Economist Writing Every Day

Autism Is Largely Genetic (Not Environmental), and Does Not Necessarily Prevent a Good Life

March 3, 2026March 3, 2026Scott BuchananLeave a comment

Autism is a condition that can cause enormous anxiety and grief, especially for parents of autistic children. The economic implications are also considerable. A 2021 study by Blaxill, et al., estimated the annual costs to society of autism in the U.S. to be $223 billion in 2020, $589 billion in 2030, $1.36 trillion in 2040, and an astonishing $5.54 trillion by 2060.

The rising diagnosis rates are sometimes attributed to changes in environmental factors or diets. It is obviously essential to get the science right on this. Here I will summarize an article by epidemiologist Mark Strand, “Understanding Autism Spectrum Disorder Epidemiologically and Theologically”. This article was published on the Biologos web site on January, 2026. The author addresses the medical aspects sand also the moral aspects. Upfront disclaimer: I have no expertise in this area; I am just trying to faithfully convey the scientific consensus.

The Myth of the “Autism Epidemic”

The article begins by addressing a common misconception: that autism is experiencing a sudden, alarming surge in cases—an “epidemic.” This idea gained traction when the current administration announced a “massive testing and research effort” to identify environmental causes behind the rise. But as the author explains, this framing is scientifically inaccurate.

Autism is not an acute condition like strep throat or a viral outbreak. It’s a lifelong neurodevelopmental disorder that emerges during early brain development, typically between ages two and four. Unlike infectious diseases that appear suddenly and resolve quickly, autism is chronic and complex. The term “epidemic” refers to a rapid, atypical increase in cases—something that doesn’t align with the actual data.

In 2022, the CDC’s Autism and Developmental Disabilities Monitoring (ADDM) Network reported 32.2 cases of ASD per 1,000 children. This is massive (fourfold) increase from two decades ago. But this rise isn’t due to a sudden environmental trigger. Instead, it reflects broader diagnostic criteria, increased public awareness, better screening practices, and greater access to services:

While this increase may seem alarming at first glance, it is widely accepted that it largely reflects changes in the definition used for ASD and the importance placed on ASD by society and educational systems.

Since it was first recognized as a condition in the Diagnostic and Statistical Manual of Mental Disorders (DSM), the definition for ASD has been broadened several times. This included combining a cluster of related neurological disorders into one disorder, relaxing the age of onset, and better classifying the presentation of autism in girls and children of color, leading to more accurate, but higher, numbers.

States like California and Pennsylvania, which have robust early intervention systems and strong Medicaid coverage for autism services, report the highest prevalence rates.

Genetics Over Environment: The Scientific Consensus

One of the most critical points the article makes is that autism is primarily genetic. Twin studies show that if one identical twin has autism, the other twin has a 60% to 90% chance of also having it. A large Swedish study of over 37,000 twins found that 83% of autism cases could be attributed to genetics.

While environmental factors may play a role, the evidence is far from conclusive. The article debunks popular myths—like the claim that acetaminophen (Tylenol) use during pregnancy causes autism. High-quality studies, including one of 2.5 million children in Sweden with sibling controls, found no link between prenatal acetaminophen use and autism risk. Similarly, the idea that vaccines cause autism has been thoroughly discredited by decades of rigorous research.

The real danger lies in chasing unproven causes—a practice that wastes resources and distracts from meaningful science. Instead, researchers should focus on gaps in knowledge using the scientific method: building on what we know, forming new hypotheses, and testing them rigorously.

The Spectrum of Experience: Diversity, Not Deficit

Autism is not a monolithic condition. It exists on a spectrum, ranging from individuals with significant support needs to those with high intelligence and exceptional skills.

The article highlights some striking statistics:

14% of autistic individuals graduate from college, compared to 32% of the general population.
Among college graduates with autism, 34.3% major in STEM fields, significantly higher than the 22.8% in the general population.

These numbers challenge the harmful stereotype that autistic people are universally disabled or burdensome. Many autistic individuals thrive in science, technology, engineering, and mathematics—fields that value pattern recognition, attention to detail, and deep focus.

Yet, challenges remain. Social communication difficulties, restricted interests, and repetitive behaviors can be isolating. Early intervention—especially for those with moderate to mild autism—can make a meaningful difference in socialization and quality of life.

A Call for Grace, Truth, and Inclusion

The article concludes by noting that autism is not a tragedy, but a part of human diversity. It calls on society at large to respond with truth, grace, and care—not fear or stigma.

