significantly lower test results: a one standard deviation increase in the school-term average daily peak of CO2 leads to a 0.11 standard deviation decrease in subsequent test scores. The estimates based on plausibly exogenous variation driven by mechanical ventilation system breakdown events confirm the robustness of the results. Our results add to the ongoing debate on the determinants of student human capital accumulation, highlighting the role of school infrastructure in shaping learning outcomes.
Note that the authors have data on the same students in high and low pollution episodes, allowing them to control for a wide variety of other factors.
Here are previous MR posts on air pollution including Why the New Pollution Literature is Credible and our MRU video on the Hidden Costs of Air Pollution. Note that you can take lower bounds of these effects and still think we are not paying enough attention to the costs of air pollution.
What should I ask Jess Wade?
I will be doing a Conversation with her, and here is her work:
Wade, a research fellow at Imperial College London, centers her work on Raman spectroscopy, a technique often employed in chemistry to identify molecules, among other uses. She has received several awards for her scientific contributions, and her Wikipedia page is robust with her many achievements.
Here is a good interview with her. And from The Washington Post:
Since 2017, Wade has written more than 1,750 Wikipedia pages for female and minority scientists and engineers whose accomplishments were not documented on the site.
Here is her own Wikipedia page. So what should I ask her?
Are macroeconomic models true only “locally”?
That is the theme of my latest Bloomberg column, here is one excerpt:
It is possible, contrary to the predictions of most economists, that the US will get through this disinflationary period and make the proverbial “soft landing.” This should prompt a more general reconsideration of macroeconomic forecasts.
The lesson is that they have a disturbing tendency to go wrong. It is striking that Larry Summers was right two years ago to warn about pending inflationary pressures in the US economy, when most of his colleagues were wrong. Yet Summers may yet prove to be wrong about his current warning about the looming threat of a recession. The point is that both his inflation and recession predictions stem from the same underlying aggregate demand model.
You will note that yesterday’s gdp report came in at 2.9%, hardly a poor performance. And more:
It is understandable when a model is wrong because of some big and unexpected shock, such as the war in Ukraine. But that is not the case here. The US might sidestep a recession for mysterious reasons specific to the aggregate demand model. The Federal Reserve’s monetary policy has indeed been tighter, and disinflations usually bring high economic costs.
It gets more curious yet. Maybe Summers will turn out to be right about a recession. When recessions arrive, it is often quite suddenly. Consulting every possible macroeconomic theory may be of no help.
Or consider the 1990s. President Bill Clinton believed that federal deficits were too high and were crowding out private investment. The Treasury Department worked with a Republican Congress on a package of fiscal consolidation. Real interest rates fell, and the economy boomed — but that is only the observed correlation. The true causal story remains murky.
Two of the economists behind the Clinton package, Summers and Bradford DeLong, later argued against fiscal consolidation, even during the years of full employment under President Donald Trump [and much higher national debt]. The new worry instead was secular stagnation based on insufficient demand, even though the latter years of the Trump presidency saw debt and deficits well beyond Clinton-era levels.
The point here is not to criticize Summers and DeLong as inconsistent. Rather, it is to note they might have been right both times.
And what about that idea of secular stagnation — the notion that the world is headed for a period of little to no economic growth? The theory was based in part on the premise that global savings were high relative to investment opportunities. Have all those savings gone away? In most places, measured savings rose during the pandemic. Yet the problem of insufficient demand has vanished, and so secular stagnation theories no longer seem to apply.
To be clear, the theory of secular stagnation might have been true pre-pandemic. And it may yet return as a valid concern if inflation and interest rates return to pre-pandemic levels. The simple answer is that no one knows.
Note that Olivier Blanchard just wrote a piece “Secular Stagnation is Not Over,” well-argued as usual. Summers, however, has opined: “we’ll not return to the era of secular stagnation.” I was not present, but I can assume this too was well-argued as usual!
Erika Fatland’s *High*
The subtitle is A Journey Across the Himalaya Through Pakistan, India, Bhutan, Nepal, and China. This is the first great book of 2023, at least that I have seen. Bravo! Travel books are hard to summarize, but I will note that most of them are bad or at best mediocre. They assume you care about the author’s adjectives, or that the interesting nature of experienced events will translate automatically to the page. This work, in contrast, is a wonderful blend of fact, history, political observation, and narrative. I read every page, and it would likely make my list of my favorite thirty travel books of all time. Here is the author’s home page, she is by background a Norwegian anthropologist who speaks eight languages.
