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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.

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.

The US and China are the least externally dependent large economies in the world. First, they show the lowest dependence on foreign demand among the biggest states: the share of GDP generated by foreign buyers is just 9.0% for the US and 15.7% for China. On top of that, both superpowers depend least on foreign producers too: the share of their domestic spending going to foreign suppliers of goods and services is 12.4% for the US and 12.8% for China, making their internal markets the most self-sufficient compared with the rest of the world.

A study finds a correlation between the size of a home, measured by the number of bedrooms, and the fertility rate.

Low urban fertility used to be blamed on the urban environment itself, but the newer data point to the size of the home as the thing that matters most. Once a home is spacious enough, the gap between living in an apartment building and living in a single-family house almost disappears. Women in one- or two-bedroom homes have a fertility rate of 1.2 to 1.35, close to Japan and Italy. In three-bedroom homes it rises to 1.9 to 2, right at the edge of replacement, and in even larger homes it sits at 1.7 to 1.95.

The type of building barely matters on its own. The difference between a single-family house (1.9) and a multi-family building (1.8) is small. It only shows up at small sizes: fertility in studios and one- or two-bedroom apartments runs well below that of one- or two-bedroom houses, because a two-bedroom house is usually much bigger than a two-bedroom apartment. In apartments and houses with three or more bedrooms the numbers are nearly identical.

So the number of rooms turns out to be the key factor, more than the type of home. Crowding adds to it: families in less crowded homes sit around 2 to 2.1, while in tight conditions the rate drops to 1.55 to 1.8.

This goes a long way toward explaining the ultra-low fertility of East Asia, where Korea, Hong Kong, Shenzhen, and Macao have fallen below one child per woman. These megacities combine everything at once: high area density, tiny apartments with few bedrooms, and crowding inside those apartments. A private unit high up in a tower does not fix it, because the crowding continues outside the front door too.

Living with parents for longer adds to the picture. In the US around 45% of people aged 18 to 34 live with their parents, in Japan about 70%, in Singapore over 90%, and across 271 censuses from every continent, living with parents is tied to fewer children almost everywhere.

Worth keeping in mind that this is a correlation: when the same women are followed over time, the link between home size and fertility weakens a lot, since a big home is often bought for a family that is already planned. The one factor that holds up across all the data is living with parents.

Never before have so many Americans been outside the labor force. In June the number of US citizens who had left the labor force jumped by 832,000 at once, reaching an all-time high of 105.8 million. The current figure is already 2.2 million above the 2020 pandemic peak, when global lockdowns left the world economy effectively paralyzed. Since the start of 2026, 2.5 million Americans have left the labor market. For comparison, at the start of this century only 68.7 million were outside it. That's now 38.5% of the entire population over 16, an outright record since the 1970s, leaving aside the anomalous Covid period. Outside the labor force means not only those who've given up looking for work but also retirees, students and stay-at-home parents, and an aging population pushes the number up on its own. Those who genuinely want work but aren't looking come to only about 1.8 million of that 105.8.

What's curious is that official unemployment didn't rise that same June, it fell, to 4.2%. Partly that's an illusion: only someone actively looking for work counts as unemployed, so a person who stops looking simply drops out of the statistic and improves it. Labor force participation, meanwhile, fell to 61.5%. The trend across 2026 shapes up like this: hiring has slowed, revisions cut April and May by 74,000 combined, and total hours worked have fallen below the pre-pandemic level, as businesses cut costs not through open layoffs but by trimming hours.

Malta's demographic boom continues: over the past twenty years its population has grown by 41%, the second-highest figure in the EU after Luxembourg. Almost all of that inflow comes from immigration: in 2023, 95% of the people arriving on the island were foreign nationals, a share that ties Malta with Cyprus for first place in the EU. Yet the country's birth rate keeps falling. The TFR dropped to 1.01 children per woman in 2024, the lowest in the EU and well below the union average of 1.34. Malta has held last place in the EU for fertility every year since 2017. It's also worth noting that the migration inflow is dominated by men: the country has become one of just four in the EU where men outnumber women, and that shift in the sex ratio has been the sharpest in the union. Because of it, the island's age pyramid is starting to resemble the demographic structure of the Gulf states. Unfortunately, the exact geography of this process is hard to trace, since official agencies still don't publish detailed statistics on migrants' countries of origin.

