Disability: Income, Unemployment, & Education

Let’s talk about disability. The American Community Survey (ACS) identifies seven broad categories, not including the baseline case of ‘No Disability’. I’ve written before about how disability designations are difficult because many qualifying conditions are a matter of degree. Surely you’ve met a person who is legally blind but can see *some*. How much sight ability can remain while still being considered blind? If a person can identify the sun’s location, but nothing else, then is that person still blind? Similarly, who’s to say whether some physical or cognitive characteristic is disabling? Why not enabling – depending on the task?

Wisely, the ACS side-steps this entire discussion and simply asks respondents whether they have “difficulty”.  We can be agnostic about the cause and degree while still trying to get at the heart of the matter. One downside is that people tend to have more difficulty as they age. Since that’s not relevant for earlier life economic outcomes, I only examine ages 24-45. Difficulty with “self care” is too small of a category in the 2024 ACS for the analysis here. Below are the other six difficulties identified:

  • Hearing Difficulty (n=6,609)
  • Seeing Difficulty (n=8,307)
  • Cognitive Difficulty (n=26,792)
  • Ambulatory Difficulty (physically getting around) (n=4,568)
  • Independent Living Difficulty (n=5,997)
  • No Disability (n=885,302)

We should say early on that many of the people who participate in “Deaf” (capitalized) culture do not consider deafness to be a disability and that any such suggestion is motivated by bias and hearing culture. I won’t engage with that here. But I can say that there is a long tradition going back through the 1800s that rejected alms-giving for deafness and instead supported independent living for deaf people. Historically, this manifested as outlawing panhandling while featuring deafness and rejecting government programs that provide simple transfers due merely to deafness. More recently, the push to reject the ‘disability’ label and its baggage has even been the motivation for some deaf people to oppose cochlear implants being covered by medical insurance. For ease, I’ll be using the colloquial terms for all of the above difficulties. Now that you have the greater context, let’s jump into the data (unweighted).

The baseline income chart is above. Given the above description, it should come as no surprise that deaf people have the highest average income among disability groups, conditional on employment. However, it’s also surprising that most of the above disabilities have incomes that I wouldn’t exactly call destitute. Many people who have a disability look much like everyone else. From an economic point of view, we all have a comparative advantage and it’s perfectly consistent with gainful employment and flourishing otherwise.

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A Simple Way States Can Reduce Healthcare Spending

Repeal Certificate of Need requirements, assuming you are one of the ~35 states that have them.

That’s what I find in a paper with Shishir Shakya just published in Health Economics.

Certificate of Need laws require healthcare providers to obtain government approval before building new facilities or expanding services. The original intent of the laws was that less building would mean less spending, but critics worry that it means less competition and higher prices leading to higher spending. Our paper finds that the critics are right, as states repealing their Certificate of Need laws see their healthcare spending fall relative to those that maintain them.

Longtime readers may recall that back in 2022 I posted about another paper of mine with a similar result, and even that wasn’t the first word on the subject. So what’s new in this paper? Besides newer data, we measure health spending as a percentage of the state economy rather than in raw dollars, to try to account for how CON changes the true burden of healthcare spending. We also use newer regression techniques that are probably more accurate but definitely help make prettier graphs.

Here’s what we found happening to health spending when states repeal Certificate of Need:

Full published paper is here, ungated working paper version here.

Has Mexico’s “Canasta Básica” Policy Made Food More Affordable?

A recent article in The Guardian profiles economist Isabella Weber. She doesn’t seem to like other economists very much, supposedly because we “obsess over ideological purity and are inflexible when searching for solutions.” Weber wants economists to come up with solutions that work even if… I guess, even if they don’t work well in theory? Maybe they will work well in practice?

Anyway, so far as I can tell, the article only gets into details on two concrete solutions to affordability. I’m sure she has more in her book, but let’s see what these are. The first for governments to create “publicly owned entities that can buy and sell grain into its market,” presumably to smooth out fluctuations in grain prices. The second is “the Mexican government’s canasta básica policy, through which retailers voluntarily cap the total price of a basket of two-dozen essential goods.”

Let’s look at the second one, since I think for developed countries like the US, fluctuating grain prices aren’t the primary source of affordability concerns. But perhaps American consumers would like a basket of grocery essentials to be cheaper.

Has this policy in Mexico worked? It was first implemented in 2022, and food inflation has certainly fallen in Mexico since then. But it has also fallen everywhere else in the world — 2022 was peak inflation not just in the US, but in most countries — so we can’t judge the success of the program based on that alone.

Instead, we can look at internationally comparable data on food spending, which comes from the USDA’s Economic Research Service. Our World in Data has a nice chart based on this data, and even though it is now two years out of date (I will provide 2025 data below), the 2023 data makes a case against the success of the “price controls” in Mexico (I put “price controls” in quotes because it is not a true price control, but an attempt to control prices via incentives):

Figure 1

It will probably not surprise you that the US spends much less on food as a percent of total spending than Mexico, given that we are a high-income country. But it is still worth pointing out just how low food spending is in the US. However, this chart still paints Mexico in a very poor light: for countries with about the same income level in the Americas (as measured by consumer expenditures), Mexicans spend much more on food than Argentina, Chile, or Panama.

If we use the 2025 USDA data, and focus on the 10 countries with consumption expenditure closest to Mexico (between about $8,000 and $12,000 of consumption), we see that Mexico is very much on the high end: only Argentina was clearly higher:

Table 1

Finally, we can compare Mexico against itself in this data, looking at food spending going back to 2017:

Figure 2

Food spending actually peaked in 2020, though be cautious of any data from 2020 as the pandemic made data collection difficult. After declining in 2021 and then into 2022 (the policy seems to have been put in place in mid-2022), there haven’t been any further declines. And food spending is still well above pre-pandemic levels (unlike the US, where the 2025 figure of 6.4% is the same as 2017-18).

Ultimately, we would want a more sophisticated analysis than my simple tables and charts here. But it doesn’t appear that Mexico’s 2022 policy really made food much more affordable, either relative to peer countries with similar income levels, or relative to Mexico’s own recent history. And that’s even before we get into any unintended consequences of the “price controls.” Of course, the policy may have made that basic basket of two-dozen items more affordable than it otherwise might be — for that we would need to compare individual item prices to peer countries — but it hasn’t obviously made food overall more affordable in Mexico.

Paper Published on Persistent LLM Hallucinations

I am pleased to announce the publication of

“LLM Hallucination of Citations in Economics Persists With Web-Enabled Models”

By Joy Buchanan, Stephen Hill, and Olga Shapoval

ABSTRACT: Despite significant advances since 2023 in large language model (LLM) capabilities and the introduction of web-enabled search functionality, citation hallucination remains a persistent problem in AI-generated academic content. We test GPT-4o, DeepSeek V3, web-enabled GPT-4o, and web-enabled GPT-5.1 across prompts derived from Journal of Economic Literature categories. Using a hierarchical citation validation framework, we find that even an advanced web-enabled model produces non-existent citations. While web-enabled models show improvement over their predecessors, the error rates remain high for scholarly applications. Our findings suggest that the fundamental challenge of citation hallucination persists despite technological advances, with implications for academic integrity and the reliable use of AI in research contexts. We provide an online tool for researchers to run LLMs through the benchmark test using our prompts.

Suggested citation:

Buchanan, J., Hill, S. and Shapoval, O. (2026), LLM Hallucination of Citations in Economics Persists With Web-Enabled Models. Southern Economic J. https://doi.org/10.1002/soej.70069

After some time went by since we published our 2024 paper on this topic and GPT became web-enabled, we thought that this silly problem with hallucinating up fake citations might have gone away. So, we went back to do our count again. We found that a significant number of references are still made up.

I believe that this particular problem of fake citations will go away soon. However, I think the starkness of fake references illustrates something more general about the LLM technology and highlights the importance of verification. Feel free to cite our 2026 paper as evidence that hallucinations are still happening, despite improvements along many margins. Since we started down this track, OpenAI released a paper titled “Why language models hallucinate” at https://openai.com/index/why-language-models-hallucinate/ Excerpt:

Hallucinations persist partly because current evaluation methods set the wrong incentives. While evaluations themselves do not directly cause hallucinations, most evaluations measure model performance in a way that encourages guessing rather than honesty about uncertainty.

You can access our working paper of “LLM Hallucination of Citations in Economics Persists with Web-Enabled Models” on SSRN at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5318314

We are not in the slow-lane “LLMs are stochastic parrots” group. Stephen Hill does even more with AI in his personal workflow than I do. AI will probably soon qualify for all of the academic prizes we used to award to human writers and scientists. However, as we hand more responsibility over to AI, keep the issue of hallucinations in mind.

Marriages By College Major

[Edited to include divorces]

The American Community Survey (ACS) collects information on how many times people have been married.  Using the 2024 ACS, we can see how that changes across ages. Getting married is what we call an absorbing state. Once you get married once, you’ll never be married zero times again. So long as people get married across the age distribution, the proportion of never married people falls.

The same is true for getting married a 2nd time. Then, you’ll never have been married once ever again. While the proportion of people never married must shrink with age, the proportion of people married once can rise. If a few people get divorced or married a 2nd time, but an even bigger number of people get marred for the first time, then the group of people married only once can grow. The ACS stops measuring once someone has been married three times – then it’s “three or more” times. A subsequent divorce the universal absorbing state. Those people never go into another category.

The chart shows the proportion of people by age and the number of times that they’ve been married. Darker hues reflect divorces.* Importantly, this is the snapshot in 2024. So each age group is a cohort snapshot in time. That’s why the proportion of people in their 80s who’ve been married zero or one time is greater than people in their 70s. They’re a different cohort. When the now 80-year-olds were in their 70s, the sum of the blue and light yellow bars were larger than for more recent 70-year-olds.

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Health Insurance Benefit Mandates and Premiums

The Center for Modern Health just released a report “Paring Down Coverage Mandates To Reduce Health Insurance Premiums“. It builds on one of my papers to estimate how much states could drive down insurance premiums by reducing the number of things health insurers are required to cover:

Estimated savings for Family Plans (Table 2 of CMH Report)

It’s nice to see that someone in the health policy world remembers that health insurance benefit mandates exist and can be a significant cost driver. I’m also glad to see that someone still cares about my 2014 paper (the first one I ever got accepted at a journal), though I wish there were more up to date estimates available for them to cite.

Why has no one developed more recent estimates? Partly for data reasons- the organizations that had been putting out counts of mandates by state each year (the Council for Affordable Health Insurance & Blue Cross Blue Shield Association) stopped doing so. Partly because of the ACA, which changed how mandates worked in complex ways.

That said, there there seems to be some more recent data from 2017 that could be used for a slight update, and this post and my ideas page detail the many things we still don’t know about how mandates operated prior to 2014:

State Health Insurance Mandates: Most of my early work was on these laws, but many questions remain unanswered. States have passed over a hundred different types of mandated benefits, but the vast majority have zero papers focused on them. Many likely effects of the laws have also never been studied for any mandate or combination of mandates. Do they actually reduce uncompensated hospital care, as Summers (1989) predicts? Do mandates cause higher deductibles and copays, less coverage of non-mandated care, or narrower networks? How do mandates affect the income and employment of relevant providers? Can mandates be used as an instrument to determine the effectiveness of a treatment? How does all this change in light of the ACA? On the identification side, redoing older papers using a dataset like MEPS-IC where self-insured firms can be used as a control would be a major advance.

Wealth of Generations in 2025

Two weeks ago I provided an update on estimates of wealth for recent generations in the US. But just this week, the Federal Reserve gave us the once-every-three-years complete update to this data, which allows us to calculate medians rather than average, and to more precisely separate age groups.

I will have a lot more to say about this data, both on this blog and a book I am working on, but for now here is the chart you have all been waiting for:

Is AI Taking Our Jobs? Signal Boosting Noah Smith

Artificial intelligence is supposed to transform the labor market. Depending on whom you ask, AI is either about to eliminate millions of jobs or usher in a new era of productivity and prosperity. I am reviewing the evidence before I teach an “unemployment” chapter…

I want to give a signal boost to economist Noah Smith, who has been following the emerging evidence about AI and employment at his blog, Noahpinion. His answer, so far, is that the evidence for widespread displacement is weak.

In his September 7 post, “AI keeps stubbornly refusing to take our jobs,” Smith points out a contrast. Many technology executives expect AI to make human workers obsolete, and public opinion surveys from Pew Research Center indicate that Americans share these concerns. Yet employment has held up remarkably well, especially among prime-age workers. AI might still take our jobs soon, but we have not seen widespread displacement yet. Economists have been saying for a long time that automating tasks is not the same thing as eliminating jobs.

In “Automation and New Tasks: How Technology Displaces and Reinstates Labor,” Daron Acemoglu and Pascual Restrepo explain that automation can reduce demand for labor performing particular tasks while also increasing productivity and creating new tasks for workers.

Consider software developers. AI can now write substantial amounts of code. One might conclude that fewer programmers will be needed. But programmers also design systems and decide what software should do. Making code less expensive could expand the amount of software that firms want to produce. Greater productivity may increase rather than decrease the demand for skilled programmers. (my old paper on who wants these jobs is “Willingness to be Paid: Who Trains for Tech Jobs?“)

Smith points readers to Guy Berger’s analysis of software developer employment and reporting on firms that have reconsidered replacing programmers with AI. These examples challenge the assumption that better coding tools necessarily mean fewer coding jobs.

One piece of evidence comes from the U.S. Census Bureau’s Business Trends and Outlook Survey, highlighted by Alex Tabarrok at Marginal Revolution and discussed by Smith. Among firms using AI, 44 percent reported that it supplemented or enhanced existing employee tasks. Ten percent reported that AI performed tasks previously done by employees, while 11 percent reported that AI introduced tasks nobody had previously performed. Most firms reported no employment change attributable to AI.

Smith also highlights research by Ara Kharazian, Jonathon Simon, and David Stevens who find that firms adopting AI tend to expand their employment, including entry-level employment. These findings contrast with the popular narrative that businesses are replacing junior employees with chatbots.

What about young workers?

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Degrees, Higher Degrees, & Higher Income

I’ve discussed multiple times now how economics degrees are associated with higher income. One reason may be that it’s not the bachelor’s degree that’s doing the legwork, but rather the graduate degree. I examine that hypothesis here for the 25 most popular degrees in the 2024 American Community Survey.

The data identifies who has a bachelor’s degree and in what field. We also know whether an individual has a master’s or professional degree, or a doctorate. Unfortunately, we don’t know the field for the higher degrees. So, while we can say that 45% of Economics majors have a graduate degree, we can’t say that the graduate degree is in Economics. After all, plenty of people major in one thing and pursue a graduate degree in another thing.

Degree Types by College Major

Below is a chart that ranks the 25 most popular college majors and whether those graduates also hold a graduate degree. There are several patterns. First, the tier near the top of the chart shows more business majors tend to be terminal. Students majoring in communications, business, and marketing tend not to proceed to get higher degrees.

That contrasts starkly with the bottom tier of the graph where majors like Chemistry, Biology, Political Science, Math, and History all tend to have high rates of graduate degrees.  I’m not sure why this is. Maybe it’s because the skills are more research-oriented in nature and prepare those students well for graduate school. Other alternatives may be that the bachelor’s degree alone doesn’t provide much income or that a graduate degree results in a large proportional increase in income for those majors.

Economics majors are on the lower end in terms of the proportion with only a bachelor’s degree (55.5%), near the middle of the pack for proportion with a master’s or professional degree (39.7%), and have an average proportion holding doctorates (5.1%).

Because the numbers make it easy, I’ll pick on education majors a little. Given the high proportion of education and elementary education majors who have graduate degrees, there are relatively few of them who have doctorates. Other majors that have a lot of graduate degree holders tend to have more doctorates, but education majors in higher education are overwhelmingly people with master’s degrees. Given that many of them work for governments, I suspect that they often get procedural pay raises merely for holding a higher degree.

Income by Degree Type and College Major

Below is a chart that shows the average income among employed people by college major and higher degree. The highest-paying terminal bachelor’s and master’s degrees are both among Economics majors ($144k & $203k). A previous post showed that Economics majors had the highest overall income and here we can see that it comes primarily from the bachelor’s and master’s degree holders, while economics majors with doctorates earn the 3rd highest income ($208k) behind computer science ($247k) and electrical engineering ($238k).

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Economic Freedom Is Enough

Adam Smith kicked off modern economics by asking what causes nations to become wealthy. We’ve spent centuries improving our answers to the question and we still have lots to learn, but I think Smith’s original answer holds up remarkably well:

Little else is requisite to carry a state to the highest degree of opulence from the lowest barbarism, but peace, easy taxes, and a tolerable administration of justice; all the rest being brought about by the natural course of things. All governments which thwart this natural course, which force things into another channel, or which endeavour to arrest the progress of society at a particular point, are unnatural, and to support themselves are obliged to be oppressive and tyrannical

I was reminded of this by the latest Economic Freedom of the World report just released:

The overall correlation of GDP per capital and economic freedom is strong, but I find the extremes even more striking. Economic freedom is necessary for wealth: every country with a GDP per capita above $50,000 has above-average economic freedom (getting even close without high economic freedom requires having lots of oil). Economic freedom is sufficient: every country with an economic freedom rating over 8 has a GDP per capita over $50,000.

I periodically see someone claim that a country “did everything right” in terms of policy but still wound up poor, usually implying that the country is doomed by their geography or genes, or arguing that “neoliberal policy” failed. Whenever I check in on these countries, I inevitably find them to be far from “doing everything right”, with economic freedom scores typically far below 8.

It’s true that these are “just correlations” and that economic freedom alone isn’t a perfect predictor. It’s worth looking into the outliers and wondering- if a country is above the trend line without oil wealth (like China), what else are they doing right? If a country is well below the overall trend line (like Guatemala), does it mean they are have something else working against them to counteract the benefits of economic freedom? Or does it mean they are about to turn the corner and see high economic growth?

Overall though I don’t think it’s a big oversimplification to say that Adam Smith was right, and economic freedom is enough to bring prosperity. If you’re looking for economic growth, start with Smith’s peace, easy taxes, and tolerable administration of justice, or the Economic Freedom of the World’s small government, strong property rights and legal system, sound money, and freedom to trade at home and abroad.