Thursday assorted links
1. ChatGPT taking U. Minnesota law exams.
2. The story and background of Scholz.
3. What is going on with PredictIt, and the legal case against them.
4. Macca reviews from 45s from 1967. Lennon does the same from 1965. Both are a bit harsh, but right on, and ruthless with those who will not innovate and progress with their styles.
5. Why is East Asia less happy?
6. Construction Physics on Goolsbee and Syverson on construction productivity. And commentary on some recent gender gap results.
The Great (British) Stagnation
David Wallace-Wells in the NYTimes:
In December, as many as 500 patients per week were dying in Britain because of E.R. waits, according to the Royal College of Emergency Medicine, a figure rivaling (and perhaps surpassing) the death toll from Covid-19. On average, English ambulances were taking an hour and a half to respond to stroke and heart-attack calls, compared with a target time of 18 minutes; nationwide, 10 times as many patients spent more than four hours waiting in emergency rooms as did in 2011. The waiting list for scheduled treatments recently passed seven million — more than 10 percent of the country — prompting nurses to strike. The National Health Service has been in crisis for years, but over the holidays, as wait times spiked, the crisis moved to the very center of a narrative of national decline.
It’s not just the NHS
By the end of next year, the average British family will be less well off than the average Slovenian one, according to a recent analysis by John Burn-Murdoch at The Financial Times; by the end of this decade, the average British family will have a lower standard of living than the average Polish one.
Wallace-Wells puts the blame on “austerity”. I see austerity as more obviously a consequence than a cause of stagnation. Government spending in Poland and Slovenia is modestly less than in the UK and the central government in Poland and Slovenia spend far less than the UK does on health. The question is not why the UK spends less–it doesn’t–the question is why it spends so much and gets so little.
A Chatbot for my Talent book with Daniel Gross
You will find it here, by a GMU undergraduate Momin Kaleem, working out of an incubator. Ask the bot and receive some answers from the book or inspired by the book. How can he make this better for other authors, he wishes to know?
My excellent Conversation with Paul Salopek
Here is the transcript and audio, here is the summary:
Paul Salopek is a Pulitzer Prize-winning journalist and National Geographic fellow who, at the age of 50, set out on foot to retrace the steps of the first human migrations out of Africa. The project, dubbed the “Out of Eden Walk,” began in Ethiopia in 2012 and will eventually take him to Tierra Del Fuego, a distance of some 24,000 miles.
Calling in just as he was about to arrive in Xi’an, he and Tyler discussed his very localized supply chain, why women make for better walking partners, the key to crossing deserts, the most difficult terrain to traverse, what he does for exercise, his information prep for each new region, how he’s kept the project funded, why India is such a good for walkers, which cuisines he’s found most and least palatable, what he learned working the crime beat in Roswell, New Mexico, how this project challenges conventional journalism, his thoughts on the changing understanding of early human migration, and more.
Here is one excerpt:
COWEN: What’s true is true. How is it that you crossed the desert? You’ve been through some of the Gulf States, I think.
SALOPEK: Yes, I’ve been through several deserts. The first was the Afar Desert in north Ethiopia, one of the hottest deserts in the world, and then the Hejaz in western Saudi Arabia, and then some big deserts in Central Asia, the Kyzyl Kum in Uzbekistan.
You cross deserts with a great attentiveness. You seem to want to speed up to get through them as quickly as possible, but often, they require slowing down, and that seems counterintuitive. You have to walk when the temperatures are congenial to your survival. Sometimes that means walking at night as opposed to the day. It means maybe not covering the distances that you would in more moderate climates.
Deserts are like a prickly friend. You approach them with care, but if you invest the time, they’re pretty inspiring and remarkable. There are reasons why old hermits go out into the deserts to seek visions. I was born in a desert. I was born in the Mojave Desert of Southern California, so I’m partial to them, maybe even by birth.
COWEN: Do you find deserts to be the most difficult terrain to cross?
SALOPEK: No, I find alpine mountains to be far trickier. Deserts can be fickle. Deserts can kill you if you’re not careful. Of course, water is the most limiting factor for survival.
But alpine mountain weather is so unpredictable, and a very sunny afternoon can turn into a very stormy late afternoon in a very quick time period. Threats like rock falls, like avalanches, blizzards — those, for me, are far more difficult to navigate than deserts. Also, I guess having been born in the subtropics, I don’t weather the cold as well, so there’s that bias thrown in.
COWEN: What do you do for exercise?
Recommended, interesting throughout.
