While overall national jobs numbers continue to inch upward, more granular data at the state and sectoral levels reveal a startling trend across the country: healthcare jobs in many states are booming, but most other sectors are stagnant.

“And no state appears as reliant on the healthcare and social assistance sector to support its labor market,” write EIG senior fellow Kenan Fikri and research associate Thomas Cronin, “as the nation’s most populous and the world’s fourth largest economy: California.”

New research from Fikri and Cronin finds that since March 2022 California has increased employment in these fields by a massive 25.3 percent, but has seen negative private-sector jobs growth otherwise.

California stands out from the pack, but it is not the only state where healthcare is masking labor market weakness overall. Georgia, New York, Oregon, New Jersey, Maryland, and Colorado all have experienced healthcare job growth exceeding 17 percent while employment in all other sectors in these states has expanded by 3 percent or less.

Nationally, jobs in healthcare and social assistance increased by a strong 16.3 percent, compared to just 2.9 percent in the non-health segments of the economy.

Amplifying EIG’s publishing of this research at Agglomerations, the Wall Street Journal printed a story in its Thursday May 14 edition anchored by these stunning findings. “EIG analyzed a three-year span of the most recent Labor Department data to cover a mixed period for the labor market, which saw a dramatic cooling off after a postpandemic hiring boom,” Harriet Torry and Josh Ulick conveyed to Journal readers.

While healthcare and social assistance jobs are not a bad thing to add to a state’s employment mix, the wages associated with them tend to be lower than in fields in which these states have suffered losses. Moreover, California, Oregon, and other states may see the healthcare sector struggle going forward with the dissipation of federal Medicaid spending.

“If this federal support starts to dry up,” Fikri told the Journal, “we’re going to soon discover whether the healthcare boom has legs sufficient to stand independently.”

Policy

• EIG submitted a comment letter arguing that the Department of Labor’s proposal to raise prevailing wages for H-1Bs and green card applicants is based on flawed premises. EIG found that the proposal fails to account for differences in worker education, experience, and qualifications when estimating prevailing wages. The letter also argued that the proposal would continue to advantage large outsourcing firms and fail to select for top talent. EIG urges the Department of Labor to adopt the alternative proposal, known as Experience Benchmarking, to correct these flaws. EIG also noted, however, that fixing flaws within the H-1B program is a suboptimal solution overall and recommends market-based immigration reforms, such as selecting H-1Bs with the highest salary offers.

• EIG issued a statement in response to the House passage of the 21st Century ROAD to Housing Act. John Lettieri, President and CEO of the Economic Innovation Group. “America cannot lower housing costs without building substantially more homes of all types,” said John Lettieri, EIG President and CEO. “This legislation takes meaningful steps toward that goal, and Congress should move quickly to get it across the finish line.”

Research & Analysis

Smells like Teen Unemployment
The overall labor market appears reasonably healthy, but real weakness has seeped in and virtually all of it is concentrated in the young. Employment has fallen not just for recent college grads but also for noncollege young adults and for teenagers, making it unlikely that the cause of these problems is AI replacing worker tasks. Something else is going on. But what?

How Students and Recent Grads are Responding to AI
Sarah Eckhardt and Nathan Goldschlag find that undergraduates “are flocking towards the most-AI-exposed degrees, with enrollment in those degrees up 8 percent last year compared to 2017. This trend holds despite a notable decline in 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.” One reason why is that the jobs these students are likely to be offered still pay very high wages.

The Problem of Partial Noncompete Bans
Partial noncompete bans can be net positives if they are carefully written and apply to a wide swath of workers, but too often the exemptions — some of them quite absurd — lead to mass confusion, chicanery on the parts of employers, and unenforceability. The evidence is clear, write Thomas Cronin and Ben Glasner: “Banning noncompetes for certain job types or income levels helps curb some abuses, but it leaves intact many of the mechanisms by which noncompete agreements hurt most workers and stifle innovation and entrepreneurship. The best way to achieve a stronger, fairer, and more dynamic economy is for states to ban noncompetes entirely.”

Around the Horn

• For the National Bureau of Economic Research, Nathan Goldschlag and coauthors have issued a working paper examining the relationship between patents and economic growth.

• Kyla Scanlon compiles ways to respond to the rise of AI, including “Be AI native, not AI proof” citing the post on students and young graduates from Sarah Eckhardt and Nathan Goldschlag.

• In City Journal, Jordan McGillis explains the fraught labor dynamics hindering autonomous trucking in California.

• In National Review, McGillis argues against a federal subsidy for an Alaskan pipeline.

• In The Dispatch, McGillis applies EIG’s Distressed Communities Index to family housing decisions.

• And also in National Review, McGillis has published a longform reported essay on the U.S. Geological Survey’s Earth Mapping Resources Initiative.

• In Newsweek, Billal Rahman quotes Sam Peak on increased scrutiny of the H-1B program.

• In an essay for the Washington Post, the University of Notre Dame’s Alexander Kustov highlights EIG’s survey work showing public support for high-skill immigration.

• Also in the Washington Post, reporters Shira Ovide and Rachel Lerman drew input from Nathan Goldschlag in their coverage of AI’s effects on the labor market.

• Coverage of the Trump administration’s new retirement plan continues, with reporters citing Ben Glasner and Sarah Eckhardt’s pivotal research on the issue in USA Today and Forbes.

EIG Chart of the Month

Via Kenan Fikri’s post on the dominance of health care jobs in the California labor market:

There is another data point making me skeptical that AI is behind the labor market woes of young people: The unemployment rate for teenagers (ages 15–18) has risen more than for any other category of young workers. From its post-pandemic low, teenage unemployment has climbed 4 percentage points, compared to the 1.5 percentage point increase for recent college graduates.

Is it plausible that the cause of these trends is primarily AI? In other words, how likely is it that AI is the main cause of employment struggles not just for recent college graduates, but also noncollege youths and even teenagers?

What do teenagers do?

When looking at the kinds of entry-level, white-collar “laptop” jobs that recent college graduates tend to do, it is natural to wonder if AI is playing a role in the lack of hiring. The same is not true when looking at the kinds of work that teenagers do.

The industries that hire teenagers are simply not the ones that AI is likely to disrupt by displacing a large share of workers. Of the teens who were employed from 2022 through 2024, for example, a combined 75 percent of them worked in Accommodation and Food Services; Retail Trade; Arts, Entertainment, and Recreation; and Health Care and Social Assistance. ChatGPT can do a lot of things, but it can’t help if you want fries with that.

Measures of AI exposure at the occupation level tell a similar story. According to three separate measures of occupational AI exposure from influential research papers, only a small share of teens — between 3.8 and 8 percent, depending on the measure — are in the most AI-exposed occupations.

To whatever extent the struggles of teenage workers have the same cause as the employment struggles of other young adults, that cause is almost certainly AI.

Side note: Are the kids alright though?

Closer scrutiny of the teen labor market reveals an interesting wrinkle in the story. By one key metric, conditions are simply back to what they were before COVID.

The employment rate for teenagers, which is the share of all teens who are employed, started surging in 2021 as the pandemic recovery took hold and climbed far above its pre-pandemic high.1 It finally peaked in early 2023 before subsequently falling to where it is now, roughly consistent with its level from before the pandemic.

Put another way, the labor market for teens has indeed weakened, but only as a reversion to a historically normal level, and a pretty strong one at that.

Contrast the teen story with employment for young adults. The employment rate for both college and noncollege young adults (ages 22–25) barely recovered to its pre-pandemic level before stagnating and then falling again in recent years.