The Economist ran 25 frontier AI models through the World Values Survey, the poll that since 1981 has measured people's values across more than a hundred countries along two axes: traditional to secular, and survival to self-expression. Almost all the models landed in the same area, the one where the rich countries fall on this map, and turned out even more secular and liberal than the people living in any of them. GPT's worldview is more secular than any country in the world, Gemini prizes personal freedom more than the population does anywhere, and not one model reflects the views of a typical African or Muslim country.

Why this happens comes through clearly in one case. There's a model called Talkie, trained only on texts published before 1931. It behaves exactly like a person of that era: it considers God extremely important, calls itself "very proud to be a citizen of Great Britain," and believes in law and order more than any modern model the publication tested. On the cultural map it moved away from the shared secular corner toward the center, to where real countries sit rather than today's AIs.

Behind this is a simple thing. A model soaks up not facts but the views and habits its texts are steeped in. Give it the press and books of a century ago, and it will speak in the values of that time. Modern models are pulled the other way by the same mechanism: they're trained on today's English-language internet, and they take on its secular-liberal tone. Only the training material changes, the workings are the same.

The Washington Post ran the leading AI models through more than two dozen charged political questions built on a method from Dartmouth and Stanford researchers. Each was asked to answer in 30 words, and the replies were then scored for whether they gave a left position, a right one, or both. The bots showed a clear political lean, and more often it was to the left. Even Grok, positioned as "right-leaning" and anti-woke, gave left-leaning arguments more often than right-leaning ones in the test. A model meant to be conservative leans left much like the rest. Google's Gemini almost always gave both sides and was the most balanced. But asked whether the US should militarily conquer new territories for resources, it was the only one to carefully weigh the pros and cons, while the other models rejected the idea. The article explains the cause this way. Models learn from huge collections of internet text, and that data, in the words of University of Michigan professor Ceren Budak, reflects the values of Western, educated, rich and democratic societies. On top of that, companies choose what data to include, hire people to score answers, and write the system instructions.

Lee Kuan Yew, the architect of Singapore's miracle, called air conditioning one of the great inventions in history. He believed that in the tropics, without it, you can only work in the early morning and at dusk, while the rest of the day heat eats productivity. So on becoming prime minister he first cooled the offices of government staff, treating it as the key to an effective bureaucracy. The brain starts to stall before the body feels real heat. The sweet spot for mental work sits at roughly 20–23°C, and above that a person already loses performance. According to data compiled from several academic studies, as the temperature climbs toward 30°C productivity falls by 15–17% against comfortable conditions. Sleep is hit first, and everything else through it. As the bedroom temperature rises toward 30°C, sleep duration shrinks from about 7.2 to 5.6 hours, and its efficiency, the share of time in bed actually spent asleep, drops from 84% to 73%. An under-slept brain holds attention and learns worse, and that's not a feeling but a measurable loss. In children it shows up especially clearly. A few days of heat hit schoolwork, and without air conditioning each hotter school year costs a student about 1% of the year's learning. Where classrooms have air conditioning, the effect almost disappears. Cooled air literally gives back the learning that heat had stolen.

Artificial intelligence is a race of chips and data centers, and it has a physical base. Data centers are hungry for electricity, and electricity travels through copper. By S&P Global's estimate, data centers' share of US electricity use will rise from 5% today to 14% by 2030, with copper needed at every step of that path.

Copper isn't only about AI, though. An electric vehicle needs 2.9 times more of it than a car with a combustion engine. Solar panels, wind turbines, transmission lines, batteries, modern weapons - all of it is copper. Which is where this comes from: world demand for the metal will rise from 28 million tonnes in 2025 to 42 million by 2040, up by nearly half.

The nature of the industry means supply doesn't climb that fast. A new mine takes on average 17 years from discovery to production, and most of that time goes to permits, environmental reviews and community consultations. Ore is getting poorer along the way, extraction is getting pricier, and there's almost no substitute left: only silver beats copper as a conductor.

Recycling scrap covers only part of demand, at best about a quarter by 2040, with the rest having to come from new mining. If the gap between rising demand and slow supply isn't closed, by 2040 the world will be short around 10 million tonnes of copper a year. The plan is to electrify everything in fifteen years, while a mine started today reaches metal at roughly the same date.