Wednesday assorted links
1. Is there objective evidence for democratic backsliding?
2. Bryan Caplan chats with Tucker Carlson.
3. The growing bureaucratiation of job interviews.
4. Fairfax elementary school temporarily bans sports.
6. Noah on changes in Japan (also a Substack you should subscribe to).
Alas Paul David has passed away, RIP
I report, with great sadness, the passing of Paul David. A fabulous scholar of economic history and the economics of technology, he lit up Stanford for six decades. https://t.co/90ylP7yvK0
— Tim Bresnahan (@timobres) January 25, 2023
National Average is Over
This paper considers the implications for developing countries of a new wave of technological change that substitutes pervasively for labor. It makes simple and plausible assumptions: the AI revolution can be modeled as an increase in productivity of a distinct type of capital that substitutes closely with labor; and the only fundamental difference between the advanced and developing country is the level of TFP. This set-up is minimalist, but the resulting conclusions are powerful: improvements in the productivity of “robots” drive divergence, as advanced countries differentially benefit from their initially higher robot intensity, driven by their endogenously higher wages and stock of complementary traditional capital. In addition, capital—if internationally mobile—is pulled “uphill”, resulting in a transitional GDP decline in the developing country. In an extended model where robots substitute only for unskilled labor, the terms of trade, and hence GDP, may decline permanently for the country relatively well-endowed in unskilled labor.
That is from a 2020 IMF working paper by Cristian Alonso, Andrew Berg, Siddharth Kothari, Chris Papageorgiou, and Sidra Rehman. Via Eric Yu.
Who is locally influential these days?
Um…um…uh-oh:
Who do people think are influential in their own community? This question is important for understanding topics such as social networks, political party networks, civic engagement, and local politics. At the same time as research on these topics has grown, measurement of public perceptions of local influence has dried up. Years ago, researchers took active interest in the question of community influence. They found that most ordinary Americans could identify a person who they thought had influence in their community. Respondents usually named business leaders. Where does the public stand today? In three different ways, we ask respondents who has local influence. The vast majority of respondents today cannot think of anyone. Those who do identify someone as influential rarely choose a businessperson. This article aims to reintroduce the public opinion of community influence and situate findings in related scholarship.
Here is the new article by Joshua Hochberg and Eitan Hersh. David Brooks, telephone! Don’t even ask how the “religious leaders” fare in the polling…
AI Is Improving Faster Than Most Humans Realize
That is the topic of my latest Bloomberg column, here is one excerpt:
I have a story for you, about chess and a neural net project called AlphaZero at DeepMind. AlphaZero was set up in late 2017. Almost immediately, it began training by playing hundreds of millions of games of chess against itself. After about four hours, it was the best chess-playing entity that ever had been created. The lesson of this story: Under the right conditions, AI can improve very, very quickly.
LLMs cannot match that pace, as they are dealing with more open and more complex systems, and they also require ongoing corporate investment. Still, the recent advances have been impressive.
I was not wowed by GPT-2, an LLM from 2019. I was intrigued by GPT-3 (2020) and am very impressed by ChatGPT, which is sometimes labeled GPT-3.5 and was released late last year. GPT-4 is on its way, possibly in the first half of this year. In only a few years, these models have gone from being curiosities to being integral to the work routines of many people I know. This semester I’ll be teaching my students how to write a paper using LLMs.
We are now at or close to the point where LLMs can read and accurately evaluate the work of…LLms. That will accelerate progress considerably.
And to close I wrote this:
I’ve started dividing the people I know into three camps: those who are not yet aware of LLMs; those who complain about their current LLMs; and those who have some inkling of the startling future before us. The intriguing thing about LLMs is that they do not follow smooth, continuous rules of development. Rather they are like a larva due to sprout into a butterfly.
It is only human, if I may use that word, to be anxious about this future. But we should also be ready for it.
Recommended. Remember my old Wilson Quarterly piece about “invisible competition”?
On censorship of LLM models, from the comments
IMO, censorship is a harder task than you think.
It’s quite hard to restrict the output of general purpose, generative, black box algorithms. With a search engine, the full output is known (the set of all pages that have been crawled), so it’s fairly easy to be confident that you have fully censored a topic.
LLMs have an effectively unbounded output space. They can produce output that is surprising even to their creators.
Censoring via limiting the training data is hard because algorithms could synthesize an “offensive” output by combining multiple outputs that are ok on their own.
Adding an extra filter layer to censor is hard as well Look at all the trouble chatGPT has had with this. Users have repeatedly found ways around the dumb limitations on certain topics.
Also, China censors in an agile fashion. A topic that was fine yesterday will suddenly disappear if there was a controversy about it. That’s going to be hard to achieve given the nature of these algorithms.
That is from dan1111. To the extent that is true, the West is sitting on a huge propaganda and communications victory over China. This is not being discussed enough.