Korea So Far

Joy BuchananLeave a comment

I am going to Korea next year. I look forward to being able to relay travel observations. Here’s what I can say so far.

I have several distinct personal points of connection to Korea through my family, my husband’s family, and my next-door neighbor here in Alabama. There is a George Mason U. campus in Korea. I hope to explore all of those connections when I go.

KPop Demon Hunters was an absolute phenomenon on Netflix last year. Every school child in America, across every race and state, knew the songs. As for the soundtrack, I determine:
“Golden” – correctly rated
“Soda Pop” – overrated
“What it Sounds Like” – underrated
And what is interesting in September of 2026 is how fast the whole thing died out. Trends come and then they really go in the monoculture (is this the “unicontext”?).

I am learning Korean by doing one lesson per day on Duolingo. Progress is slow, partly because I do not expect to need to become fluent ever. I am enjoying this. I have frittered away a good number of hours on cognitive trivia like computer Solitaire and Wordle. On the margin, I encourage a few of you English speakers to make learning a Non-Romance Language your new game. I have also used Duolingo to try to brush up on Spanish and French, but it’s fun to enter a totally different mindset altogether. It’s also difficult and might be frustrating if I had a higher bar for my performance.

If any readers have a connection in Seoul, please let me know.

Economics Major Income Premium

Zachary Bartsch2 Comments

I’ve written about Economics major incomes before. The consistent empirical fact is that they earn more than most other college majors. But why? My working theory is that’s it’s due to human capital differences.

Challenge 1: Top Business Schools

“The highest ranked business schools offer economics majors with various business concentrations instead of separate business majors. So, the high average income of econ majors is due to those top tier finance concentrations and the like.”

This challenge doesn’t hold water. If the high average income were just due to top performers, then omitting them would break the pattern of high economic major compensation. But it doesn’t. Trimming the top and bottom 10% of incomes for each major doesn’t cause economics to fall much in the ranking.

Challenge 2: Econ Majors Choose Higher Paying Occupations

“Economists aren’t especially productive. They merely choose higher pay occupations. Other majors could achieve the same thing if they wanted to.”

This challenge is partially true. Economics majors do choose higher paying occupations. The Bureau of Labor Statistics has an extensive list of occupation categories and codes that are linked to the American Community Surveys. I examine the broadest categories that have sample sizes of at least 40 for each economics and other majors.  The below scatter plot shows the relationship between average income by occupation and the proportion of economics majors who chose to work in those occupations. There is clearly a positive relationship. Economics majors do choose higher paying occupations.

But the claim about productivity isn’t quite right. If economics majors were just as productive as other majors within their occupational category, then they would earn around the average income within each occupational category. But they don’t! Below is a chart that plots the average income premium over non-economics majors within each occupational category (error bars are one standard error).  The occupations to the left are more abstract or even social in nature. That’s where economics majors earn their big income premium. Further to the right are occupations that are more ‘hands-on’. Economics majors earn about the same as non-econ majors in those categories.

The one interesting case is ‘Computer and Mathematical’ occupations, which are abstract in nature and yet economics majors have no better earnings on average. Those occupations have a higher than typical proportion of Computer Engineering, Computer Science, Computer Information Systems, and Mathematics majors. Given that those majors 1) also have training in abstract theory and 2) are highly specialized, it’s impressive to me that economists can keep up.

Additionally, economics majors are not uniformly distributed across occupational categories. They tend to pursue occupations in which they have an advantage as indicated by their wage premium. The below chart has the same horizontal axis as the one above and has more mass further to the left. A higher proportion of economics majors are in the occupations where they outperform others in the same occupation.

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New Health Freedom Index

James BaileyLeave a comment

The Center for Modern Health and the Knee Regulatory Research Center just released an index of how free residents in each state are to provide and pursue health care as they see fit. Their summary map looks like this:

The index was created by averaging measures of freedom in 54 separate categories, summarized into the 5 broad areas of Professional, Institutional, Patient, Payment, and Delivery Freedom. A report with maps for each of the 54 underlying measures is here, and a spreadsheet with all the data is here.

This project represents a major effort on an important issue, but I have to say my favorite part is just how unusual the final map of the overall ranking looks. I’ve created many maps of the states based on data and seen many more, but almost all of them (no matter the underlying variable they represent) end up falling into a handful of looks. They are either secretly maps of population density, or North vs South, or East vs West, or the South + Appalachia (high poverty, low health and education, et c). But the Health Freedom Index groups states in a way I’ve never seen before, putting Maine and New Hampshire with Mountain West states as the most free, while South Carolina and Louisiana join California and much of the Northeast among the least free.

