1. Building a top math team at an ordinary Florida high school (WSJ), a strong and important piece. We should be doing 10x of this or more.
The process of competing for science funding is so onerous that much of the value is dissipated in seeking funding. Risk aversion by committee means that breakthrough science is often funded surreptiously, on the margin of funded science. These problems are serious and make alternative funding procedures worth thinking about even if radical.
To avoid rent dissipation and risk aversion, our state funding of science should be simplified and decentralized into Researcher Guided Funding. Researcher Guided Funding would take the ~$120 billion spent by the federal government on science each year and distribute it equally to the ~250,000 full-time research and teaching faculty in STEM fields at high research activity universities, who already get 90% of this money. This amounts to about $500,000 for each researcher every year. You could increase the amount allocated to some researchers while still avoiding dissipating resources on applications by allocating larger grants in a lottery that only some of them win each year. 60% of this money can be spent pursuing any project they want, with no requirements for peer consensus or approval. With no strings attached, Katalin Karikó and Charles Townes could use these funds to pursue their world-changing ideas despite doubt and disapproval from their colleagues. The other 40% would have to be spent funding projects of their peers. This allows important projects to gain a lot of extra funding if a group of researchers are excited about it. With over 5,000 authors on the paper chronicling the discovery of the Higgs Boson particle in the Hadron Supercollider, this group of physicists could muster $2.5 billion dollars a year in funding without consulting any outside sources. This system would avoid the negative effects of long and expensive review processes, because the state hands out the money with very few strings, and risk aversion among funders, because the researchers individually get to decide what to fund and pursue.
There are issues to be sure (see the paper) but experimentation in science funding is called for:
Government funding of science is a logical and well-intentioned attempt to increase the production of a positive externality. However, the institutional forms in which we have chosen to distribute these funds have created parasitic drag on the progress of science. There are many exciting proposals for new ways to fund science, but picking any one of these without rigorous experimentation would be foolish and ironic. The best proposal for science funding reform is to apply science to the problem. Rapid and large-scale experimentation is needed to continuously update and improve our science funding methods.
See also Tyler’s important post, Science as a source of social alpha.
According to the OECD, only 5 per cent of Colombians pay income tax. Revenue from personal income tax is worth just 1.2 per cent of gross domestic product compared to an OECD average of 8.1 per cent.
Taxes on businesses, on the other hand, are relatively high. The outgoing rightwing government of Iván Duque initially cut the corporate tax rate from 33 to 31 per cent. But when the coronavirus pandemic hit, followed by prolonged street protests against his rule, he was forced to raise it to 35 per cent, its highest level in 15 years and above the Latin American average.
Here is more from the FT. Note that the president-elect, Petro, actually is planning on cutting taxes on business, though he is proposing a wealth tax on individuals as well.
Although magical beliefs (such as belief in luck and precognition) are presumably universal, the extent to which such beliefs are embraced likely varies across cultures. We assessed the effect of culture on luck and precognition beliefs in two large-scale multinational studies (Study 1: k = 16, N = 17,664; Study 2: k = 25, N = 4,024). Over and above the effects of demographic factors, culture was a significant predictor of luck and precognition beliefs in both studies. Indeed, when culture was added to demographic models, the variance accounted for in luck and precognition beliefs approximately doubled. Belief in luck and precognition was highest in Latvia and Russia (Study 1) and South Asia (Study 2), and lowest in Protestant Europe (Studies 1 and 2). Thus, beyond the effects of age, gender, education, and religiosity, culture is a significant factor in explaining variance in people’s belief in luck and precognition. Follow-up analyses found a relatively consistent effect of socio-economic development, such that belief in luck and precognition were more prevalent in countries with lower scores on the Human Development Index. There was also some evidence that these beliefs were stronger in more collectivist cultures, but this effect was inconsistent. We discuss the possibility that there are culturally specific historical factors that contribute to relative openness to such beliefs in Russia, Latvia, and South Asia.
You can find that here.
At the same link are some fascinating sequels, such as “Counties with more goats than people.” And what is the most common type of livestock in each county? Recommended, via Mike Doherty.
How to improve society is one of the most commonly discussed questions, but it is not always approached with sufficient seriousness. We don’t think analytically enough about which variables can have maximum impact and also which are most feasible to steer.
For instance, the management of science is a radically underappreciated issue. How many managers of scientific labs receive any management training at all, even in the basics? On a scale of 1 to 10, how well are most labs or non-profit scientific ventures run? I’ve asked a number of people in the hard and biological sciences this question, and more often a laugh is the response, rather than a citation of a very specific number. I’ve never heard anyone say they are run just great.
On the other side of the market, the rest of us are failing too. In our social discourse, we have not elevated better scientific management as a social priority. This could be done in our universities, non-profits, research labs, government agencies, and of course in the private sector too. It’s not a sexy policy issue, but science is one of the most significant means for improving society. In the language of finance, you could say that science is a major source of social alpha.
Science offers the added benefit of being relatively easy to influence or control. Trying to improve the management and policy of U.S. science isn’t an easy task, but it is a relatively small part of our economy and the notion of science is relatively well-defined. Furthermore, our government has many direct policy levers such as the National Science Foundation, the National Institutes of Health, the Department of Energy, and the Department of Defense, not to mention numerous state universities. If we can’t improve the performance of our science, you have to wonder what can we do.
In contrast, some other sources of society-wide superior performance are broad and far-ranging in nature, but often too difficult to steer. I have in mind such variables as “trust,” or “having a cooperative culture.” Those are strong positives for societies, but also a little intimidating for a policy program and they can be very difficult to pin down.