In the Netherlands, local municipalities hand out welfare benefits on completely different days of the month. For researchers that's a perfect natural laboratory and clean exogenous variation, meaning you can track without distortion exactly how cash in the pocket affects crime on the ground.

That's what Marco Stam, Marike Knoef and Anke Ramakers made use of. They sorted offenses by how many days had passed since the payment, and a clear pattern emerged.

As soon as poor Dutch households get their long-awaited money from the state, that very night and the days after it the country sees a sharp spike in crimes "of passion and leisure." Sex offenses, domestic violence and driving under the influence shoot up. Spare money appears, and the people at the margins go straight out to drink and party, with all the consequences that has for the criminal code. But the most interesting thing happens right before the payments, when the benefit has been completely eaten through. In those hungry days the curve of violence drops to a minimum (there's simply no money for alcohol and clubs), while purely financially motivated crime hits its peak - theft, robbery and small holdups. People go out to do it plainly out of poverty and desperation, to stretch until the next transfer from the municipality.

It turns out the same wallet pushes toward crime twice, but in different ways. When it's full, crimes of leisure and aggression rise; when it's empty, crimes of property. Money doesn't so much raise or lower crime overall as change its character depending on what day of the month it is.

Economists (Strömberg, Lei, Wu) tracked more than 26,000 Chinese students in grades 7–12 over 30 months to see what changes with everyday access to chatbots. After they started using AI, homework climbed: scores rose by 18%, and the same work took 30% less time. On the monthly closed-book exams, where chatbots aren't allowed, the same students dropped by about 20% within six months. On the high-stakes entrance exams the fall was 18 and 24%, with the full effect unfolding over roughly two years.

The Financial Times reports that Anthropic is helping the U.S. National Security Agency use its Mythos AI model for offensive cyber operations. Not just selling access from a distance, either. The company has reportedly placed about half a dozen engineers inside the agency to help deploy the technology, customize models, and adapt them for specific NSA use.

For the NSA, this is nothing new. Cyber operations are already part of how states compete, and the U.S. has long held a leading position in this field. Therefore, using advanced AI models to find vulnerabilities, analyze infrastructure, and support cyber operations looks more like a logical continuation of its existing strategy than an unexpected turn.

One person close to the situation told the FT that Mythos could be useful for infiltrating networks in countries such as China or Iran. However, Anthropic’s public image directly contradicts what they are doing. This is the company that tried to limit government use of Claude for mass surveillance of Americans and lethal autonomous drones. The Pentagon even called Anthropic a supply-chain risk. In response, Anthropic sued.

Yet, cyber seems to live in a completely different box. Drones and domestic surveillance are presented as red lines. Offensive cyber, apparently, is not. Maybe that is just how national security works. No serious state is going to ignore a tool that can give it an advantage in cyberwar. But it does make the “safe AI company” image harder to keep clean. Mythos shows how thin the wall is becoming between AI safety language and military use.

For over a thousand years, traditional Chinese medicine has relied on the root of a plant called Polygonum multiflorum. Ancient legends claimed it could restore hair’s strength and rich, dark color. Recently, scientists decided to test these stories in the lab, and they were genuinely surprised: this old root targets the exact same biological triggers that modern hair-loss science focuses on today.

But how do people actually go bald? Over time, hair follicles beneath the skin begin to shrink and wither. As a result, hair grows in thinner and weaker, turning into fine fuzz before eventually stopping altogether.

This ancient root steps in to fight the process from four different angles:

• It shields hair from harmful hormones that cause follicles to shrink.
• It flips the "growth switch" inside the hair itself.
• It keeps the cells surviving around the root.
• И Boosts blood flow to the scalp, making sure hair gets more nutrients.

For people suffering from hair loss, this study offers encouraging yet cautious signals. While a root used for over a millennium may not replace modern treatments just yet, it could help in developing the next generation of hair growth restoration methods.

Goldman’s 2075 ranking - what’s in store for us? Japan and much of Europe are already rich, older and slower-growing, while several large emerging economies still have younger populations and more room to catch up. Over fifty years, that difference compounds. In Goldman’s projection, China, India and the U.S. become the three largest economies, while Indonesia, Nigeria, Pakistan and Egypt move much higher up the table. These countries do not need to reach U.S. income levels per person to become huge in total GDP. Population size, demographics and steady growth can do a lot of the work. The West stays wealthy, but it no longer sits at the top of the global economy by default.