Some of the Index’s creators will be presenting it online on September 18th.

Note: I’m affiliated with the Knee Regulatory Research Center at WVU, but I wasn’t directly involved with this project. I’m working on a different data project with Knee I hope to discuss here soon.

Women Have Always Worked, Often in the Formal Labor Force

Jeremy HorpedahlLeave a comment

In a February 2025 blog post, I created a chart showing male and female work patterns both inside and outside the home, going back to 1900. The data was for the United States, and the general trends were more female work in the paid labor force, more male work in the household (rather than paid labor force), but overall total hours of work being roughly constant from 1900 to 2023 (on average, of course).

Despite women always working, 1900 did look a lot different: adult working-age women in the US spent about 50 hours per week working in their own household, and a little under 10 hours per week in the formal paid labor force. Men were almost a mirror image of this (though working a few hours less per week in total).

So the gradual change over the 20th century was a huge shift in gender roles, and by 2023 women on average spent about the same number of hours in household and paid work. But the US is something of an anomaly here. Female labor force participation was higher — in some cases much higher — in other developed countries around 1900. Using data collected by Claudia Olivetti, here are female labor force participation rates for working age women (generally ages 15-64) around 1890-1900 (I average across years if there are multiple estimates):

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What If AI Earnings Fall Far Short of Expectations?

Scott BuchananLeave a comment

Trillions of dollars are being plowed into building U.S. data centers to handle future AI compute demand. These are being paid for by rich people and organizations, anticipating juicy returns on their investments. Those juicy returns depend on consumers (individuals or businesses) being willing to pay enormous amounts for access to AI.  Commentators are so enamored with the glorious prospects of how AI will end poverty and maybe even death, that it is hard to find a clear statement of who exactly will pay how much for all this. Lots of folks are happy to pay $20/month for AI. $200/month? Not so much.

I predict that those earnings will fall far short of expectations. We observe that AI consumption is bifurcating into two main markets, a commodity tier and a premium tier (with, of course, sub-tiers within each of those broad categories).  Chinese models are readily available over the internet, and they have proven capable of performing well enough to handle most AI tasks. The Chinese models are priced far lower (on the order of 10X lower) than the big U.S. “frontier” models (ChatGPT from Open AI, and Claude suite from Anthropic). By far the most U.S. compute usage depends on these two labs. Western users are starting to use the Chinese models more and more. This keeps prices so low that the frontier labs are losing money on their AI sales. Sophisticated users automatically route their AI workload to the cheapest feasible provider.

Their will always be a subset of AI usage in the West that requires the highest level of performance, or freedom from Chinese government spying or manipulation, that will be directed to a premium tier. But if that premium tier ends up being only, say, 20% of AI usage in 2028, there is a real question as to whether the financial bases of the data centers being built now can be sustained. There are huge bear and bull arguments on both sides here, which are tough to balance. I got only equivocal “it depends” answers from AI on this.

If the data centers don’t make expected profits, what then? It all depends on how they were financed. Most of the build-out to date has been the big 4 hyperscalers, Google, Amazon, Microsoft, and Meta spending their free cash flow from their other business lines. If it turns out they simply flushed that money down the toilet, no big deal. Just a trillion-dollar whoopsie. The CEOs will still get their bonuses, don’t worry.

But now as more debt financing enters in, the stakes get higher. Analysis seems to show that the debt loads that the big 4 hyperscalers have incurred is manageable – -their base cash flows are so huge that they can manage their own debt. But in the past year we have seen the emergence of monstrous “Special Purpose Vehicles” (SPVs) with a mixture of equity, debt, and guarantees, to finance practically all the upcoming trillion dollars of data centers. This pushes the financing of the balance sheets of the hyperscalers.

If those newer data centers flop, their equity investors will take a hit, leaving their creditors in the hole and in control. One really needs to analyze exactly who those equity and debt holders are for the SPVs.  If the creditors decide to recoup some of their investment by selling the data center for say 60 cents on the dollar, the most likely buyers would be…Google and Amazon. There is a school of thought that this (let the SPVs fail, scoop up their assets at discount) has been their plan all along. World domination in AI compute!  Just like they have achieved world domination in online search and video and shopping. Maybe.