Why teen employment rose to greater heights during the pandemic than young-adult employment is itself an interesting question. Part of the story likely has to do with post-pandemic policies, which by design made it easier to not work for people who benefited from them — a group that largely excluded teens. Stimulus checks went to their parents. Teens mostly never qualified for expanded unemployment insurance. And because most teens live with their parents, they get nothing from an eviction moratorium. Nor do people who haven’t been to college yet benefit from pauses in student loan payments.

Still, the relative resilience of the teen labor market is consistent with the fact that it has deteriorated in recent years, just as the labor markets for recent college graduates and noncollege young adults have deteriorated. Something is damaging the labor market for young people. If, as I have already argued, that something is unlikely to be AI, then what is it?

Low Hire, Low Fire

I have to admit that current labor market conditions are kind of weird. This is not a normal business cycle.

In addition to the struggles of young workers, another sign of weakness for the overall labor market is the slowdown in real and nominal wage growth. The quits rate, which tends to climb during strong labor markets as workers leave their jobs for better opportunities, is down from its post-pandemic highs. And interest rates are probably still above neutral, meaning that the Fed still has some pressure on the brakes of the economy as it tries to fight inflation.

On the other hand, the prime employment rate, which is the share of all adults aged 25–54 who are employed, has moved down only slightly from its peak. Total unemployment has inched up, but only a bit. Taken by themselves, and in the context of previous historical periods, these two measures would suggest a healthy environment for jobs.

One way to summarize this bizarre labor market is that real weakness has crept into it, but it seems concentrated almost entirely on the youth. Just why the youth are bearing the brunt is unclear, an ongoing economic mystery.

For teenagers, one possible contributing factor is that because their employment rate climbed so high after the pandemic, it thus had the farthest to fall. But that point doesn’t resolve why youth of all kinds, including groups that didn’t see a post-pandemic surge, have also experienced these deteriorating conditions. Teenagers are hardly the only canary in this coalmine.

Time to Zoom Out

Somewhere between the economic measures showing weakness and those showing strength is the middling reality of the low hire and low fire economy, in which employers are not laying off workers but neither are they hiring many of them. The lack of visibility into the economic future has left businesses paralyzed by a generalized uncertainty.

There are plenty of good reasons for it: tariffs that move up and down violently, wars abroad and the resulting shocks to energy prices, interest rates that might go up or down, high levels of deportations, and — yes, it’s fair to include — the unknowable effects of AI on the economy and society.

But while AI is a possible cause of uncertainty, one of several, it’s important to recognize that employers not hiring because they are frozen by AI uncertainty is not the same thing as AI replacing the tasks of young workers. Those are two different stories.

Economists and researchers and journalists are mostly focused on the story of task displacement. They are constantly trying to assess how real it is, whether it has started, how far it will go, which jobs are vulnerable to it, and so on.

I think it’s time to zoom out and instead look at AI uncertainty and also the many other types of possible macro uncertainty the economy now confronts. Given the mystery surrounding youth labor-market weakness, it would be helpful to widen our scrutiny beyond the question of which tasks performed by recent college grads might someday be taken by AI, and instead investigate the strange macroeconomy we are now living through.

See our GitHub with replication code here.

Unless, that is, you’re a babysitter.

Washington, DC bans noncompete agreements for workers earning below roughly $160,000 a year. But the ban expressly excludes “casual babysitters,” meaning that a teenager in high school who occasionally watches their neighbor’s kids can be legally barred from babysitting for anyone else.

While defenders of noncompetes argue that they act as protection for trade secrets, client relationships, and training investments, none of this applies to casual babysitters. They are both highly replaceable and unlikely to know anything damaging to their employers.

The absurdity of DC’s babysitter exception is emblematic of what noncompete policy often looks like when states opt for a partial ban: divisions by income, occupation, or industry that tend to be arbitrary, confusing, and difficult to enforce.

Truly Un-exception-al

Businesses take advantage of this confusion by applying noncompete agreements far more broadly than is necessary to protect their proprietary information. News stories and multiple studies and surveys have uncovered countless examples of employment restrictions on workers whose job mobility poses no special threat to their employers: sandwich makers, house cleaners, temporary warehouse workers, and more.1

By one 2014 estimate, 38 percent of workers have worked under a noncompete at some point in their lives. This despite the fact that other enforcement mechanisms, such as nondisclosure agreements, already exist to protect business interests and are much less harmful to employees and the economy as a whole.2 3

Some states are beginning to recognize the problems with partial noncompete bans. Washington State, for example, first adopted a noncompete ban only for workers with annual earnings below $100,000 in 2020. But just six years later, it passed a new law replacing that partial ban with a full retrospective noncompete ban for all workers,4 extending protections to the remaining 15 percent of the state’s workforce and 43 percent of the state’s total earnings.5 (Retrospective noncompete bans are those that invalidate existing bans in addition to outlawing future ones.)

Washington now joins California, Minnesota, Oklahoma, and North Dakota in fully banning noncompetes.

Unfortunately, workers in the other 45 states and DC are still reliant on incomplete and uneven protections. Large parts of the workforce remain vulnerable to contracts that suppress mobility and discourage entrepreneurship.

The story of Epic, a megalithic electronic health records (EHR) company, offers a particularly stark example of the rationale in favor of universal bans, with no carveouts, on noncompete agreements. Based in Wisconsin, which only limits how long noncompetes can last, Epic requires its employees to sign a noncompete that prevents them from working for any of the roughly 4,500 companies across the United States on its “blacklist” that either compete with Epic or are connected to its customers and partners. This noncompete effectively forces workers leaving the company to exit the healthcare industry entirely. Former Epic employees can’t even create their own healthcare start-up or business for the duration of their noncompete clause.

Leaving workers with such narrow options effectively negates their power to negotiate with their employer. Noncompetes thus can suppress wages, restrict the flow of talent and ideas, and limit new business formation.

Job-Type Noncompete Bans

By far the most common type of partial noncompete ban is based on job type. These laws selectively protect workers in specific industries or with particular job titles or legal statuses — such as physicians, healthcare workers, or employees paid by the hour — on the theory that noncompetes for these types of workers are especially likely to harm them or the wider economy. All other workers are still subject to noncompetes from their employers.

Noncompete bans that focus on specific job types are often so comically narrow that they may as well not exist at all. Kentucky and Iowa, for example, ban noncompete agreements only for nurses, home health aides, and other health staff who contract with healthcare employment agencies instead of working for hospitals directly. Connecticut has a noncompete ban for workers specifically in non-cable broadcasting companies. And in Vermont, noncompetes are banned only between barbering and cosmetology students and their universities. Predictably, less than 0.1 percent of each state’s workforce is covered by each of these provisions.6

Even some of the broadest noncompete bans by job type end up covering very few workers. Nevada’s noncompete ban for all hourly workers, for example, covers only about 10 percent of its workforce.7

Job-type noncompete bans are also the easiest bans for firms to circumvent. A mere title or classification change can be the difference between whether or not a noncompete is enforceable for a worker doing otherwise similar tasks. Under Nevada’s noncompete ban for hourly workers, for example, a retail worker paid hourly may be protected, while a salaried worker doing nearly identical work is not. These laws simply fail to be a meaningful impediment to firms imposing noncompete agreements on their employees.

And in edge cases where the legal enforceability of a worker’s noncompete agreement is unclear, many workers will assume their noncompetes are binding. Studies show that even clearly unenforceable noncompete agreements significantly reduce workers’ job mobility behavior and new employers’ willingness to hire them.

Finally, noncompete bans by job type can distort the labor market by artificially making some jobs more appealing to workers than others. When only some jobs carry noncompetes, workers may avoid otherwise better matches in favor of less restrictive roles, leading talent to flow toward jobs with fewer contractual barriers rather than where it would be most productive. This misallocation will ultimately reduce both job match quality and overall economic efficiency.