The article notes: “It is not a good use of resources to repeat studies on well-established scientific evidence or chase popular beliefs about supposed causes.” Rather than searching for a single cause to “eliminate,” we should focus on understanding, supporting, and empowering autistic people. This includes investing in early screening, improving access to therapy, and promoting inclusive education and employment.

The rise in autism diagnoses is not a crisis to panic about—it’s a call to do better with better science, better policies, and better compassion. By grounding our understanding in data, embracing neurodiversity, and responding with love, we can build a world where autistic individuals are not just accepted—but valued.

How to retain your worst employees, US Army edition

March 2, 2026March 2, 2026mdmakowsky2 Comments

I almost titled this article “The dumbest auction I’ve ever heard of”, but I want to be careful, just in case I fundamentally don’t understand the auction the US army is creating for it’s warrant officers.

Under the new program, called the Warrant Officer Retention Bonus Auction, eligible warrant officers will submit confidential bids for how much money it would take to keep them on active duty for an additional six years, the Army announced last week….Eligible officers can submit a minimum bid of $100 per month, increasing at $100 intervals, the release said, and once the market closes, the Army will use those bids to define a “single, market-clearing bonus rate,” to pay as many officers as the service’s budget allows.

Officers who submit bids at the chosen rate — or lower — will be awarded those bonuses. The catch? Those whose bid above the rate will get no bonus.

Did I just read that right? The they want their officers to 1) estimate their reservation bonus for remaining in the employ of the US Army, and then 2) bid that within a closed bid auction. There is a pool of bonus funds, such that a maximum bid will be established, paying out exclusively to those officers who bid below the maximum. All officers who estimated their reservation bonus (effectively, their reservation wage) above that threshold will receive nothing, almost guaranteeing their exit from the Army.

Are. You. Kidding. Me.

Allow me to rephrase it another way. You want to award retention bonuses to the officers with the weakest outside options and, in turn, have the lowest reservation wages while, at the same time, awarding nothing to your best and brightest officers, those with the greatest outside options? I doubt you could more cleverly design a policy to maximally purge talent from the armed forces. Military bodies have enough problems as is retaining talent, particularly as the promotion pyramid gets narrow in the upper ranks that continue to be filled by older officers uninterested in retiring. We’re going to have to invent entire new swaths of Murphy’s Laws to internalize these leadership shenanigans.

There are, in my outsider estimation, two broad categories of officers with the strongest outside options: 1) young officers with strong technical skills and a demonstrated ability to engage critical thinking skills under pressure, and 2) top tier officers whose personal networks are invaluable to military contractors looking to secure big ticket military contracts. This auction structure will create a mass exodus of the former and an accelerated pathway to the latter. Both are bad, but the loss of young talent could be absolutely devastating as warfare shifts to an ever more technical landscape.

Please tell me I am missing something in the comments and that this isn’t the dumbest labor market policy in the history of moderm US military operations

Learn to Ode 2026

February 28, 2026February 28, 2026Joy Buchanan1 Comment

Joke: https://x.com/TheLincoln/status/2027215235103207693

Writing about the Citrini Research report on February 28 feels like a being 6 years behind (it was only 6 days ago).

“THE 2028 GLOBAL INTELLIGENCE CRISIS: A Thought Exercise in Financial History, from the Future”

Two things the white-collar chattering class fears is that their jobs will disappear or their stock portfolios will crash. The Citrini note put that feared scenario in a picture frame so we could stare at it, like Annie Jacobsen’s book on nuclear war. The post imagines a 2028 scenario: AI automates white-collar work, companies collapse, private credit blows up, mortgages default, unemployment hits 10%.

Brian Albrecht responded: “We don’t need to just make up fantasy stories: Using economics to read Citrini Research’s AI”

Tyler encouraged us to consider a response put out by Citadel “The 2026 Global Intelligence Crisis“

Even cognitive automation faces coordination frictions, liability constraints, and trust barriers. It seems more likely that AI will be a complement rather than a substitute for labor is many areas.

One barrier to AI taking all the white-collar jobs as quickly as 2028 is just physical scaling constraints.

Having done research on “learn to code” (Buchanan 2022), I always watch new developments with interest. In 2023, I told an auditorium full of students in Indiana to learn to code if they don’t hate the work too much. At that time I had forecast that AI tools would make coding less miserable but not eliminate the need for technical human workers. Even if that was good advice at the time, is it still good advice today? I wish I had time to put up a blog on this topic every week.

