Amid the broader fertility crisis, a new study by four economists, from Brazil, Singapore, the UK, and the US, is worth a closer look. The researchers use a natural experiment in Brazil, where housing credit is allocated through a lottery run by "consórcios" (group lending pools). A regular mortgage requires banks to screen applicants carefully, demand a large down payment, and take collateral, so many young families never qualify. A consórcio is open to almost anyone: members just make fixed monthly payments. The lottery replaces credit scoring, and defaults are absorbed by a shared guarantee fund.

For people aged 20 to 25, winning access to housing raises the probability of having a child by 32% and the average number of children by 33%. For people over 40, there is no such effect, which the authors attribute to the biological limits of fertility at that age.

The timing of the housing win turns out to matter as much as the win itself. Someone who gets housing at 20-24 ends up with roughly twice as many children over their lifetime as someone who gets the same housing ten years later, at 30-34. Every year of waiting produces a lasting drop in potential fertility.

The effect is largest for the poorest families and for those who had previously lived in the worst housing conditions.

The study also finds a gender pattern: in households where women earn a high income, housing access has less influence on the decision to have children, which the authors link to the higher career opportunity cost women face.

In this powerful visualisation, first broadcast on BBC Four in 2010, the late Swedish physician and public speaker Hans Rosling tracks 200 countries across two centuries, plotting life expectancy against income.

What emerges is a world that has changed far more dramatically—and unevenly—than labels like "developed" and "developing" suggest.

These two measures obviously aren't the whole story. But as a four-minute snapshot of how human life has changed, it’s incredibly effective.

Source: The Joy of Stats (BBC, 2010), presented by Hans Rosling. Data and visualisation: Gapminder.

According to McKinsey, by 2040 eighteen high-potential industries could generate up to $48 trillion annually, raising their share of global GDP from 4% to 16%. It's a signal of a global restructuring of the economic system under the influence of technology, demographics and climate. The main driver is AI, an industry that could grow more than 50-fold, from $85 billion to $4.6 trillion, thanks to the rapid development of generative AI and multibillion-dollar investment. Next come e-commerce (a fivefold rise to $20 trillion), cloud technology, electric vehicles, digital advertising and semiconductors. New niches are booming too: modular construction makes building faster and more automated, obesity drugs are becoming the answer to a global epidemic of excess weight, and robotics and autonomous cars are the key to reducing dependence on human labour.

Cybersecurity and biotech are also posting steady double-digit growth, while future industries such as air mobility, nuclear fission power and commercial space are rapidly attracting investment and interest. By Morgan Stanley's estimate, the obesity drug market alone will reach $77 billion as early as 2030. And in the US, more than $500 billion has been invested in AI since 2013, with almost 7,000 new companies created. The world stands on the verge of a new industrial revolution. These industries already demand attention, investment and workforce training - the structure of the economy for decades ahead depends on them.

An analysis of 7,000 billionaires over the past 25 years, based on data from Forbes, Hurun and Gapminder, shows a serious shift in the structure of global wealth: for the first time in a quarter-century of records, half of the combined fortune of the world's billionaires belongs to honest self-made entrepreneurs from open, competitive fields - tech founders, retailers, financiers and builders of consumer brands. The share of so-called "uncompetitive" and murky capital (commodity oligarchs, construction developers, the gambling business, defence contractors and other enthusiasts of state budgets and political connections), together with plain inheritors, has fallen to an all-time low.

The turn didn't happen at once: from 2001 to 2014 the share of old money and inheritance was actually creeping up, and only over the past decade did the trend reverse. The fortunes of classic commodity-type oligarchs have been shrinking steadily since 2021. Three drivers helped: the wild stock-market bull run of recent years, the expansion of China's middle class, and the explosion of mobile internet, which made it possible to scale any service to billions of users overnight.

The second thing worth noting is that, even as capital has been getting cleaner, political attacks on the rich have peaked (the word "billionaire" was used more than 900 times in a single session of the US Congress), while the economic case for confiscating honestly earned fortunes has objectively weakened.

Doctors per capita is an underrated indicator of a country's well-being. It shows who has managed to build up human capital and who lives in a constant shortage of qualified staff. According to Our World in Data, rich countries have about ten times as many doctors per person as poor ones. The global average is around 1.9 doctors per thousand people, while in low-income countries it's just a fifth of that.

A doctor is one of the most expensive specialists an economy can produce. Their training takes decades of study, a modern university base, clinical infrastructure and a state able to fund all of it for years. So the number of doctors in a country is essentially the accumulated result of decades of economic development, not something that can be scaled up quickly.

What follows is no longer OWID's statistic but the mechanism behind it. Rich countries have long imported not just raw materials and technology but people. Doctors trained in India, Nigeria, Egypt, Pakistan or the Philippines end up working in the US, Britain, Germany and the Gulf states. The result is a kind of tax on poverty: less developed states pay to train a specialist, and a richer economy reaps the fruits of their knowledge. The outflow is stark enough that the WHO keeps a separate list of countries where the shortage of medics is critical and from which they should not be actively recruited.