Is science really a source of social alpha? Well, science gave the world mRNA vaccines, though not to all societies at the same time. The U.S. and UK cashed in early there, in large part due to their domestic scientific achievements. Science helps keep the U.S. defense establishment strong. Superior science also was essential to the building of the United States as a wealthy, developed nation. If you are hoping that we cure cancer, or limit the problems of climate change, those issues too rely on science. Most generally, science feeds into productivity growth which in turns boosts real wages and the general opportunities available in society.
Science policy could take up a much larger “mind space” in current policy debates. American science was mobilized in part due to the panic over the 1957 Sputnik scare, when the Soviet Union seemed to be ahead of the United States in a number of scientific dimensions. We don’t have a comparably focused scare today, but today’s problems are in fact no less urgent.
The institutions of the National Institutes of Health, the National Science Foundation, the Department of Defense and more all have very specific policies toward science. I do not have all or even most of the answers, but what are the chances that those institutions have the very best policies toward science? Pretty close to zero. And as time passes, those institutions seem to become more bureaucratic, more concerned with process, and less innovative, hardly a surprising evolution to anyone familiar with Washington, D.C.
I think we should experiment more with DARPA-like models, where rotating program managers, operating in relatively flat bureaucratic structures, have the autonomy to commit significant sums of money. I also think in pandemics our science institutions should have wartime-like powers to act more quickly and decisively. Whether or not those particular views are correct, a sustained national dialogue about science could yield substantial dividends.
On the research side, science policy and the study of science, should be far more prominent. In my own field, economics, economics of science is barely a subfield and it probably accounts for less than one percent of all research. In my ideal world, it would account for at least five percent of all of economic research.
Similarly, the history of science has produced thousands of wonderful books and articles, but it is hardly the highest-status or most popular subfield in history. We can appreciate what is there while wishing for much, much more, not just in terms of numerical outputs but also in terms of social status and reach to a broader set of readers.
Better science is one of the biggest “free lunches” standing before us.
Programmers in the US are well-paid and companies report difficulty hiring programmers. At the same time, while it’s less reported, there are a lot of people who are good at programming but can’t get programming jobs.There’s a simple explanation, and it’s one that I’ve validated in several ways since realizing it: companies only want to hire already-employed programmers. There’s little incentive to hire someone not already working as a programmer, because if you pay them less than the market rate, they’ll leave after a year, and it takes months to get net productivity from them. (This is great for that person, but the company doesn’t care.) There’s also a big difference between good and bad programmers that can be hard for non-technical managers to determine. There are some developer jobs specifically for new graduates, but fewer than there are computer science graduates alone, and only at certain companies. There’s also a limited window to get one after graduating. Some people can get jobs after a coding bootcamp, yes – but in general, only people in demand for DEI reasons can actually do that, and any technical college degree works about equally well. The higher developer salaries get, the more unqualified people apply, the higher search costs get, and the more companies are disinclined to hire people who aren’t already working as developers.
That is from bhauth.
5. How is Neom progressing? (Bloomberg)
6. Dracula, timekeeping, and Progress Studies. Dublin Mean Time! Recommended.
The best approach is to hunt down particular foodstuffs and consume them, rather than thinking in terms of restaurants. That includes fruits (start with blueberries and blackberries and then move on to the stuff you have never heard of), cheeses, arepas, empanadas, other baked goods, all forms of corn (“choclo”), and whatever they happen to throw your way.
“Have I had this yet?” should be your starting question, and try to get to a full slate of yes’s!
Don’t obsess over finding the best restaurants. You will end up with some excellent meals, but it is not the same as learning the local cuisine. That said, the dining scene is much improved since my last visit eight years ago. I didn’t have complete control over my time and meals, but can recommend Casa del Rey and Sauvage as both very good, without being convinced that they were the very best.
I gave a keynote address in Bogotá to the International Adam Smith Society, here is my talk. Why is Adam Smith still relevant to Colombia of all places? It’s not just the market economics, rather my remarks focused on Book V (the best and most interesting part of WoN!) and Smith’s take on standing armies and why they are conducive to liberty.
The latest report shows that the consumption of old music grew another 14% during the first half of 2022, while demand for new music declined an additional 1.4%. These old tunes now represent a staggering 72% of the market.
And it’s likely to get worse when the full year numbers are released—because we are still in the midst of the Kate Bush/Metallica phenomenon spurred by the showcasing of their old songs on Stranger Things.
If this were just a short term blip of nostalgia caused by a popular TV show, I might ignore these numbers. But every other bit of market research tells the same story.
A number of recent articles on Hollywood have announced that we are living in the Golden Age of the Aging Actor. Harrison Ford, who turns 80 this week, may be the most prominent example. As the public face of several major brand franchises, he is still in demand, and will soon show up in another Indiana Jones movie, a kind of Raiders of the Lost AARP Card affair. But this aging Ford assembly line is hardly an isolated example—the graying actor is everywhere. Top Gun is the biggest box office success of the year, and it features Tom Cruise reprising a role he last played in 1986. I fully expect to see a septuagenarian Superman or Batman in the future.
Here is more from Ted Gioia’s Substack, which I recommend more generally.
Your advice is much appreciated, thank you!
We analyze the statistical power of political science research by collating over 16,000 hypothesis tests from about 2,000 articles. Even with generous assumptions, the median analysis has about 10% power, and only about 1 in 10 tests have at least 80% power to detect the consensus effects reported in the literature. There is also substantial heterogeneity in tests across research areas, with some being characterized by high-power but most having very low power. To contextualize our findings, we survey political methodologists to assess their expectations about power levels. Most methodologists greatly overestimate the statistical power of political science research.