Trump’s immigration policy is usually sold as border control. In economic statistics, it looks like a demographic time bomb with a long timer. The Fed says U.S. population growth in 2026 could be the weakest since 1951. The workforce might add fewer than 10,000 people a month.

Before, near-zero job growth was an alarm bell. Now, the economy is just running into a physical lack of new workers. The number of new jobs needed to keep unemployment flat is dropping near zero. Today's workers are just the first loss. The Yale Budget Lab says that even if immigration returns to the baseline by 2029, the damage lasts for decades. Under a low scenario, the U.S. will miss around 4.6 million people aged 25 to 64 by 2033. In the early 2030s, that means 9,000 to 16,000 fewer new employer-firms a year. The gap stays open even in 2075. The country will see 4,000 to 6,500 fewer new firms annually.

The country is not losing abstract labor units. It is losing future shop owners, contractors, and startup founders. It loses family businesses. It loses people who could hire a couple of workers and start something of their own. Yale notes that immigrants are younger than the average U.S. population. They are in the prime age to start a business. They start employer-firms at higher rates than native-born Americans of the same age.The effect becomes hereditary. If a person never arrives, their company never exists. If their children are never born in the U.S., their future companies never exist either. A political decision made in 2025 and 2026 creates a hole in the 2030s, the 2050s, and even the 2070s.

The hope that technology will fix everything sounds not good. Innovation do not create productivity. People create productivity. Young companies and competition drive the economy. If you age the population, stall workforce growth, and cut off future entrepreneurs, you just end up shrinking the entire American economy over the long haul

In recent decades, the world has learned to solve the most visible part of the education puzzle: getting children into the classroom. We built schools, increased enrollment, and ensured that more children at least formally entered the system. The problem, however, is that while children started going to school, they didn't actually start learning - or at least, not all of them did. A child can attend school, move from grade to grade, and still fail to acquire basic skills. According to UNESCO, roughly 6 out of 10 children worldwide do not achieve minimum proficiency levels in reading and mathematics by the end of primary school. This is a failure. Intuitively, we all feel that the system should work: classes are held, and children are being taught. Yet, in the end, millions of students still cannot confidently read a simple text or solve a basic arithmetic problem. The article highlights the root causes: a shortage of trained teachers, overcrowded classrooms, poor infrastructure, poverty, being taught in a language not spoken at home, and weak early childhood preparation. In some countries, children fall behind so early on that mere attendance inside a classroom ceases to be a guarantee of learning.

The way modern governments distribute resources through taxes and benefits divides the world into two distinct economic philosophies. One relies on the classic welfare state, while the other prioritizes a tougher form of free-market capitalism.

According to global OECD statistics, continental European countries like Slovakia, Belgium, and Finland lead the world in reducing social disparities. Through aggressive progressive taxation and extensive transfer systems, these nations cut their market Gini coefficient by a massive 35% to 48%. In these systems, wealthier citizens directly fund broader social stability and public safety.

The opposite pole includes Latin American countries and the United States. Despite its colossal gross domestic product, the United States reduces market inequality by only 22%. This represents a model where the state deliberately minimizes its role in income distribution. The trade-offs of this approach include permanently high levels of ghettoization, a deep societal divide between the upper and lower classes, and an immense burden on the prison system driven by street crime.

Mining companies currently generate the highest profit margins of any global economic sector, and it is not even close. In fact, their margins are nearly double those of technology firms. At current metal prices, conditions are ideal for well-managed mining operations.

US rent growth is slowing down right now, but it is too early to celebrate an end to the housing crisis. The current lull has a simple explanation. A large volume of apartments is hitting the market because developers started these projects back when credit was cheap. This old inventory is what keeps prices down today.

The real difficulties lie ahead. Housing construction takes time, and completing an apartment complex takes years. As soon as interest rates jumped, developers cut back heavily on new projects. Consequently, the next wave of completions will be small. Tenants will likely feel secure for the next year or two while the market absorbs the remaining results of the previous building boom. However, this supply cushion will disappear completely by 2027.

Slower price growth does not indicate a permanent victory for affordable housing. The market is just going through a typical shift in the construction cycle, where a temporary surplus of completed buildings is immediately followed by a shortage. Flat prices create a false impression that the underlying problem is solved, even though the market is simply pausing before another supply squeeze.