The resulting slowdown in data center investing would likely throw the U.S. economy into slowdown or recession, considering that it’s estimated that fully half of our recent GDP growth has been from circularly-financed AI buildout. Chipmakers’ (Nvidia, Micron, AMD, etc.) profits depend on continued acceleration in AI build-out. If that build-out stalls, or even slows down, chipmaker profits will crater. Whether this risk is already priced into their share prices is debated.

Boilerplate disclaimer: Nothing here should be considered advice to buy or sell any security.

There’s no non-partisan way to say this

mdmakowskyLeave a comment

I try to avoid nakedly partisan output, especially in this forum. I give my self a little more leeway on social media, but still try to remain within reason. And over my entire liftetime, the intellectual capability of the president has always been a target for insult (sometimes horrifically so) by the opposition party. But we have arrived, at some point in the preceding months or years depending on your vantage, at the arrival of a sitting President who genuinely appears to be limited in his ability to self-regulate.

A sundowning old man, addicted to AI slop, surrounded by supplicants and apparatchiks, with control over the US nuclear arsenal.

— David Roberts (@volts.wtf) 2026-09-06T18:16:44.829Z

If you let the post play, it shows 7 posts over 10 minutes. The rapidity of posts is, I believe, relevant because it signals that it is unlikely to be the product of a staffer in charge of his social media. PR staff would, regardless of content, prefer that posts each have their own time at the top of the feed to generate attention. By firing off so many posts in rapid sequence, each post is buried by the subsequent. This is not professional PR.

As for the posts, it is precisely the kind of content that would generate a lot of interest from juveniles and those in cognitive decline. It’s the kind of slop, AI or otherwise, that gets a lot of attention from individuals with zero opportunity cost of time on Facebook. Read in conjunction with the all-caps posts of threats to ban trade with whole swaths of the globe and other similarly illegal executive actions, it’s just part of the evermounting evidence that the President is not in full control of his impulses. Putting aside what Thomas Schelling would say about fragility and faillings of madman theories of conflict, it’s brings up one of the most important reforms we can and should be pushing for in the next administration.

Age limits on representation.

Reliable, unbiased cognitive tests would be first-best, of course, but the problem there is a) failure is endogenous to the control of the branch administrering them, and b) extrication in the wake of a failure is the kind of stress test that our system does not seem up to at the moment. For all of it’s coarseness, counting days since the record of your birth is straightforward and relatively easy to enforce before being allowed on the ballot in the first place.

One of the realities of modern medicine is that our ability to preserve the body is likely to outpace our ability to preserve the mind at a level adequeate to the task of high representative office. At the same time, our abilitty to extricate and replace someone unfit to their duties in the public sector will likely always be limited relative to similar processes in the private market, where executive services will always be at will in any context where the executive is not themselves the owner.

From a public choice/political economy perspective, age limits will not be an easy legislative or constitutional feat. Representatives will always favor the status quo rules that helped them get their job and subsequently keep it. This is only made more complicated by the basic human optimism regarding our own current and future capacities. And perhaps the simplest obstruction is also the biggest: the reward for winning an election is the expected incumbent advantage in the next election. To limit the prospective future terms for any sitting official, in their current position or the one they aspire to, is to reduce the value of what they already earned, and nobody likes that.

In the face of all that, however, remains a simple truth. The citizens of the United States and the broader world are all made less safe by a sitting President whose self-control and capacity to understand complex contexts has waned well-beyond the threshold of reliable decision-making. That’s a cost big enough and bad enough, wrapped in all the risk aversion and loss-aversion who can possibly imagine, that it should at least have a chance to motivate real legislative action. At least a chance.

Anyway, happy Labor Day.

Temperature vs thinking level for LLM products

Joy Buchanan2 Comments

If using ChatGPT or an API in 2023, you might have followed the advice “turn the temperature down” when you wanted a more serious answer. That is going out of date. The following is a collaboration between me and Grok to lay this out simply:  

What temperature was

Imagine the model is always choosing the next word from a long list of options, ranked from “very likely” to “weird but possible.” Temperature changes how adventurous that choice is.

  • Low temperature: almost always pick the obvious next word. Answers feel steady, repetitive, “corporate.” Good when there is basically one right output, like extracting a date from a contract.
  • High temperature: more willing to pick a less obvious word. Answers feel livelier but sometimes incorrect.