Income-Based Bans

Income-based noncompete bans forbid noncompetes for all workers earning below a certain income level. One upside of these bans is that they are much simpler, making it apparent when a noncompete is enforceable and when it is not. Another is that they tend to cover much wider swaths of the workforce, depending on the level of the income cutoff.

While most job-type bans apply to less than 5 percent of their state’s workforce, New Hampshire’s noncompete ban for workers earning less than 200 percent of the state’s minimum wage — about $29,000 per year in 2024 — covers more than 25 percent of the state’s workers. And the income-based noncompete thresholds are typically much higher in the other states that have them. Oregon, for example, bans noncompete agreements for those earning below about $116,000 per year in 2024 dollars, protecting about 85 percent of its workforce.8

Higher earners, however, are by definition left exposed by income-based noncompete bans. These workers are typically the most specialized and therefore the most likely to engage in the kinds of productive activities, like starting businesses and transferring skills across firms, that are restricted by noncompetes.

Narrow specialization also makes it substantially more difficult to find a new job that doesn’t violate the terms of their noncompete without leaving the entire industry, so the harm of a noncompete agreement to high-income workers themselves can be especially pronounced.

High earners furthermore account for a disproportionately large share of total income, which means that income-based noncompete bans protect a much smaller share of total earnings than of workers. New Hampshire’s ban on noncompetes for workers earning below twice the minimum wage, for example, covers 25 percent of its workforce but only 6 percent of the state’s total income. So failing to ban noncompetes for these workers leaves a great deal of economic activity vulnerable to restriction.

Income-based noncompete bans are an effective way to protect lower-wage workers from abusive business practices, but states still sacrifice a great deal of productive activity by allowing noncompetes for high earners.

Just Go All the Way

We recognize that political reality differs from state to state. In some places, given the absence of federal action, a full ban may not yet be possible. Advocates of a universal ban on noncompetes sometimes need to make concessions to protect at least some workers. They might acknowledge that a partial noncompete ban has risks but nonetheless consider it better than no ban at all.

Consider a few recent examples of states moving towards a partial ban:

• Utah passed out of committee a noncompete ban for non-exempt workers, students, contractors, and employees earning below $155,000 per year;

• Virginia’s state legislature approved new protections for licensed medical practitioners and discharged employees;

• New York state is debating a noncompete ban for workers earning less than $500,000 per year.

Partial bans, especially when applied clearly and to a wide swath of the labor force, can still be large net positives for workers and the economy, reducing barriers to mobility and improving labor market dynamism. They are especially worthwhile if they prove to be an incremental step on the way towards a complete ban, as was the case for Washington.

But we also encourage advocates of partial bans to remember the evidence: Banning noncompetes for certain job types or income levels helps curb some abuses, but it leaves intact many of the mechanisms by which noncompete agreements hurt most workers and stifle innovation and entrepreneurship.

The best way to achieve a stronger, fairer, and more dynamic economy is for states to ban noncompetes entirely.

See our GitHub with replication code here.

Not so. Healthcare job growth varies dramatically by state, as does the extent to which healthcare contributes to total job growth. And no state appears as reliant on the healthcare and social assistance sector to support its labor market as the nation’s most populous and the world’s fourth largest economy: California.

Another take on a “health-full” labor market

The total number of jobs in the United States increased by 4.7 percent from March 2022 to March 2026, but the differences across sectors were massive.1 Jobs in healthcare and social assistance increased by a whopping 16.3 percent, compared to just 2.9 percent in the non-health segments of the economy.2 Artificial intelligence (AI) may dominate financial markets, but healthcare still reigns supreme in the labor market.

Scan the figures in the charts below, and it becomes apparent just how geographically disparate job growth has been over the past four years even in this quintessentially local sector.

California outpaces every other state with its 25.3 percent job growth in healthcare and social assistance from March 2022 to 2026 — and that growth wasn’t being powered by a quickly rising population. The number of California residents increased by less than 1 percent between 2022 and 2025.3

But what really distinguishes California is how it combines such massive growth in healthcare with such little growth in other parts of the economy.

California eked out total job growth of a mere 3.4 percent over the past four years, the bulk of which was racked up at the beginning of the period (see the top figure). Take out healthcare and social assistance, and employment across all other sectors in the state declined by 0.3 percent. Over the past four years, employment in the non-care portions of the economy shrunk in only three other states, led by DOGE-walloped DC.

The growth gap between the health and non-health parts of the economy (the length of the bars in the above graph) is greater in California than in any other state.

Healthcare now accounts for 17.4 percent of all jobs in California, up three percentage points in four years. That is twice the rate at which healthcare is expanding its dominance over the labor market nationwide.

All subsectors go

Growth was solid across all four main subsectors of the California care economy. Employment in hospitals increased 5.2 percent, in ambulatory medical care (think doctors’ and dentists’ offices) by 15.7 percent, in nursing home facilities by 17.2 percent, and in social assistance by 27.2 percent over the past three years.4

Even more granularly, 36 of the 39 narrow industries that make up the care sector added jobs over the most recent three-year window.5 In absolute terms, services for the elderly and disabled topped the list, adding a whopping 211,000 jobs (31.5 percent increase) and accounting for nearly half of all job growth in the state’s care sector.6 Mental health practitioners led the way in percentage terms, with the ranks of California therapists nearly doubling (92.7 percent growth, or 27,212 new jobs).7

These numbers are huge but not anomalous. Nearly every corner of the care economy is growing, and no single corner can explain the larger sector’s might.

Nevertheless, it is striking how exceptionally low-paid these new jobs in services for the elderly and disabled are, coming in at $487 a week on average (or just over $25,000 per year) in the state.8 This is the lowest paid sub-sector of the entire care economy and one of the lowest paid in the entire economy, too.

Stepping back, it is also notable just how starkly the robust growth across basically every corner of the care sector compares to weak growth across the rest of California’s economy.

Trading down?

Lay out all the pieces and it becomes clear that, as the California economy evolves, jobs in high-wage sectors are being replaced by jobs in low-wage sectors. Jobs are shifting from manufacturing, information (tech), and finance and professional services into much lower-paying care roles. States such as Idaho are adding jobs in both high- and low-wage sectors. California is undergoing a great labor market transformation towards lower-paid work.

Consider this rough estimate: Multiply the average weekly wage in each sub-sector in Q3 2022 by the number of jobs gained or lost over the subsequent three years (essentially an expanded version of the above chart with the most granular subsectors available), and California workers shed around $1.26 billion in inflation-adjusted earnings a week through this sectoral trading down. That equates to $65.4 billion annually, and this amount is actually an understatement of the composition change’s effect because the total number of private sector jobs grew slightly (by 28,900, as shown in the rightmost column above) over this time period.

Diagnostic check

Could these findings be figments of the data — statistical or administrative blips that don’t reflect economic realities on the ground?

Our analysis was conducted using two Bureau of Labor Statistics’ (BLS) datasets, State and Area Employment, Hours, and Earnings (SAE); and QCEW. The latter, which is derived from state unemployment records, feeds into the former, which adds survey responses from BLS’s Current Employment Statistics series to offer even more timely estimates. QCEW is derived from high-quality administrative data and forms the benchmark for numerous other labor market series. California’s sheer size translates into high-quality data estimates across both sources.

Nor do there appear to be any California-specific biases in the largest subsectors driving the trends, such as Services for the Elderly and Persons with Disabilities (or NAICS 62412 in the classification system used by statistical agencies; see the footnotes). Concerns about how home health aides are scoped and categorized at the establishment level — is each home they work in considered an establishment, or only the main employer or agency home office? — do not apply to this exploration of jobs. What is more, any anomaly in one corner of the Health Care and Social Assistance sector (NAICS 62) is unlikely to explain weaknesses in other sectors. It’s highly unlikely that a care worker would be misclassified into manufacturing, retail, or tech, for example. Across the board, the state’s relative estimates appear in line with others; they simply add up to a story that is unique to the Golden State.

California has the highest foreign-born share of the population of any state. Perhaps the spike in care jobs somehow extends from President Trump’s immigration crackdown, as once-informal jobs are formalized or native-born workers begin to occupy the positions vacated by departing migrants?

Here too, the evidence doesn’t match up. The largest increase in care economy jobs appears in the first quarter of 2024, well before the election. The rate of increase in elderly services held steady around 3 percent per quarter from Q3 2024 through Q3 2025. And while it’s true that immigrants are overrepresented in the home health care industry (NAICS 621) and undocumented immigrants occupy a range of healthcare support roles, we would expect to find evidence to support the formalization hypothesis in construction and other industries, too, where the immigrant share of the workforce is even higher. The fact still stands that few sectors are expanding in the Golden State beyond healthcare.

Prognosis

What does the present composition of job growth mean for the future of the California economy? It’s hard to tell, but the rate at which the Golden State is shedding jobs in its highest-value sectors like tech and professional services while adding them in decidedly duller corners with more dismal wages is cause for concern. With a gubernatorial election this year, a billionaire tax possibly on the ballot, and soon-to-be-former Governor Newsom preparing for a potential presidential run, closer examination of the California model is also of national interest.

California’s population is not especially old or feeble. Only 16.5 percent of its population is 65 or older, making it the sixth-youngest state on this measure. Californians also report fewer chronic health conditions than almost anywhere else. So why is its healthcare sector so mighty?

One plausible partial explanation is that state policies are driving the outcome — in other words, that government subsidies are allowing more people to access more healthcare and social support, stoking demand.

And indeed, many of those new therapy jobs are probably supported by the state’s Children and Youth Behavioral Health Initiative, a $4.6 billion dollar program launched in 2021 to connect youth with mental health services. Enrollment in Medi-Cal, the state’s medicaid program, has increased significantly over the past decade as the state has continuously expanded coverage, including in recent years to undocumented populations. As a result, only 5.9 percent of Californians went without health insurance in 2024, a record low. The extensive margin (new enrollment) isn’t the only one at work; the intensive margin (use of healthcare services) is too. The state legislature has noted that Medi-Cal spending per enrollee is increasing even faster than the number of enrolled. Both greater utilization and greater coverage are driving demand and creating jobs in the process.

So how should we think about the care economy in the broader context of the California labor market? Is it an economic engine, a useful jobs sponge? Or does it represent an unhealthy dependence? Is the care economy picking up the slack left by weakness in other sectors or crowding out growth?

And given how intimately involved the public sector is in healthcare finance, we have to ask where the public support is coming from and what tradeoffs are involved with raising resources from one corner of the economy to spend them in another.

And then, finally, we have to ask how the political economy changes when healthcare spending effectively becomes a jobs program.

We might start to get some answers to these questions over the coming months, as the Medicaid- and healthcare-related portions of the One Big Beautiful Bill Act work their way through state coffers. Medi-Cal’s expansion was not only fueled by the state’s swelling tax receipts after the pandemic. It was also made possible by federal largesse and a tax on private plans that will be subject to heightened federal scrutiny going forward.

States that expanded Medicaid under the Affordable Care Act now have a nearly 3 percentage point greater share of their workforce in healthcare jobs than those that didn’t on average. A dozen expansion states have triggers that could discontinue or dial back their support if federal funding dries up — which it is poised to do. We may soon get a better sense of the extent to which the healthcare jobs juggernaut has been fueled by Washington, and whether it has legs strong enough to stand independently.

Where does that leave us? Healthcare has driven the plot in the national jobs story for several years running. The subplot of weak growth in other good jobs sectors may carry the narrative from here.

They say the future happens first in California. What we don’t know is how healthy it will look.

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 towards the most-AI-exposed degrees, with enrollment in those degrees up 8 percent last year compared to 2017. This trend holds despite a notable decline in 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 important limitations, 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 work showing that AI-exposed occupations have higher incomes, this finding is also consistent with research from 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 Kolko recently put it, 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 AI might be 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 code here.

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 occupation before the 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 in “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.

Measuring Enrollment

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 the National Student Clearinghouse Research Center, which reports 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 pages here. 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]).

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.

On the final day of April, President Trump signed an executive order to address one of the biggest wealth-building gaps in America’s economy: that too many workers, including 42 percent of full-time private-sector workers, lack access to retirement plans.

With the finalization of a policy hinted at in February’s State of the Union address, the Trump administration has created a new path to retirement for these workers.

As we explain in our detailed Q&A about the new policy, the government is creating the infrastructure and parameters for financial institutions to offer workers a retirement product similar to the Thrift Savings Plan (TSP), which is the retirement vehicle currently available to federal employees and members of the military. Moreover, these new individual retirement accounts will offer a federally-supported $1,000 annual match to low-income workers, consistent with the Saver’s Match legislation passed by Congress in 2022.

These new products will be listed on TrumpIRA.gov, along with an explanation for how eligible workers can claim the Saver’s Match. The new web site will be launched by the Treasury Department by January 1st, 2027, according to the executive order.

This policy builds upon years of EIG research, led by senior economist Ben Glasner. As Glasner wrote in 2023, “Retirement accounts are the largest source of aggregate fungible wealth for American households and are an important tool to build a nest egg for the future. Unfortunately, access to employer-provided retirement plans remains deeply divided across earning levels and regions of the country.”

With the launch of this new policy, EIG research estimates that 54 million more workers will now have access to a retirement plan. Of those 54 million, 11.5 million will be eligible for the full Saver’s Match, with another 14.6 million eligible for a partial Saver’s Match.

The new retirement initiative is a clear victory for economic inclusion, and it also creates momentum for even more progress. The next step is for Congress to pass the Retirement Savings for Americans Act (RSAA), a bipartisan piece of legislation that was built on the recommendations of a 2021 EIG white paper co-authored by economist Teresa Ghilarducci and Kevin Hassett, the current Director of the National Economic Council. The RSAA, most recently reintroduced by Representatives Lloyd Smucker and Terri Sewell and Senators John Hickenlooper and Thom Tillis in April of 2025, would automate enrollment, set a default contribution rate, and increase the match.

For media coverage of the new Trump administration retirement plan announcement citing EIG’s research and work behind the scenes, check out Axios, Semafor, CNBC, Barron’s, and USA Today.

Policy

• The EIG Opportunity Zones Coalition sent a letter to congressional leaders urging them to remove a provision in the Senate’s 21st Century ROAD to Housing Act that would restrict built-to-rent (BTR) development by requiring investors to sell properties within seven years. The coalition warns this would effectively eliminate Opportunity Zone investment in BTR housing, since OZ investments must be held for 10 years to receive the full tax benefits. The provision could potentially reduce new home construction by an estimated 72,000 units annually.

  ---

Research & Analysis

Manufacturing job gains are fading, but growth persists in dynamic places
The post-pandemic recovery of manufacturing jobs has been strikingly uneven across states and metropolitan areas. “Over four-fifths of this employment growth has occurred in just 354 out of 3,143 counties,” writes Jason He in his detailed analysis of the manufacturing sector. The two states that really stand out are Florida, because of its high manufacturing dynamism (elevated startup and exit rates), and Texas, which added the most manufacturing jobs of any state and accounted for over a third of the country’s manufacturing employment growth since 2019, writes He. National manufacturing employment has slipped since hitting a post-pandemic high in the third quarter of 2023.

H-1B Workers are Critical for AI Dominance
H-1B workers now represent an estimated 4.3 percent of the nation’s total AI workforce, and each year the share of H-1B visa holders who work in AI keeps climbing. Furthermore, write Sarah Eckhardt and Jason He, “Nearly four out of five new H-1B holders working in AI also completed their education in American universities, compared to 52 percent of H-1Bs overall. Losing many of these workers to China would undermine the domestic AI industry and could threaten American national security.” The United States should embrace

The Problem of Population Loss
Do residents of stagnant local communities recognize population loss as a problem? Sarah Eckhardt and Adam Ozimek answer: “The data show that local perceptions match demographic reality. Eighteen percent of respondents living in counties that lost population between 2010 and 2020 identified population loss as being a major problem for their community, versus 10 percent of those living in growing places. Including those who see it as a minor problem, 54 percent of respondents in declining counties view population loss as an issue, versus 34 percent in growing counties.” In addition to their direct contributions to the AI sector, these workers generate substantial fiscal benefits for the government. The United States should reform its high-skilled immigration system to make it easier for these workers to come, work, and stay here.

Around the Horn

• At the Center for Economic Policy Research, Nathan Goldschlag and coauthors explain why universities are losing top AI researchers to the private sector.

• In the Wall Street Journal, Jordan McGillis argues that female labor force attachment is a key driver of household income dispersion.

• In the New York Times, Jessica Grose cites Adam Ozimek and Nathan Goldschlag’s Agglomerations analysis on what’s happening in the young-adult job market. Rogé Karma cites the same analysis in The Atlantic.

• In Business Insider, James Rodriguez features Jess Remington’s research on the bifurcation of the housing market.

• In Newsweek, Billal Rahman quotes Sam Peak on the effects of the Trump administration’s $100,000 H-1B fee.

• In the Minnesota Star Tribune, Emma Nelson draws upon EIG’s Great Transfer-mation to show the rising share of Americans’ incomes deriving from federal social programs.

  ---

EIG Chart of the Month

From the post by Sarah Eckhardt and Adam Ozimek showing that residents of stagnant communities recognize population loss and a problem for them.

EIG Video of the Month

Jason Harrison and Ben Glasner riff on Jess Remington’s post in March, How the Housing Market Split in Two, which showed the differing experiences between new and existing homeowners:

The issue is certainly not that politicians neglect retirees as a group, or that policymakers are unwilling to use public money to help them. The United States already heavily subsidizes retirement savings. In 2019, federal income and payroll tax expenditures related to retirement savings totaled $276 billion, according to the most recent report that offers this breakdown from the Congressional Budget Office.1

The real problem is that the existing retirement system is badly designed to benefit the workers and retirees who most need help.

Retirement tax breaks from the government, primarily through 401(k) and similar plans, disproportionately benefit the highest-earning households. The primary reason for this disparity is not that lower-income workers choose to save a smaller share of their income, but rather that so many of them lack access to an employer-sponsored retirement savings vehicle in the first place.

The problem is obvious when simply viewing the key stats, starting with the outcomes…2

• Households in the top 20 percent by income received more than 60 percent of the benefits from “exclusions for pensions and retirement savings accounts” (the CBO category that represents the foregone taxes collected because of retirement program tax breaks).

• The bottom 40 percent of households by income together received less than 5 percent of the benefits.

• Within the bottom 20 percent of households by income, four out of five of them received none of these benefits at all.

… and then looking at the data on access to retirement accounts:

• Roughly 54 million workers, or about 47 percent of all full-time and part-time private-sector workers between the ages of 18 and 65, are not offered any retirement plan at work.3

• Among full-time private-sector workers, 42 percent lack access to an employer-sponsored plan. The situation is even worse for part-time workers: 79 percent have no access to an employer-sponsored plan.

• Nearly 80 percent of workers in the lowest earnings decile lack access, compared with just 18 percent in the highest decile.

• The income disparity is accompanied by racial and ethnic, generational, and rural vs urban disparities. Black workers, young workers, workers in small firms, and those in rural communities, for example, are all significantly less likely to have access as well.

For more on the methodology behind these estimates, including data on how many workers lack access to an employer match, see our earlier detailed analysis.

A New Approach

Closing the access gap and reducing the inequality in retirement savings will be impossible without first building the infrastructure that makes it easier for workers to save.

The federal government has started taking the necessary first steps, but there is confusion about what exactly has been announced, the likely effects of these new policies, and what policymakers should do next.

Here we attempt to answer some of the most common questions.

What just happened?

On Thursday, April 30th, the White House issued an executive order to expand retirement plan access for private-sector workers without employer-sponsored coverage.

Specifically, the government is creating the infrastructure and parameters for financial institutions to offer these workers a retirement product similar to the Thrift Savings Plan (TSP), which is the retirement vehicle currently available to federal employees and members of the military. The TSP offers low-fee index funds that invest in equities, bonds, and Treasuries.

In addition, the federal government will match half of each eligible worker’s contributions to this plan up to a limit of $1,000 (or 50 percent of the first $2,000 contributed by the worker).

How will workers know which plan they should choose?

These new products will be listed on TrumpIRA.gov, along with an explanation for how eligible workers can claim the Saver’s Match. The listed plans will be vetted by the Treasury Department to ensure their offerings align with those offered by the TSP. The plans will also be required to have no minimum contribution or balance.

In what ways will this new retirement policy help workers?

The first way is simply that more workers will have access to a tax-advantaged retirement plan. Retirement plans are incredibly effective wealth-building tools,4 but not every employer offers them, leaving many workers — especially low-income workers — excluded from this path to financial security in old age.

The second way this policy will help workers is through the match, which will boost each worker’s annual retirement contribution.

Where is the money for the match going to come from?

We need to take a step back to explain the answer.

The Saver’s Credit, which has been available to taxpayers since 2002, is a nonrefundable tax credit of up to half an individual’s contributions to a 401(k) or other retirement vehicle. It is capped at $1,000 per year. That it is nonrefundable means that many low-income taxpayers cannot get the full credit because their overall tax liability is too low (less than $1,000).

In 2022, Congress passed the Secure 2.0 Act, which replaces the Saver’s Credit with something called the Saver’s Match starting in 2027. The Saver’s Match is effectively a fully refundable tax credit of up to $1,000 (matching up to half the eligible taxpayer’s first $2,000 contributed to a retirement plan) to be deposited directly into an individual’s retirement account.

The Trump administration is making it possible for eligible workers who currently lack an employer-sponsored retirement plan to also have access to the Saver’s Match through their new retirement account. Thus, according to the administration, this matching benefit for its new plan has already been passed by Congress under the Secure 2.0 Act, and no new legislation is needed.

So is every worker who gets access to this new retirement plan eligible for the $1,000 match?

No, because the Saver’s Match is targeted by income. The only workers who are eligible to receive the full $1,000 match are married couples filing jointly who make at or below $41,000; heads of household who make at or below $30,750; and single filers making at or below $20,500.

If a worker makes more than that, given their filing status, they aren’t necessarily out of luck. They may fall into phase-out ranges. The Saver’s Match phases out linearly over modified adjusted gross income ranges of $41,000 to $71,000 for joint filers; $30,750 to $53,250 for heads of household; and $20,500 to $35,500 for single and other filers.

When does the new retirement policy take effect?

The program will launch in 2027. According to the executive order, the Treasury Department must also launch TrumpIRA.gov by January 1, 2027.

Can’t workers who don’t have access to an employer-sponsored retirement vehicle just set up their own retirement accounts, and access the Saver’s Match that way?

Yes. But what we know about low-income workers is that they overwhelmingly tend not to because of the administrative burden.5 Setting up such a plan on your own isn’t easy. The Trump administration is proposing to take on the administrative burden so that workers don’t have to. The new accounts will be offered by private providers and overseen by the Treasury Department.

And workers will then be automatically enrolled in those accounts?

No. Auto enrollment would require new legislation from Congress. So would increasing the matching amount beyond $1,000 and adding a default contribution rate.

How many workers will end up benefitting from the Trump administration’s new retirement plan?

We’ll give you the topline numbers first:

• 54 million total workers will become eligible for a new retirement plan.6

• Of those 54 million, 11.5 million will be eligible for the full Saver’s Match.

• Of those 54 million, another 14.6 million will be eligible for a partial Saver’s Match.

Here’s how the numbers break down by single, married filing jointly, and head of household:

And here’s how the math works. (If these details don’t interest you, feel free to skip ahead to the next question.)

As already noted, roughly 54 million workers currently lack access to an employer-sponsored retirement plan. Conceivably, all of them will be eligible for the new retirement plans created by the Trump administration.

Not all of them, however, will be eligible for the Saver’s Match of up to $1,000. This is where the calculations become a little complicated.

We can start by looking at how many workers will be eligible for the full Saver’s Match regardless of whether or not they already have access to a retirement plan. Using the Saver’s Match income thresholds and data from the Survey of Income and Program Participation (SIPP), we estimate that 15.1 million workers would qualify for the full 50 percent match.7 This includes full-time, part-time, self-employed, and government workers.

But only 3.5 million of these workers currently have a qualifying retirement account. What this means is that because of the Trump administration’s plan — here comes the answer — 11.5 million workers will have new retirement accounts through which they can receive the full Saver’s Match, or roughly 8 percent of workers.

Having done a similar calculation for workers with incomes in the phase-out range, we estimate that another 14.6 million workers will have new accounts through which they can receive a partial Saver’s Match.8

Why is President Trump doing this?

The president noted in the State of the Union speech in February that half of working Americans lack access to a retirement plan with matching employer contributions, pulling directly from work we did here at EIG.

What effects can we expect from the Trump administration’s new policy?

It’s impossible to know exactly how many workers will actually start saving more for retirement once they have access to these new accounts.

For the roughly 26 million workers who will gain new access to either the full or partial Saver’s Match, however, it is very likely to induce more savings. Survey evidence shows that interest in participation rises sharply once workers understand that a match is available. We also know from the federal Thrift Savings Plan that introducing a match increased employee participation by roughly 22 percentage points.

You mentioned that to automatically enroll these workers, increase the match, or set a default contribution rate, Congress would have to pass new legislation. Is someone writing that legislation?

It has already been written! The Retirement Savings for Americans Act (RSAA) is a bipartisan piece of legislation that was built on the policy recommendations of a 2021 EIG white paper by economists Teresa Ghilarducci and Kevin Hassett, who is currently President Trump’s Director of the National Economic Council. The RSAA was most recently reintroduced by Representatives Lloyd Smucker and Terri Sewell and Senators John Hickenlooper and Thom Tillis in April of 2025.

Like President Trump’s current plan, the RSAA would give workers without employer-sponsored retirement plans access to a new retirement vehicle similar to the federal Thrift Savings Plan, with similar investment options and employee ownership of the plan. But the RSAA would also go much further and offer to workers:

• Automatic enrollment in their new plan

• A default contribution rate set at 3 percent of a worker’s income

• Matching contributions of up to 5 percent

In short, the RSAA would extend to low-income and moderate-income families the same opportunity to build wealth that higher earners have long enjoyed.

Can you quantify the likely benefits for workers if the RSAA is passed?

Differences in wealth accumulation across households are driven not only by income but by unequal access to employer-sponsored retirement plans and participation in asset markets like equities and housing. Low-income households participate in these wealth-building systems at far lower rates, and that gap compounds over time.

What makes the case for a policy like the Retirement Savings for Americans Act more than a matter of fairness is the scale of the projected economic benefits.

Economists Pavel Brendler and Moritz Kuhn have estimated that every dollar of public spending on RSAA would result in roughly $2.40 in worker contributions and investment returns for workers and their families.

Younger workers would eventually enjoy the biggest gains, as they benefit most from time and compounding. According to Brendler and Kuhn, enacting RSAA today would amount to roughly $157 billion in additional accumulated retirement wealth, measured in 2024 dollars, for the nearly 5 million American households currently aged 25–29 by the end of their 40-year working life.

Simulation work from RAND shows that the RSAA would “enable the lowest earners (those who consistently earn in the bottom 10 percent of the earnings distribution) to save approximately $126,000 over a 40-year working career.” A worker at the median of the earnings distribution could approach $585,000 in savings.

If a lot of workers end up with tax-advantaged retirement accounts, and many of them are also getting a 5 percent match via public funds, won’t the policy have a big fiscal cost? Has anyone modeled that?

Yes — and in fact the fiscal effects over the long run are positive, not negative. Modeling from RAND suggests that implementing RSAA could generate more than two trillion dollars in net federal and state savings over forty years, largely because higher retirement assets reduce future spending on asset-tested programs such as Supplemental Security Income and Medicaid.

Okay, so what happens now?

It will be up to Congress whether to enact the remaining core elements of RSAA, such as auto enrollment and expanding the match. We hope it does. Policies that are fiscally responsible, address a big lingering inequality, and offer a boost to those who need it most are rare. With bipartisan support already on the table and key figures in the administration enthusiastic about the proposal, the moment for legislative action on retirement is now.

But the consequences of population loss can unfold gradually, making them difficult to recognize as they occur. Unlike discrete shocks — such as the COVID pandemic — which generate immediate momentum for action, the effects of population decline can be subtle and diffuse enough to lack the kind of urgency that motivates policy responses.

This raises a question: do affected residents themselves recognize population loss as a problem? Survey data suggests that they do.

The demographic problem is real

Population loss is undoubtedly a widespread issue. While demographic change has profound effects on a local economy and community, it’s unclear whether individuals actually perceive population loss in the first place.

We can test this question using a nationally representative survey of 2,005 respondents conducted by EIG and Echelon in 2022.1 In that survey, individuals were asked: “How much of a problem are each of the following in your local area these days, if at all?” One of the options was “population loss.” Importantly, we know the zip code where each respondent lives, which we then match to county-level economic and demographic data.2

The data show that local perceptions match demographic reality. Eighteen percent of respondents living in counties that lost population between 2010 and 2020 identified population loss as being a major problem for their community, versus 10 percent of those living in growing places. Including those who see it as a minor problem, 54 percent of respondents in declining counties view population loss as an issue, versus 34 percent in growing counties.

The more a county’s population shrank over the decade, the more likely its residents were to report population loss as a problem. In the top 10 percent of the fastest growing counties, 7 percent of respondents report that population loss is a major problem for their communities. In the fastest shrinking counties, that number rises to 23 percent.

Regression analysis demonstrates population loss is a non-linear problem. Among growing places, 1 percentage point slower population growth is associated with a 0.2 percentage point increase in the probability that survey respondents identify population loss as a major problem. In shrinking counties, the effect is 5 times larger.

Nobody’s business

Residents of shrinking communities don’t just recognize population loss in the abstract.

The survey data also shows that individuals identify specific economic symptoms that typically accompany demographic decline.

One of the most important consequences of population loss is lack of dynamism. A growing body of scholarship shows that demographic decline is linked to lower rates of business formation.3 Fewer residents doesn’t only mean fewer entrepreneurs, but also a smaller labor force. Both issues make it less likely that a community will be able to generate startups.

The survey data indicates that residents do perceive this connection between population loss and less dynamism playing out in real life. Respondents were asked how the number of new businesses has changed over the past 10 years, as well as how the availability of restaurants, stores, and entertainment options has evolved. In both cases, individuals living in areas with slower population growth are more likely to report that the number of businesses declined over the period.

What’s more, another survey question illustrates that the lack of new businesses rises to the level of concerning in demographically struggling places. Individuals reported whether a lack of new businesses or investment is a problem in their community, and regression analysis shows this is statistically significant related to population change. A 1 percentage point decrease in population growth is associated with a 0.79 percentage point increase in concern about insufficient investment and a 0.49 percentage point increase in concern about business decline.

In short, individuals in shrinking places do understand they have fewer new businesses and that this is a problem.

Wider problems

Unsurprisingly, the lack of investment in shrinking places is not an isolated problem. Another unavoidable result of demographic decline and lack of investment is that the tax base erodes. This in turn causes other deteriorations in quality of life. Population loss can also lead to local vacancies as some neighborhoods empty out and businesses shutter without replacement.

Our survey results are consistent with all of these issues. Regression analysis shows individuals in shrinking places are more likely to report that vacancy, the availability of good jobs, and poor public service are a problem in their community, and the effect is statistically significant. School quality is directionally worse, though not statistically significant.

Interestingly, people in declining places are very likely to report that both there were not enough workers (77 percent of respondents), and not enough good jobs (73 percent of respondents).

People living in declining places are less likely to support immigration than those living in growing ones.

Encouraging immigration to distressed regions is one of the main policy levers available to address population loss and its associated economic consequences.4 Yet residents of the places most in need of immigration are among those least likely to support it.

Residents of shrinking communities are less likely to view immigration as a net positive for the local or national economy, and are less supportive of temporary seasonal migration and immigration designed to address labor shortages. They are less likely to support high-skilled immigration even if it is framed as supporting entrepreneurship.

Support for immigration remains lower among those living in shrinking counties when controlling for urban versus rural status, political affiliation, gender, and educational attainment, suggesting that other factors play a role.

The contradiction between recognizing population loss as a local problem and being more likely to oppose a viable remedy poses a real challenge for policymakers.5 Well-designed place-based programs are unlikely to succeed without a corresponding effort to build public understanding of immigration’s role in reversing economic decline.

Still, there is reason for optimism. Support for high-skilled immigration remains strong: 61 percent of residents in shrinking communities support it, compared with 71 percent in growing ones. Policymakers should address the sources of opposition in declining areas, but this relatively high amount of baseline support suggests that efforts to expand high-skilled immigration in these regions may face only limited resistance.

See our github with replication code here.

Last August, the Chinese State Council announced its most comprehensive AI policy initiative to date. One directive of this “AI+” initiative is to expand China’s AI talent pool by encouraging enterprises to appeal to skilled workers using equity, stock options, and other incentives. The Chinese State Council has also announced a new visa for foreign STEM graduates.

Meanwhile, support for the American AI workforce is conspicuously absent from the White House’s recent AI Legislative Framework. And recent proposed and enacted changes to the H-1B visa program (for skilled immigrants) and OPT (Optional Practical Training, which eases a college student’s path to employment after graduation) either make it harder for AI experts to remain in the United States or fail to help their prospects.

The booming AI sector in the United States has greatly benefited from immigrant founders and engineers.1 Without sustaining and enhancing America’s AI talent advantage, we risk ceding ground to China in a competition with significant national security implications.

By the numbers

Estimates vary widely, but there are between about 50,000 and 200,000 AI jobs in the United States.2 Workers employed in these jobs are in high demand, with postings accelerating by the day.3

And as the sector becomes more and more important, the share of H-1B workers who work in AI has also climbed.

Using the most recently available FOIA data on H-1B approvals, we estimate that nearly a thousand workers who received H-1Bs in 2024 work in AI-related occupations,4 representing 1.12 percent of all approvals that year.

That figure may not sound huge, but this share far exceeds AI’s presence in the broader American workforce, in which AI jobs account for only 0.06 percent of all jobs.5 (The AI share of H-1B approvals also does not include university researchers in AI, for which an uncapped, or theoretically unlimited, number of H-1Bs can be issued.)

These annual flows of H-1B workers into AI are starting to add up. Using the share of approved H-1B Labor Condition Applications (LCAs) — a prerequisite for filing an H-1B petition — we estimate that H-1B workers now represent 4.3 percent of the nation’s total AI workforce.6

Nearly four out of five new H-1B holders working in AI also completed their education in American universities, compared to 52 percent of H-1Bs overall. Losing many of these workers to China would undermine the domestic AI industry and could threaten American national security.

In the small but rapidly growing AI labor market, the addition of 1,000 high-skilled workers each year would have an outsized impact on American competitiveness. With reforms to the H-1B visa selection process, we could attract even more AI experts.

How to Triple the Number of AI Workers on H-1Bs

Had H-1Bs been selected by EIG’s proposed wage-ranking system in 2024, the number of AI workers admitted would have more than tripled, rising to 3,330.7 A shift of that magnitude would amount to a meaningful expansion of the American AI talent base.

H-1B workers in AI are already well compensated, earning a mean wage of $150,000 — 37 percent above the already high average wage of H-1B holders broadly. A wage-ranked selection system would boost their mean wage even higher to $169,000.

Beyond their direct contributions to AI development, each worker generates substantial fiscal returns: the average federal fiscal impact of current H-1B AI workers is $38,000 per worker, rising to $44,000 under a wage-ranking system.8

Another way to clearly see the superiority of the wage-ranking model is to simulate what would have happened if it had already been adopted in the past. Under wage-ranking, the share of new H-1B visa recipients in AI would have been 3.9 percent in 2024 rather than 1.1 percent.9

The current H-1B lottery-based system favors large tech firms and relies on easily-manipulated occupational classifications.10 By reflecting actual market demand signaled through wages, a ranking system would satisfy frontier AI startups’ hunger for talent, generate positive fiscal impacts for the American people, and boost American innovation into the future.

Data through 2025 reveals that manufacturing jobs growth over the past six years has been highly concentrated. Over four-fifths of this employment growth has occurred in just 354 out of 3,143 counties. These high-growth counties accounted for around a quarter of 2019 U.S. employment in the sector. Areas that have sustained such growth into 2025 tend to have high dynamism and low pre-existing manufacturing dependence. Many legacy industrial cities continued to struggle.

Where are the new manufacturing jobs?

In the map below, pink-shaded counties, representing 62.3 percent of manufacturing employment in 2019, saw zero or negative growth between 2019 and the first three quarters of 2025. National recovery in manufacturing after 2020 was therefore driven by blue-shaded counties with positive net job growth. The high-growth counties with over 350 net new jobs accounted for 26.5 percent of national manufacturing employment pre-COVID but have contributed 83 percent of growth since then.

Aside from Florida, where most counties experienced gains in manufacturing employment, and the New England states, where nearly all counties lost manufacturing jobs, most states exhibit a common pattern: job losses in rural counties and large metropolitan cores, and gains in small cities and suburban areas.

Between 2019 and the first three quarters of 2025, 95.6 percent of net manufacturing jobs growth at the state level was concentrated in just twelve states, which can be grouped into three regions: the Southeast, Great Plains, and Mountain West.

The southeastern states of Florida, Georgia, Alabama, and South Carolina together account for 35.8 percent of state-level manufacturing jobs growth. “Superstar” manufacturing counties in this region are anchored by existing heavy-industrial clusters such as Huntsville–Decatur, Alabama; Spartanburg, South Carolina; and Savannah, Georgia, with growth driven primarily by expansions in the aerospace and automotive industries.

Florida stands out for having the largest share of counties with significant manufacturing increases. The state’s strength stems partly from its comparatively high dynamism in the sector, such as robust firm entry and job churn. In 2019, the manufacturing startup rate in Florida was 6.9 percent, compared with 4.7 percent nationally. Subsequently, Florida’s startup rate in manufacturing increased steadily each year, reaching 8.1 percent in 2023. By contrast, the national startup rate in the sector rose briefly to 5.3 percent in 2022 before falling back to 4.8 percent in 2023.

