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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 a potential shock. But a number of economists, journalists, and other experts have drawn worrying parallels between these two shocks.

We understand why. Across a large body of literature, 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.

The workers most exposed to the China Shock were highly concentrated in a select few manufacturing industries. In fact, the vast majority of American workers had no exposure at all.

Here is how we know.

A paper published 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 for workers, 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

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

Instead, we use the approach in 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 what could be automated, not what has been automated today. 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.

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.

Why does it matter that AI shocked workers are more educated and have higher earnings? The reason is that education and earnings greatly affect the ability of workers to adapt to job loss.

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 effects were concentrated among workers with lower initial wages.5 Low 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 were less likely to 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 Yagan found that 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 the decline of coal mining.

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

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.

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 geographically concentrated.

Local labor markets adapt to small losses of employment all the time. Even outside of recessions, roughly 30 million jobs across the United States are lost every year due 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.

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

Research shows that 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 (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.

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 have also surged, rising at the fastest pace since 1959. The current AI 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.

It is true that the impacts of the China Shock were more negative than many economists expected. But it is also the case that those negative effects are no longer a mystery. Thanks to a growing body of research, quite a bit is known about which types of people and places were more affected than others.

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.

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 workforce from 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.