Adjustments can happen along the margin of price as well as quantity. Wages to programmers can come down from their previously exalted heights, which could help the market absorb some of the young professionals who listened to “learn to code” in 2023.

So, now that the value of coding skills is in question, people are turning back to the value of the maligned English degree. It has been true for a long time that employers felt soft skills were more scarce than STEM degrees. I might add that an economics degree conveys a highly marketable blend of hard and soft skills.

Buchanan, Joy (2022). “Willingness to be paid: Who trains for tech jobs?” Labour Economics,
79, Article 102267.

Regulatory Burden By Presidential Administration

February 27, 2026February 27, 2026Zachary Bartsch1 Comment

During president Trump’s first term in office, he made a bunch of waves (as he’s wont to do). His more educated supporters said that he engaged in substantial deregulation of telecommunications, which got a lot of press. There was a quiet contingent of educated voters who were relatively silently supportive on Trump’s regulatory policy, even if his character was indefensible or his other policy was less desirable.

But was Trump a great deregulator? Or was it one of those cases when we say that he regulated *less* than his fellow executives? The George Washington University Regulatory Studies Center can help shed some light with their data. Specifically, they have calculated the number of ‘economically significant’ regulations passed during each month of each president going back through Ronald Reagan’s term. What counts as ‘economically significant’? The definition has changed over time. But, generally, ‘economically significant’ regulations:

“Have an annual [adverse] effect on the economy of $100 million or more
Or, adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, or tribal governments or communities.”

The only exception to this is between April 6, 2023 and January 20, 2025 when the threshold was raised to $200 million.

The Data

The graph below-left shows the number of economically significant regulations for each president since the start of his term, through July of 2025. It’s reproduced from the link above except that I appended Trump’s second term onto his first term. What does the graph tell us? There doesn’t seem to be much of a difference between republicans and democrats. Rather, it seems that, generally, the number of economically significant regulations increases over time. Importantly, the below lines are cumulative by president. So each year’s regulations each cost $100m annually and that’s on top of the existing ones already in place. So, regulatory costs generally rise, with the caveat that we don’t see the relief provided by small or rescinded regulations (for that matter, we don’t see small regulatory burdens here either). Something else that the below graph tells us is that presidents tend to accelerate their economically significant regulations prior to leaving office. Reagan was the only exception to this pattern and he *slowed* the number of regulations as the end of his term approached.

Below-right is the same data, but the x-axis is months until leaving office. Every president since Bush-41 has accelerated their burdensome regulations during their final months in office. The timing of the acceleration corresponds to how close the preceding election was and whether the incumbent president lost. Whereas all presidents regulate more in their last 2-3 months in office, the presidents who were less likely to win re-election started regulating more starting around eight months prior to leaving office. Of course, they wouldn’t say that they expected to lose, but they sure regulated like there was no tomorrow.

What about Trump? Trump’s fewer regulations is caused by his single term. He definitely still added to the regulatory burden (among economically significant regulations, anyway). While Trump started with the fewest additional regulations since Reagan, and Biden started with the most ever initial regulations, together they earn the top prizes for most regulations added in their first term.

What if we append these regulations from end-to-end? That’s what the below chart does. We do have to be careful because the series is a measure of gross economically significant regulations and not net economically significant regulations. So, it’s possible that some rescissions dampened the below values, but this is the data that I have for the moment. While each presidential administrations increases regulation more than the prior, the good news is that the rate of change is not exponential. The line of best fit is quadratic. We’re experiencing growing regulations, but at least it’s not compound growth.

The Cost

We can estimate the costs of these economically significant regulations. It’s a rough cut, and definitely a lower bound since rescission is rare and $100 million is itself a lower bound, but we can multiply the number of regulations by $100m to get minimum annual cost. Like I said, the Biden criterion from April 2023 through January 20, 2025 changed, so those regulations get counted as $200 million instead. The change in definition means that the regulation counts underestimate the late-term Biden regulations relative to the other presidencies.

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Humanity’s Last Exam in Nature

February 26, 2026February 25, 2026James BaileyLeave a comment

Last July I wrote here about “Humanity’s Last Exam”:

When every frontier AI model can pass your tests, how do you figure out which model is best? You write a harder test.

That was the idea behind Humanity’s Last Exam, an effort by Scale AI and the Center for AI Safety to develop a large database of PhD-level questions that the best AI models still get wrong.

The group initially released an arXiV working paper explaining how we created the dataset. I was surprised to see a version of that paper published in Nature this year, with the title changed to the more generic “A benchmark of expert-level academic questions to assess AI capabilities.”

One the one hand, it makes sense that the core author groups at the Center for AI Safety and Scale AI didn’t keep every coauthor in the loop, given that there were hundreds of us. On the other hand, I’m part of a different academic mega-project that currently is keeping hundreds of coauthors in the loop as it works its way through Nature. On the third, invisible hand, I’m never going to complain if any of my coauthors gets something of ours published in Nature when I’d assumed it would remain a permanent working paper.

AI is now getting close to passing the test:

What do we do when it can answer all the questions we already know the answer to? We start asking it questions we don’t know the answer to. How do you cure cancer? What is the answer to life, the universe, and everything? When will Jesus return, and how long until a million people are convinced he’s returned as an AI? Where is Ayatollah Khamenei right now?

Most Married Women with Children Were Working By the Late 1970s

February 25, 2026Jeremy Horpedahl1 Comment

A recent essay by Jeffrey Tucker as “Has Life Really Improved in Half a Century?” Specifically, Mr. Tucker is interested in measuring median income of families (he uses household income, but families are clearly what he is interested in).

Tucker grants that real median household income has increased by about 40 percent from 1984 to 2024 (if he had used family income instead, the increase is almost 50 percent). But… he says this is illusory. That’s because it now takes two incomes to achieve that median income, whereas it only took one income in the past:

“Adding another income stream to the household is a 100 percent rise in work expectations but it has yielded only a 20-plus percent rise in material income. The effective pay per hour of work for the household has fallen by 40 to 50 percent!”

(He makes a data error by saying that in 1976 real median household income was $68,000-$70,000, when it was actually $59,000 in 2024 dollars in 1976 — real income didn’t fall from 1976 to 1984!)

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Sleigh or Sled Shovels: Move Lots of Snow with No Lifting

February 24, 2026Scott Buchanan2 Comments

Now that we have your attention (if you just got buried in a blizzard yesterday), let’s talk about shoveling snow. Everyone knows how a standard snow shovel works. You bend down, with one hand on the end of the handle and the other hand halfway along the handle, you shove forward, load up the shovel blade, then (Ooof!) lift it up and throw the snow where it needs to go. For many of us, this action uses muscles and joints that are not conditioned for it. Fun facts: every year some 100 Americans die from shoveling snow, and another 11,000 or so end up in the emergency room.

Is there a better way? Well, a powered snowblower can work. But that doesn’t fit everyone’s situation. It turns out there is a better way to manually shovel snow, that fits many (not all) situations.

As I was reading about “electric snow shovels” (more on that another time), I ran across mention of “sleigh shovels” or “sled shovels” or “snow scoops.” Apparently, they are very widely used by Canadians and Alaskans, who ought to know something about snow. A genius aspect of these shovels is that you never have to lift them.

Here is a picture of a 24” Garant brand sled shovel:

Source: Ace Hardware

Here’s how they work: Start with the position shown, shove it forward (you get to use both hands out in front of you, in an ergonomically good position), till the scoop is largely filled with snow. Then, tilt it back a little, and push this load forward, sledding along until you get to the edge of the driveway. Keep pushing it another several feet, out onto the lawn. Then dump the snow off the shovel by a quick shove forward and a sudden jerk back, to pull the shovel out from under the snow. Plan your dumping points so as to get a gradual ridge beside the driveway, not a narrow, high ridge right at the edge.

Here is a 47-second video demo, on a small scale.

Take a quick look at 1:40 – 3:40 (two minutes) of this video to see a more challenging situation (deep snow, big existing ridge on edge). This shows that one scoop shovel-full is equivalent to more than three regular shovel-fulls, and this snow is expelled from the driveway with NO LIFTING. It’s beautiful! Here are two screen shots from this video:

Garant seems to be the most well-established brand here. ACE hardware (see photo above) is selling them for $70. On Amazon, I see a Garand model being sold for an eye-watering $266, maybe scalping prices for the latest blizzard. That is a lot of money for a plastic scoop with a metal handle. You can probably do better by shopping elsewhere or at a different time.

I am tempted to get one, but I don’t have a wide driveway with grassy dumping areas on the sides. I have to shovel mainly steps and narrow sidewalks, often with wet, slushy, not super deep snow. Sleigh shovels can work in these situations, but their advantages are muted, compared to the deep powdery snow found in colder regions.

But if I were living in Boston or Providence or New York, a sleigh shovel would be mighty handy right now.

Supply and demand has a mind of its own

February 23, 2026February 22, 2026mdmakowskyLeave a comment

I think there’s a lot of crosstalk about AI in part because proponents tend to focus on the immenient supply side shifts from innnovation, while critics seem to happily observe failures to stoke consumer demand. Not being much of a futurist, I’m largely content to watch and wait with minimal speculation. At the same time, I see signs of increasing demand for other products, in blatant disregard for past and present identity politics. It’s probably good to remember that supply and demand are less a beast to be wrangled than a rocking ocean to be adapted to.

Learning the Bitter Lesson at EconLog

February 21, 2026Joy BuchananLeave a comment

I’m in EconLog with:

“Learning the Bitter Lesson in 2026“

At the link, I speculate on doom, hardware, human jobs, the jagged edge (via a Joshua Gans working paper), and the Manhattan Project. The fun thing about being 6 years late to a seminal paper is that you can consider how its predictions are doing.

Sutton draws from decades of AI history to argue that researchers have learned a “bitter” truth. Researchers repeatedly assume that computers will make the next advance in intelligence by relying on specialized human expertise. Recent history shows that methods that scale with computation outperform those reliant on human expertise. For example, in computer chess, brute-force search on specialized hardware triumphed over knowledge-based approaches. Sutton warns that researchers resist learning this lesson because building in knowledge feels satisfying, but true breakthroughs come from computation’s relentless scaling.

The article has been up for a week and some intelligent comments have already come in. Folks are pointing out that I might be underrating the models’ ability to improve themselves going forward.

Second, with the frontier AI labs driving toward automating AI research the direct human involvement in developing such algorithms/architectures may be much less than it seems that you’re positing.

If that commenter is correct, there will be less need for humans than I said.

Also, Jim Caton over on LinkedIn (James, are we all there now?) pointed out that more efficient models might not need more hardware. If the AIs figure out ways to make themselves more efficient, then is “scaling” even going to be the right word anymore for improvement? The fun thing about writing about AI is that you will probably be wrong within weeks.

Between the time I proposed this to Econlog and publication, Ilya Sutskever suggested on Dwarkesh that “We’re moving from the age of scaling to the age of research“.

Vaccine Variety

February 20, 2026February 20, 2026Zachary Bartsch4 Comments

The flu and covid-19 vaccines don’t work super well. Both vaccines permit infection and transmission at quite high rates. The benefit from these vaccines come largely from reductions in mortality or severe symptoms conditional on infection. The covid-19 vaccine is itself especially risky or ineffective depending on the age and health of the individual. Plenty of people eschew vaccines.

I live in Collier County, Florida where there have been 61 confirmed cases of measles so far this year. I have since learned that Measles is EXTREMELY contagious. It floats around the air and on items and just sort of hangs out and waits for a place to replicate. I’ve also learned that symptoms include a fever, eye irritation, possible brain swelling, severe dehydration, and a characteristic rash. The severe dehydration easily puts people in the hospital, the eye irritation can lead to permanent vision loss, and the brain swelling can be acute, or a symptom delayed by 5-6 years, which can also be fatal. I’ve also learned that having the vaccine, which is usually administered in two doses, provides about 97% immunity. The vaccine works so well, that the department of health recommends no behavioral change among the vaccinated population when there is a measles outbreak. Barring unique circumstances, measles immunity can persist for a lifetime.

Unfortunately, a large segment of the anti-vaccine mood affiliation retains the salience of the covid-19 vaccine characteristics. Other vaccines and diseases in the typical pediatric schedule are not similar. Most of these prevent infection >90% of the time (TDAP is low at 73%), prevent transmission, reduce mortality when there are breakthrough infections, are effective for years or decades, and are extremely safe for all age groups.

The risks of disease versus the corresponding vaccine are orders of magnitude away from each other. The tables below summarize the data (with sources). I did not double check the source on every single figure. If you glance below, then you’ll see why: Even if the numbers are closer by 10 or 100 times, vaccines still look really good.

First, mortality: The data is divided by disease and age group, and provides mortality rates for both the disease and for the vaccine. The numbers are proportions, conditional on infection or vaccination. There are a lot of zeros in the vaccine mortality rates and certainly more than for the diseases. For example, a measles infection is 10,000 more lethal than the MMR vaccine which prevents it. In fact, all of those zeros in the vaccine rates reflect mortality that is so uncommon, that the estimated one out of every 10 million is just rounded up because researchers don’t think that the risk is zero.

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