On older products you could often set this yourself.

What thinking level is

Newer models (Gemini 3, OpenAI’s reasoning models) do extra work before they talk to you. They draft a private scratchpad: break the problem into steps, check themselves, then write the answer you see. You don’t see the scratchpad even though you pay for it. That hidden draft is the “thinking.”

Thinking level (sometimes called reasoning effort) is not “how random should the next word be?” It is “how long is the model allowed to work on that draft?”

  • Low / minimal: answer quickly and cheaply. Fine for “what’s the status of ticket 1842?”
  • High: spend more time (and more tokens) on hard things — a gnarly spreadsheet, a multi-step plan, a tricky piece of code.
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College Major & Income Sources

Zachary Bartsch4 Comments

We already know that economists earn more income on average. But when and how one earns income matters for how you spend your time both now and in the future. Being more productive affords the option to earn more money by working. For that matter, it also affords the option of staying home or pursuing passion projects at work or elsewhere.  Earning more money earlier in life also has implications for how you spend your time later in life.

Specifically, given the choice, you may choose to work less as a young adult so that you can spend more time with your family. The tradeoff isn’t just whether to work now or spend more quality time with others. After all, money can be saved for the future. Choosing to work less (or for a lower salary) today means that you may choose to work more in the future in order to achieve your desired standard of living. Personally, assuming I make it to old age, I would very much like to afford spending time with my family.

The more that you earn earlier in life, the more that you can save and invest for the future. The more that you save, the more that you can enjoy the fruits of compound interest. It’s not just a matter of earning more now rather than later. If you work and save now, then your future income can be passive. That is, your future earnings won’t require you to spend your time in an office or otherwise employed. You can still do that if you want, but you wouldn’t *need* to.  By having more retirement, investment, and social security income, your future self will earn plenty of income without spending as much time formally working.  You can instead spend time with loved ones or on other pursuits.

Below is the stacked bar graph of average income sources over each decadal age cohort. All data is from the 2024 ACS, so it’s just a snapshot in time rather than following individuals over the course of their life. I singled out people with Economics, Finance, and other 4-year college degrees. Economists make the most lifetime income if we count salary and other compensation alone. But if we look at the older cohorts, economics majors also earn more passive income. You’d think that Finance majors would earn more from investments. But among people in their 70s, economics majors earn more investment and retirement account income. Finance majors do earn more social security in that cohort, however.

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Ranking State Economies On a Governor Time Scale

James BaileyLeave a comment

Governors serve for 4 years in most US states. You can find many rankings of state economies out there, but they tend not to measure how states have done over the last 4 years- instead they either use measurements based on levels (things like mean income which were mostly determined by events before the current governor’s term), or one year of growth (which has a lot of randomness), or they don’t explain what period it is based on at all.

But if you want to know how a state’s economy has performed under an incumbent governor (for instance, to inform your vote on whether to re-elect them), the best way is to measure it’s economic growth over the period of their term- most commonly, 4 years. A governor currently up for re-election following their first term would typically have taken office in January 2023. Below I map how two of the most commonly used economic measured have fared by state from January 2023 to the most recent available data (what is available differs by measure):

Source: My calculations from BEA current-dollar GDP
Source: My calculations from BLS data on total employment

Overall South Carolina looks best and Wyoming looks worst. There might be other economic measures you prefer, like poverty rates or median income- but whatever measure you prefer and whatever politician you are evaluating, I encourage you to check how that measure has changed since their term started and how that ranks compared to other similar regions.

This post was inspired by the mailers that would-be Rhode Island Governor Foulkes’ campaign keeps sending me suggesting I vote for her in the primary against incumbent Rhode Island Governor Dan McKee because “we are last in the country for our economy”, citing this CNBC ranking. Looking into the source, CNBC says:

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Median Family Income for Married Couples With Children Is Probably Higher Than You Think

Jeremy HorpedahlLeave a comment

In 2024, median income for married couples with children at home was $143,400 in the US. That’s an almost 80 percent real (inflation-adjusted) increase since 1974, the first year Census reports comparable data. Is there some selection bias in who chooses to get married and have kids? Yes. Has there been an increase in dual-income families? Yes, but probably much less than you think (the median family of this type already had two earners by the late 1970s).

With those caveats, this is still pretty impressive: