Originally Ìýon Agglomerations, the Substack newsletter from the Economic Innovation Group.

By Adam Ozimek, Jason Harrison, and Nathan Goldschlag

To hear an extended conversation about the themes in this post, please subscribe and listen to our podcast,ÌýThe New Bazaar.

The China Shock refers to the widespread job loss that certain workers and communities suffered because of the rapid rise of trade with China in the 2000s.

The AI Shock refers to the same thing happening to white collar workers from the rise of Artificial Intelligence.

For now, the AI Shock is merely aÌýpotentialÌýshock. But aÌýÌý´Ç´ÚÌý,Ìý, and otherÌýÌý³ó²¹±¹±ðÌýÌýworrying parallels between these two shocks.

We understand why. Across aÌýÌýÌý´Ç´ÚÌý, researchers have found that the China Shock had big and enduring impacts on the local economies and workers who were most exposed to it.

Even more concerning, the extent of the China Shock’s damage was a surprise to economists and other experts. It had long been known that expanding trade will produce winners and losers, but economists tended to think that jobless workers would adjust by moving to the better job opportunities created in a growing economy. Outmigration and flexible wages would restore balance to local labor markets, while many factories would be repurposed to make different products or provide services.

For too many communities, however, especially certain manufacturing towns in the Midwest and South, those adjustments never came. Jobs were lost and not replaced. Jobless people who stayed in their towns simply stopped looking for work. Health, crime, and family formation deteriorated.^[1]

Will AI do to laptop workers what the China Shock did to so many factory workers? Hoping to avoid the same mistakes of the past, researchers and policymakers will be tempted to say yes.

But after a closer look at the details of the China Shock, combined with our understanding of workers exposed to AI, we arrive at a different conclusion.

The real lessons of the China Shock emerge not from its similarities with the AI Shock, but from its differences. And these lessons should mitigate concerns about a massive upcoming disruption, not exacerbate them.

But to understand how we discovered these lessons, we first need to explain how we compared the experiences of China Shocked workers to the potential outcomes for AI Shocked workers. We begin with our measures for each set of workers.

China Shock Exposure

Here is how we know.

AÌýÌýpublished in 2021 by David Autor and his co-authors, building upon earlier work, produced a measure of trade exposure by industry — specifically, the increase in Chinese imports as a share of each industry’s output.

We largely follow this approach. But to arrive at a measure of China Shock exposure forÌýworkers, we did two things. First, to measure industry exposure, we chose the time period of 1991 to 2007, a window that captures both the 1990s rise in Chinese import competition and the acceleration after China’s 2001 WTO accession. Second, we adapted the Autor measure — again, the rise of Chinese imports within each industry — by assigning exposure to individual workers based on the industries they worked in.

Our findings:

• For 82 percent of American workers — including everyone outside of manufacturing — there is no measured exposure. They worked in industries in which there was no rise in Chinese imports as a share of their industries’ output between 1991 and 2007.

• Another 14 percent of American workers faced positive but still modest exposure. The rise in Chinese imports was less than 15 percentage points as a share of their industries’ output.

• In contrast, roughly 5 percent of workers were in industries where the growth in Chinese imports was quite large — 15 percentage points or more — relative to output in their respective industries. For example, Chinese imports climbed by 15 points in electrical machinery and tires, 54 points in computers and related equipment, 70 points in toys and sporting goods, and 77 points in footwear. All are manufacturing industries.

It is this last category — the 5 percent most exposed workers — that we use as our comparison group in the rest of this analysis.

In other words, when we compare workers exposed to the China Shock to workers exposed to the AI Shock, we are specifically referring to the 5 percent of American workers who were most exposed to Chinese import competition between 1991 and 2007.

This group numbers about 6 million workers. They all worked in manufacturing, representing about a third of all workers in manufacturing industries. We explore their other characteristics in the sections to come.^[2]

AI Shock Exposure

There is no directly comparable dollar based exposure measure for AI.

Instead, we use theÌýÌýin a 2024 paper by Tyna Eloundou and co-authors to identifying the set of workers, based on their occupations, that are most exposed to AI.

Eloundou and co-authors begin by measuring whether the use of AI, potentially with additional specialized software, could reduce the time to complete a task by at least half. To do this they prompt both expert human reviewers and an early version of OpenAI’s ChatGPT-4 to classify tasks based on this time reduction rubric, finding a high degree of agreement between them. For our analysis, we will refer to such tasks as those that can be “done by AI.�

Eloundou and co-authors then aggregate from the task level to the occupation level, computing the share of tasks that can be done by AI.

What is useful about this measure is that it focuses on whatÌýcould be automated, not whatÌýhas been automatedÌýtoday. Because the AI shock is mostly yet to come, a forward looking exposure measure is more appropriate.

Another complicating factor is that it is not yet possible to know how much task exposure will lead to actual job loss. For the China Shock, empirical estimates show that the 5 percent most exposed workers were at serious risk of job loss. For the AI shock, however, there is no evidence about the share of a job’s tasks being done by AI that would constitute such risk.

Even if a very high share of an occupation’s tasks can be automated by AI, the occupation itself may survive as a new and different combination of tasks.^[3] As there is no obvious cutoff for which workers will face displacement (job loss) pressures, we consider multiple cutoffs for the pool of workers that are AI exposed. Table 1 below shows the percentage of the workforce employed in occupations at each respective amount of task exposure to AI.

Our narrowest definition of high exposure assumes that the AI shock only affects the workers at the most extreme end, where AI can replace 90 percent of their tasks. These workers represent less than 1 percent of the workforce.

Our broadest definition assumes the AI shock disrupts a much wider swathe of jobs, including everyone where at least half of their tasks could be done by AI, representing closer to one out of every four workers.

But as our analysis will illustrate, the results of our comparison between China Shocked workers and AI Shocked workers are consistent regardless of where we draw the line.

AI Shocked Workers: More educated, better paid

With highly exposed groups for our two shocks defined, we can explore how they are similar and how they differ. Two differences are especially notable.

1. Compared to China Shocked workers, AI shocked workers are far more educated.

Figure 2 shows that the majority of China Shocked workers have an education level of a high school degree or less, while only 6 percent have an advanced degree.

Using the broadest sample of AI Shocked workers — at least half of their tasks are exposed to AI — the majority of them have a bachelor’s degree or higher. Workers in this group are more likely to have an advanced degree (18 percent) than just a high school diploma or less (17 percent).

And the more concentrated the AI shock, the more educated the group becomes. Workers in the most exposed AI Shock group, representing less than 1 percent of the workforce, are three times more likely to have an advanced degree than to have a high school diploma or less.

In short, regardless of how broad or narrow we define the AI shock, it will affect a group of workers who are substantially more educated than workers who were most exposed to the China Shock. As AI exposure rises, so does educational attainment.

One of the reasons for the disparity is simply that average education rates increase over time, with the China Shock reflecting an early 1990s workforce. Between the early 1990s and early 2020s, the share of workers with a high school degree or less fell from about 48 percent to just over 30 percent. But even if we redo the analysis comparing shocked workers in the same period, the educational gaps persist.^[4]

2. AI Shocked workers also have higher earnings than China Shocked workers.

Regardless of which AI Shock exposure group we use, its share of the top third of all earners is much higher than the China Shock share, while the reverse is true for the respective shares of the bottom third of earners, as shown in Figure 3.

Only about a third of China Shocked workers were in the top tercile (top 33 percent) of overall earners during the time of that shock, whereas 43 percent of the broadest group of AI exposed workers made the top tercile. Meanwhile, when looking at the most exposed group of AI Shocked workers, nearly two thirds of them were in the top tercile.

In contrast to the heavy concentration of AI Shocked workers among the top tercile of earners, China Shocked workers were fairly equally distributed across the wage spectrum, with around a third of workers in each tercile.

Not all workers are equally vulnerable

One piece of evidence supporting this view is that the least educated are more likely to be unemployed. Census data going back over 80 years shows that unemployment for the least educated third of workers is always above, and usually at least double, that of the most educated third.

Another piece of evidence is that even among those workers who lost jobs from the China Shock, not all of them suffered equally.

Difficult adjustments and longlasting effectsÌý among workers with lower initial wages.^[5]ÌýLow wage manufacturing workers in highly exposed industries lost about 1.2 years of initial earnings over the following 16 years relative to comparable workers in less exposed industries. But middle wage workers lost much less, and the estimated effect for the highest paid third of workers was essentially zero.

High wage workers were able to leave exposed firms and industries without large earnings losses. Lower wage workers, on the other hand, were more likely to remain at their initial employer until the firm underwent mass layoffs, pushing those workers into worse jobs or forcing them to exit the labor force entirely.

The proof that more educated and higher paid workers adapt better to shocks is not just limited to the China Shock research. A similar pattern occurred during the Great Recession. Workers with more education wereÌýÌýto lose their jobs initially, and when they got a new one it was less likely to be part time or to pay lower earnings. Looking at the longrun effects of local labor market shocks from the Great Recession, Danny YaganÌýÌýthat the workers with the highest initial earnings were least likely to be jobless in 2015, long after the recession had officially ended.

The relationship between education and adaptability to local shocks has also been found in theÌý.

One reason that better educated, higher paid workers are more able to adapt is they are moreÌý. Another reason is that their skills are moreÌý.

Whatever the cause, the evidence on a wide range of economic shocks illustrates that when job loss is concentrated among lower wage and less educated workers, it is likely to generate different adjustment problems than a shock concentrated among workers with higher pay and more college degrees. We consider this an important reason to worry less about the AI shock mirroring the China Shock.

Where the shocks are

The disruptiveness of an economic shock is determined not just by which people get hit but also which places. Research has shown that one reason the China Shock was so disruptive was that it was geographicallyÌý.

Local labor markets adapt to small losses of employment all the time. Even outside of recessions, roughly 30 million jobs across the United States areÌýÌýdue to layoffs or business closures. But the national unemployment rate stays low as workers reallocate from shrinking sectors or firms to growing ones. This activity is simply part of the everyday churn of the economy.

When job losses are large and concentrated in a few specific places, in contrast, it becomes much more difficult for those local economies to adapt. Rather than Schumpterian creative destruction, the job loss is large relative to the local economy. The reallocation will take longer, and the struggle to adapt also can generate demand shortfalls as local spending pulls back and spills over into other sectors. Nationally set monetary policy will do little to stabilize local economies suffering from demand problems that arise from concentrated shocks.

If the few million jobs lost from the China Shock had been scattered evenly throughout the country, they would have represented just a small fraction of normal economic churn, and thus workers would have bounced back more easily. Instead, the shock was heavily concentrated in manufacturing towns in the Rust Belt and South.

The good news is, the evidence we have suggests that the AI shock will be far less geographically concentrated. Figures 5 and 6 below show the respective place level exposures to the China Shock and the AI shock across Commuting Zones (CZs). China Shock exposure is concentrated in a much narrower set of manufacturing heavy labor markets. AI exposure, in contrast, is more evenly dispersed throughout the country.

The difference is also obvious when looking at relative exposure in the hardest hit places, as shown in Figure 7. The 10 Commuting Zones most exposed to the China Shock average about 5.8 times the exposure of the average one; the next 40 about 3.2 times the exposure of the average one; and the next 50 about 2.1 times.

AI exposure is much flatter, with the top 10 CZs only about 1.2 times the average CZ.

Which types of places

How a place adapts to a shock also depends on the characteristics of the place.

Ìýthat local economies whose workers have higher average levels of education adapt much more easily to economic shocks than places with lesser educated workers. The reasons for their resilience go beyond the fact that higher educated workers themselves are more resilient. Places with more human capital tend to have faster rising populations, which helps to boost dynamism and the growth of underlying demand growth. A more educated population will also be more innovative and have more entrepreneurs who identify next best uses for displaced capital and labor.

When we compare China Shocked places to AI shocked places, it is clear that education levels are a major differentiator. Figure 8 below looks again at exposure ranked Commuting Zones, but it compares the average share of workers with a high school degree or less against the average share of those workers for all Commuting Zones in the relevant year.^[6]Ìý(We use 1990 education data for the China Shock and 2024 education data for the AI Shock.)

The most China Shocked places had populations with lower than average education levels at the time, while the opposite is true for the most AI exposed.

Looking at the share of the population with bachelor’s degrees or higher, we see a complementary pattern. China Shocked places are slightly below average, while AI shocked places are way above average.

Missing Something?

We have presented the evidence for why the China Shock was far worse than the AI Shock is likely to be. What might the opposite case look like? We’re happy to play devil’s advocate, both because it’s a good intellectual habit and also because the case ends up looking so much weaker than ours.

One possibility is that the AI shock materializes much faster than the China Shock. If AI agents prove to be as capable as their creators suggest, displacing a worker whose primary tasks occur on a laptop could be as simple as installing and initializing the AI agent.

A second possibility is that the AI Shock ends up displacing a much bigger share of workers than we expect. In our most aggressive AI Shock scenario, 27 percent of workers would be exposed, 5.7 times larger than the 5 percent exposed to the China Shock. If most or all of these exposed workers lose their jobs — especially if, coinciding with the first possibility, they lose their jobs quickly — an utterly massive share of the labor force will find itself suddenly unemployed. The job losses and subsequent demand destruction could be enormous.

That’s the worst case scenario: AI hits faster and hits bigger than even pessimistic forecasts. We find this outcome unlikely, for two reasons.

First, rolling out AI at such scale requires significant computing hardware, which takes time to build. Data center construction is a real constraint and is already causing policy blowback. The prices of information processing equipment haveÌý, rising at the fastest pace since 1959. TheÌýcurrentÌýAI buildout is already facing big obstacles. Imagine how much bigger they would be for the kind of buildout required to replace millions of workers.

Second, if the AI Shock were to produce vast job losses, the magnitude of the effect itself makes economic policy more powerful in offsetting the effects on demand. Recall that monetary policy, for instance, was of limited use for helping local economies destroyed by the concentrated China Shock. If the AI Shock proves to be bigger and more widespread, monetary policy regains in potency.

What is more, significant losses of jobs because of AI would be accompanied by rapid economic growth. Both economic growth and AI augmentation are helpful for smoothing the economic adjustment in a variety of ways, including boosting the demand for other types of workers.

Nevertheless, we admit that an AI Shock large enough to upend the labor market overnight would be unprecedented, which means that reasoning about it involves uncertainty in both positive and negative directions — about adjustment costs but also benefits, layoffs but also progress, unemployment but also innovation. The economy would be entering uncharted territory.

In such a world, comparisons to the China Shock, or to any other great historical shock, would therefore be pointless.

A tale of two shocks

In sum, the China Shock was disruptive because it 1) disproportionately hurt workers with less education, 2) was geographically concentrated, and 3) inflicted its worst damage on places with lower levels of human capital.

It is still early days for AI and so a large dose of humility is appropriate. But so far as we can tell, the AI Shock is aimed at the kinds of people and places that actually weathered the China Shock just fine.

The old cliché is that history does not repeat itself but does often rhyme. If the AI Shock turns out to be as bad as the China Shock despite such different underlying characteristics, it would turn the cliché on its head: an instance in which history did repeat itself but skipped the rhyming. It’s not impossible, but we find this outcome unlikely.

Appendix

In the preceding analysis, we compare the education levels of the workers most exposed to the China Shock to those who are most exposed to the AI shock. One complicating factor is that these two shocks are occurring at different points in time, and education levels among workers overall climbed.

Absolute increases in education over time are relevant for resilience, but we can also compare relative education rates by comparing each group of exposed workers to the average education rates of the overall workforceÌýfrom the same time period. In other words, we can compare the China Shocked workers to the average education of workers between 1991 and 1993. And we can compare the education of AI Shocked workers to average education levels of all workers between 2021 and 2022.

The results are directionally the same: Workers exposed to the China Shock are still less educated, while the Al exposed groups are more educated.

Specifically, compared to the overall 1991 to 1993 workforce, workers exposed to the China Shock are 10.4 percentage points more likely to be high school graduates or less and 5.9 percentage points less likely to have a bachelor’s degree or higher.

In contrast, the most AI Shock exposed groups are substantially more educated than the 2021 to 2022 workforce. Depending on the exposure group, they are 13.7 to 24.1 percentage points less likely to have a high school diploma or less, and 13.1 to 29.9 percentage points more likely to have bachelor’s degrees or higher.

Taking the z scores for each group shows consistent results, with China Shock exposed workers below their period average and every exposure level of AI shock above its period average.

Originally Ìýon Agglomerations, the Substack newsletter from the Economic Innovation Group.

By Sarah Eckhardt and Nathan Goldschlag

A recentÌýÌýfound that 42 percent of bachelor's degree students have reconsidered their degree choice because of Artificial Intelligence. Another 16 percent say they haveÌýalreadyÌýchanged their field of study due to AI.

Students consider current labor market conditions when choosing a degree, so it isn't too surprising that AI could shape students' decisions today.^[1]

But the study does not ask students exactly how AI has shaped their choices, only whether it has. Are students shying away from fields that have more exposure to AI, perhaps worried that AI will shrink the number of jobs available to them? Or are students shifting towards those fields, preparing for a future in which they will have to be comfortable using AI?

To find out, we can check enrollment for groups of degrees based on the AI exposure of the jobs that students with those degrees are likely to take, as shown in Figure 1.

As is clear, undergraduates are flocking towardsÌýthe most-AI-exposed degrees, with enrollment in those degrees up 8 percent last year compared to 2017. This trend holds despite aÌýÌýin Computer Science degrees, one of the most-AI-exposed degrees, but whose decline is more than offset by increases in other exposed degrees like Engineering.

(A quick primer on our methodology before we move on: To estimate AI exposure, the general approach taken by researchers is to look at the tasks that workers perform in a given occupation and count how many of those tasks AI is likely to be good at. The more tasks associated with a given occupation that AI can perform, the higher the AI exposure of a worker in that occupation. AI exposure measures have importantÌý, most notably that exposure can mean either augmenting tasks or automating tasks. We estimate the AI exposure of degrees chosen by college students by using the typical occupations that graduates of each degree end up choosing.^[2] See the Appendix for more details.)

Why are students still pursuing AI-exposed degrees? Part of the answer is shown in Figure 2: Wages are highest for those with the most-AI-exposed degrees.

In addition to our earlier workÌýshowing that AI-exposed occupations have higher incomes, this finding is also consistent withÌýÌýfrom Morgan R. Frank, Alireza Javadian Sabet, Lisa Simon, Sarah H. Bana, and Renzhe Yu. They used data from LinkedIn profiles, combined with degree-level AI-exposure inferred from 3 million higher education course syllabi, and found that after the launch of ChatGPT, highly-exposed students enjoyed higher salaries and found jobs more quickly.

What About Recent Graduates?

While students today have the chance to switch degrees, those who graduated before the rise of generative AI are stuck with the degrees they have. It is theoretically plausible that young graduates with AI-exposed degrees face lower labor demand in their field and are forced to find work elsewhere. Think software developer turned retail manager. But is it in the data?

We can use American Community Survey (ACS) data to see the degrees and occupations of young graduates. The table below shows, for example, the top ten occupations of young graduates with a Computer and Information Sciences degree and how they have changed — or not changed — over time. The overwhelming majority of Computer Science graduates work in Computer and Mathematics occupations, the share having fallen less than a percentage point from the pre-COVID average to the years 2023 and 2024.

It doesn't seem like young graduates with Computer Science degrees are upending their careers to find work. What about those with other AI-exposed degrees?

We first looked at the top three most-common occupations for each degree during the years 2015–2019. For Computer Science degree holders, this would be Computer & Mathematical, Office & Administrative Support, and Management occupations from the table above. We can then determine how many young graduates with each degree in subsequent years have also ended up in those same three occupations. The results are in Figure 3.

Across all three groups — Computer Science graduates; all graduates with the most-AI-exposed degrees including Computer Science; and all graduates with any degree — the share of graduates going to the most-common occupations has changed little over time. This result is consistent with a more-complex but generalized measure of how occupation choices change over time, as shown in the Appendix.

Taking Stock

There are several possible explanations for why we don't see students avoiding AI-exposed degrees or young graduates working in occupations outside their field.

First, how AI is impacting the job market today is not at all clear. As Jed KolkoÌý, this research is still in its early innings. If students are looking at the labor market to decide what to study, it's not clear how AI would reorder their choice.

Second, the content of degrees or occupations themselves may change. If the curricula of AI-exposed degrees change to focus on skills complementary or resilient to AI, students may not feel the need to avoid those degrees. Similarly, the mix of tasks workers do in highly-AI-exposed occupations may be changing in ways that leave plenty of work for newly minted college graduates.

Third, as we noted above, the AI-exposure measures themselves have a lot of limitations. Knowing that a job contains tasks that an AIÌýmightÌýbe good at doesn't necessarily tell you what will happen to employment in that job. This could help explain why Computer Science had decreased enrollment while engineering, which is also classified as highly AI-exposed, had increased enrollment.

Finally, there might be some behavioral frictions that slow the reallocation of students and graduates to occupations outside their field. Rather than looking far and wide for work, young highly-AI-exposed graduates might hold out for work in their field, waiting in unemployment longer.

Perhaps next year's enrollment data will show movement away from AI-exposed degrees, but we doubt it. Not all "exposure" is the same. Occupation and sector-specific bottlenecks, diffusion dynamics, task reallocation, and output demand effects will jointly determine labor demand — and, in turn, the degrees students choose.

See our github with replication codeÌý.

APPENDIX

Classifying degrees by AI exposure

We classify groupings of degrees by AI exposure using two pieces of information: (1) occupational-level AI-exposure measures and (2) the cross-sectional mapping of degrees to occupations. We utilize the Eloundou et al. (2024) GPT-4 beta scores, the most commonly used measure in the literature, as our occupational measure of AI exposure. This approach essentially walks occupational exposure back onto degrees based on how often individuals with each degree are observed in each occupationÌýbeforeÌýthe introduction of generative AI.

We start by using a sample of college graduate workers between the ages of 22 and 27, inclusive, in the 1-year ACS for years 2009 to 2019. Using these years avoids contamination with AI-induced changes in degree-to-occupation flows and changes during COVID. In each year, each individual in the sample is observed to be in one of 39 groupings of degrees in the ACS's classification scheme and the Census 2010 4-digit occupation code. We compute the weighted mean of occupational AI-exposure scores for each degree-occupation pair. We then use this weighted mean to classify the 39 degrees into 5 quintiles based on AI exposure.

The AI-exposure scores are crosswalked to 2018 census occupation codes following our methodology inÌý"AI and Jobs", and to Census 2010 codes based on a crosswalk constructed using ACS samples with codes available under both vintages (occ2010 and occ for post-2018 samples). In some analyses, like Table 1 and Figure 2, we collapse occupation detail to 24 major SOC occupation groups. The degree-level exposure measures are shown below.

The ACS data is not well-suited to measuring how responsive college enrollment counts are to the introduction of generative AI. The ACS only reports college majors for those who have received their diploma. Students who graduated in 2023 and before were unable to adapt their degree choice in response to developments in AI that occurred in that year or after. Students graduating in 2024 would have needed to select a major by their sophomore or junior years (2023 or 2022), and so may have already been anticipating AI disruption when making this selection, but many would have been locked in to a major before ChatGPT's release in November 2022. The 2025 cohort of college graduates with a bachelor's degree are the most likely to have been capable of taking AI into account in their degree selection, but the 1-year ACS runs only through 2024.

To address these issues, we rely on theÌýÌýreports the number of enrollees — graduate and undergraduate — by declared major. This provides a more timely estimate of student majors. The clearinghouse received enrollment data submissions from 97 percent of all Title IV, degree-granting institutions in the U.S. in the fall of 2024. Coverage rates vary by year, but are handled with a weighing system described on their methodology pagesÌý. Degree majors follow a different classification as the ACS, though most majors match up one-to-one.

Additional Figures

The figure below shows the change in degree-to-occupation shares for young college graduates with a Computer and Information Sciences degree. The figure shows the L1 distance, which is computed as the sum of absolute differences between shares in a given year and the base year. It answers the question: "How different is the occupation mix of students with a given degree now than it was in the past?"

If students were forced to look outside their field for a job, this should show up as an increase, even if temporary, at the end of each of these lines — the occupation mix would be different from prior years. Across different baselines, changes in degree-to-occupation shares are relatively stable over the last few years.

The chart below shows the percentage change in enrollment between 2024 and 2025 by degree and AI exposure. Bubble size is weighted by 2025 enrollment. We can see the 8 percent decline in enrollment in Computer and Information Sciences, which is among the most-exposed degrees. Engineering and business degrees, which are also highly exposed, saw rising enrollment. These two different trends net out to the slight increase in enrollment for all highly exposed degrees shown above. The linear trend is statistically insignificant at the 5% level (-17.17 [8.97]).

Ìý

References

Blume-Kohout, M. E., & Clack, J. W. (2013). Are graduate students rational? Evidence from the market for biomedical scientists. PLoS One, 8(12), e82759.

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306-1308.

Frank, M. R., Sabet, A. J., Simon, L., Bana, S. H., & Yu, R. (2026). AI-exposed jobs deteriorated before ChatGPT. arXiv preprint arXiv:2601.02554.

Long, M. C., Goldhaber, D., & Huntington-Klein, N. (2015). Do completed college majors respond to changes in wages?. Economics of Education Review, 49, 1-14.

Ryoo, J., & Rosen, S. (2004). The engineering labor market. Journal of political economy, 112(S1), S110-S140.