That's why the map of doctor coverage reads like a map of inequality. It shows not only the difference in the quality of medicine but a country's ability to hold on to its own qualified people. In the 21st century that's one of the main marks of a country that has made it.

https://www.youtube.com/watch?v=THodtjsCTSI

This video SUBSTANTIALLY misses the mark.

First, the video identifies a real short-run problem, but it frames that problem too much as “America is losing its geniuses” and not enough as “America has built a system that depends on importing already-trained, relatively inexpensive human capital instead of producing enough of it domestically.”

And amusingly, the video nearly discovers my argument by itself. It explicitly points out that a PhD student may be working on roughly a $30,000 stipend and that ordinary research jobs aren't especially well compensated. That's not merely a story about smart people. It's a story about the price and supply of scientific labor.

I would retitle the video “American is chasing away smart AND cheap minds”

Suppose America imports an extremely capable 24-year-old who has already received most of his K–12 education abroad, brings him into a PhD program, pays him $35,000–$45,000 to staff a laboratory, and then perhaps moves him into an American company.

 From the standpoint of the American research institution, that's an amazing deal. Someone else bore a substantial portion of the cost of producing human capital.

 The alternative is much harder: improve American elementary mathematics, improve middle-school algebra, get more teenagers through calculus and physics, improve teacher quality, identify talented kids who aren't already headed toward elite colleges, make engineering and science careers attractive, and then wait 15–20 years for the investment to mature.

Individual universities and corporations have weak incentives to solve that gigantic pipeline problem themselves. They can simply hire from the world market.

 So there's a political-economy story here that I think the video basically misses:

Immigration can be enormously beneficial while simultaneously allowing American institutions to postpone fixing domestic human-capital formation.

 Those propositions aren't contradictory.

And the domestic pipeline is not some imaginary concern. The latest NAEP results show eighth-grade math remained 8 points below 2019 in 2024, while twelfth-grade math was 3 points below 2019; at grade 12, declines occurred across the measured distribution except at the 90th percentile. That doesn't prove politicians have literally “given up” on American education, but it certainly makes “why aren't we producing more scientists ourselves?” a much bigger question than the video allows.

The creator finally gets there at 20:36 of a 21-minute video:

“we should probably double down on K through 12 education for the domestic talent pipeline”

…and then immediately says that's “a whole different video.”

😂 BRO THAT'S THE VIDEO.

That's arguably the central long-run economic question.

 

Second, the video's implicit comparison sometimes becomes:

Europe: public research spending → science 🙂

America: private-sector research → profit 😡

 

That's much too simple.

America's peculiar strength has been precisely its ability to move along the chain:

basic science → applied research → venture financing → company → scale → mass-market product

 

U.S. businesses funded about 75% of total U.S. R&D in 2023, versus 18% from the federal government. And this isn't merely accounting trivia: American businesses overwhelmingly perform the development part of R&D—the stage closest to creating commercially usable products.

Internationally, the difference is visible too. NSF's comparable data put the domestic-business share of R&D funding at about 68% in the U.S., versus roughly 60% in Germany, France and the UK.

And here's the killer: the European Commission itself basically agrees with me about Europe's commercialization problem.

The Draghi competitiveness report says the major post-1990s EU–US productivity divergence is largely attributable to Europe's failure to capitalize on the digital revolution—not merely failure to conduct research, but failure to create companies and diffuse the technology commercially. About 70% of foundational AI models since 2017 were developed in the U.S.; three American hyperscalers account for more than 65% of the European cloud market.

And this statistic is almost comical:

Europe has not created from scratch a company worth more than €100 billion in the past 50 years. The United States created six worth more than €1 trillion during that period.

That's a gigantic clue that research spending is not synonymous with innovation-driven economic growth.

I wouldn't necessarily say European public research itself necessarily crowds out private commercialization. That's hard to establish empirically.

There really can be a crowd-out mechanism: if the supply of scientists and engineers is relatively fixed, government laboratories and subsidized research can bid up the price of those scarce workers and pull them away from private-sector development. Interestingly, a recent IMF model gets exactly that result under severe high-skill labor constraints.

Europe's problem isn't necessarily that government research spending mechanically crowds out private R&D. It's that Europe has historically been much worse at translating research into scalable private enterprises.

That is an enormously important distinction.

And, again, Draghi basically says so: European digital companies have trouble obtaining later-stage financing and scaling, while fragmentation of the single market, financing constraints and regulatory inconsistencies inhibit commercialization.

Which leaves me with a somewhat different interpretation of the whole video

There is a legitimate problem if brilliant researchers who otherwise would have remained at MIT, Stanford, NIH, NASA, etc. are relocating overseas because funding abruptly disappeared. Basic research has positive externalities and uncertain commercial applications, so there's a perfectly orthodox economic case for public support. The video is right about that.

But I don't think the evidence warrants the melodramatic formulation “America is losing its smartest people.” 

I'd frame the bigger problem this way:

America has become accustomed to buying the finished product of other countries' human-capital systems.

We educate some extraordinary Americans, of course, but we've also constructed universities, hospitals and research labs around a huge international supply of extremely talented graduate students, postdocs, physicians, engineers and scientists. If that flow diminishes, suddenly institutions discover how expensive highly skilled labor actually is.

And that produces a much more interesting policy question than “How do we get the foreign scientists back?”

It's:

Why does a country of 340 million people, with some of the richest school districts, universities and corporations in human history, behave as though producing another 100,000 excellent mathematicians, engineers and scientists domestically is beyond its capacity?

The danger isn't just brain drain. It's that importing brains became a substitute at the margin for building brains, while America's real comparative advantage—the private capital, entrepreneurship, commercialization, and enormous consumer market that converts knowledge into useful stuff—gets treated in the video almost as an embarrassing defect rather than one of the principal reasons the U.S. became the world's technological center in the first place.

Research on income and well-being has identified a "satiation point": a level of earnings beyond which money no longer adds to a person's joy in life. Remitly, drawing on work from Purdue University and data from the International Labour Organization, calculated this "price of happiness" for 50 countries and compared it with the average local wage. Almost everywhere, the average worker falls short of the threshold.

The only country where wages have crossed that threshold is Slovenia. There the average wage is about $42,800 and the price of happiness $36,800, meaning earnings run 16% above the level beyond which happiness no longer grows. No other country crosses the line.

Next on the list come not the wealthiest economies but, for the most part, Eastern Europe. Luxembourg (92.8% of its threshold), Estonia (90.5%), Singapore (90.0%), Lithuania, Czechia, Latvia. Seven of the top eleven spots went to Central and Eastern European countries. Their wages are modest by global standards, but their price of happiness is low too, so the gap between the two stays small.

For the rich, high-wage countries the gap is the opposite, huge, because their price of happiness climbs even faster than pay. In the US the average wage is the third-highest in the study, $75,300, yet it covers only 55.8% of the local happiness threshold of $134,800. Australia's price of happiness is the highest on the list, $161,300, double the average wage. The UK, Canada and New Zealand are in the same trap: wages are high, but they're further from their own expensive happiness than workers in less wealthy Eastern Europe.

Interesting data from American household surveys (the ACS for 2020-2024): they calculated a model estimate of lifetime earnings, from age 25 to 64, for graduates of different majors in the US. The incomes of workers of different ages were added up as if a single person went through a whole career at today's wages.

There's a major whose holders earn, on average over a career, less than people with no higher education at all. And it's the only one out of all 64 in the set. The "no degree" line sits at 2.64 million dollars over a lifetime. Yet graduates in Early Childhood Education earn just 2.53 million. Four years of university end up leaving a person behind an ordinary worker with no degree. In the same guild of degree-holding poor are elementary education (2.82 million), social work (2.98 million), art and music education (3.06 million) and theology (3.32 million).

At the other pole is the familiar aristocracy of the knowledge economy. The most profitable studies are economics (6.90 million), chemical engineering (6.68 million) and finance (6.58 million). Computer and electrical engineering trail slightly (6.48 and 6.28 million). The gap between the guild of finance-and-tech types and the guild of preschool teachers and social workers comes out almost threefold.

An intriguing study on how much we can trust cross-cultural comparisons based on low-stakes tests.

Students in the US and Shanghai were given a 25-question math test in PISA format and told they would receive 25 dollars (or 90 yuan), but that one dollar would be taken away for each mistake, with a skipped question counting as a mistake. It turned out that losing money noticeably shook up the American students: their scores rose on average by 1.36-1.59 questions, an effect of about 0.20-0.23 standard deviations. In Shanghai the incentives barely worked, the gain was just 0.22-0.25 questions and not statistically significant. That is, Shanghai students were already working at their maximum.

The American students began answering not only more often (especially in the second half of the test, adding up to 10% to the probability of answering) but also more accurately, the share of correct answers among all attempts rose by about 4 percentage points. The effect was strongest among those whose predicted scores sat near the US average; students with a very low base couldn't make up for a lack of preparation even with an incentive. The researchers then modeled how the US place in the PISA ranking would change if incentives were present there too: the average score would rise by about 22-24 points, moving the country in math from 36th to the level of Australia, around 19th.

The authors stress that this gap between the US and Shanghai reflects not only ability but also differences in intrinsic motivation on low-stakes tests. Without external rewards American students give up noticeably sooner, while Shanghai students go all in. In cultures where studying is seen as a duty to oneself and one's family, students always work at their maximum. Where the test means nothing, results turn out to be a mix of competence and indifference. So direct comparisons of countries by such tests may reflect motivation more than intelligence or school quality.