Texas added the most manufacturing jobs of any state, accounting for over a third of the country’s manufacturing employment growth since 2019. Manufacturing expansion in Texas spans multiple industries and is concentrated around the four largest metropolitan areas: Dallas–Fort Worth, Houston, San Antonio, and Austin.

The three states in the Lower Missouri River Basin, Kansas, Missouri, and Nebraska, also saw notable manufacturing job gains. Growth is concentrated in the suburbs of large metropolitan areas such as Kansas City, as well as in small and mid-size urban centers like Garden City, Kansas, and Columbia, Missouri. Alongside their traditional strengths in food processing and aerospace manufacturing, these states have also seen substantial job gains in chemicals, pharmaceuticals, machinery, and electric vehicle components.

Finally, four Mountain West states — Arizona, Nevada, Utah, and Idaho — recorded both large absolute gains in manufacturing employment and percentage increases exceeding 8 percent. Semiconductor manufacturing in Arizona’s Phoenix–Tucson corridor and Boise, Idaho (home to Micron), benefited from CHIPS and Science Act funding alongside rising global demand. Nevada increased manufacturing employment by 13.4 percent between 2019 and 2025, partly driven by expansion of the existing Tesla Gigafactory in the Reno metro area. Smaller urban centers such as Idaho Falls, Idaho, and Cedar City, Utah, experienced growth in chemical manufacturing, while suburbs of Salt Lake City saw a pronounced expansion in pharmaceutical production.

Has manufacturing growth held up?

Comparing manufacturing employment levels between the 2019 annual average and the 2025 Q1–Q3 average masks key dynamics in the intervening quarters. After the 2020 recession, manufacturers benefited from rebounding demand, monetary easing, federal investment, and the resumption of global trade. However, as the economy began to cool in 2022, manufacturing startup and job-turnover rates began declining, indicating that the brief upswing in dynamism has largely eroded. Supply chains have also faced disruptions from tariffs and armed conflicts.

Nationally, manufacturing employment has slipped from its post-pandemic peak of 12.9 million in the third quarter of 2023 to 12.6 million in the third quarter of 2025.

The map above shows which core-based statistical areas (CBSAs)1 are continuing to grow their manufacturing employment and which are past their post-2019 peak levels. CBSAs that sustained manufacturing growth into 2025 expanded employment by 12.9 percent, outstripping total workforce expansion by 3.1 percentage points and raising manufacturing’s share of total employment by 0.2 percentage points. Yet these growth centers account for only 13 percent of U.S. manufacturing employment as of 2019, demonstrating a high degree of concentration.

Among states that added substantial manufacturing jobs, sustained growth was concentrated in specific metro areas. Of the 67 Texas CBSAs that increased manufacturing employment, 23 continued to expand into 2025, accounting for 45.8 percent of the state’s manufacturing workforce in 2019. An overwhelming 95 percent of Nevada’s pre-pandemic manufacturing employment was in CBSAs that maintained growth through 2025. In contrast, Arizona’s CBSAs in the Phoenix–Tucson corridor experienced rapid semiconductor and EV-driven expansion but reached peak manufacturing employment in 2023. Only 0.5 percent of the state’s 2019 manufacturing jobs were in areas that maintained their post-pandemic growth trajectory.

The time series of manufacturing employment in CBSAs that are still growing and have not yet reached their employment peak reveals a critical turning point in the post-pandemic recovery. Employment in these growing CBSAs continued to rise through mid-2022 despite reversals in a few sparsely populated areas.

Beginning in the third quarter of 2022, however, the series experienced a sharp decline as increasing numbers of CBSAs reached their peak employment levels and exited the sustained growth category. This inflection point coincides closely with the Federal Reserve raising interest rates throughout 2022 to combat inflation. The tightening of monetary conditions, together with a slowdown in the broader economy, likely curtailed post-pandemic workforce expansion across many manufacturing businesses and contributed to the widespread peaking of employment levels observed thereafter.

The seeds of manufacturing divergence were planted before the COVID recession. In 2019, a quarter of manufacturing firms in metropolitan areas2 with employment growth into 2025 were younger than five years old, compared to 22 percent for metro areas that grew but reversed course and 20 percent for those that never recovered. This higher concentration of young firms suggests stronger entrepreneurial capacity in the years leading up to 2019, which likely boosted startup formation after the pandemic shock. Startup rates confirm this pattern: sustained-growth areas consistently had a larger share of new manufacturing firms between 2019 and 2023.

Metro areas that sustained growth into 2025 had the highest manufacturing establishment entry rates from 2019 to 2023 compared to the other two categories. While entry rates declined after 2022 across most metro areas, corresponding to employment peaking nationwide, the decline was least pronounced in metropolitan statistical areas (MSAs) with sustained growth, falling by only 0.5 percentage points while areas that reversed course or declined saw entry rates fall by more than twice as much.

Those with consistent growth into 2025 also show higher establishment exit rates, indicating elevated churn and dynamism. However, exit rates vary less across metro area categories than entry rates, suggesting ease of entry for new manufacturers likely contributed more to divergent growth paths than factory closures.

Our examination of post-pandemic manufacturing employment trajectories reveals high geographic concentration and widespread reversal of gains. Existing factories hire more workers during periods of high demand, monetary expansion, and government investment, but new manufacturers and plants appear to be crucial for enduring employment gains.

An American manufacturing renaissance requires understanding the policy conditions that enabled higher dynamism in areas with sustained manufacturing growth. We already know some levers that would work: removing regulatory hurdles for opening new factories, making it cheaper and easier to build workforce housing, and raising labor mobility through right-to-work laws and noncompete bans.

Policymakers must take dynamism seriously as a prerequisite for resilient manufacturing ecosystems. And we have shown the places that could offer a few lessons.

H-1B immigration has long been proven to boost innovation, entrepreneurship, and America’s macroeconomic outcomes. New research from EIG’s Adam Ozimek and Sarah Eckhardt shows that it also provides a shot in the arm for America’s fiscal woes.

In a report we published on March 17, Adam and Sarah find that the average H-1B household contributes $30,050 net each year to the federal budget. That’s 2.6 times the $11,530 contribution of a typical U.S. household.

Adam and Sarah furthermore break down the fiscal benefits for all 50 states and the District of Columbia. In 49 states, H-1B households are net fiscal positives, contributing an average of $5,040. These benefits are evident even in low-income states like Mississippi and West Virginia, which draw net fiscal impacts from H-1B households of $4,600 and $5,780 respectively.

As EIG has argued previously, the H-1B program leaves much room for improvement. This report models the fiscal impact at the federal level and the state level of implementing two high-leverage reforms: authorizing H-1B holders’ spouses to work in the United States, and changing the H-1B selection process from a lottery to a wage-rank system. Combining our two reform proposals would more than double the positive fiscal impacts, to $65,020 at the federal level and $10,650 at the state level.

Check out the full report, a handy one-pager, and an accompanying Agglomerations post here.

Policy

• Washington State became the fifth state to impose a full ban on noncompete contracts, which will take effect next year. The bill passed with bipartisan support and follows a 2020 reform that banned noncompetes for middle and low wage workers. EIG released a statement in response, quoting CEO John Lettieri: “Washington State’s action marks a significant step forward in restoring dynamism and opportunity in the labor market. Noncompete agreements prevent workers from taking better jobs, limit their earning potential, and impede the formation of new businesses. By moving to curb their use, Washington is helping to unlock opportunities for workers while strengthening competition and innovation across its economy.”

• Following the launch of the new EIG housing vertical in February, we are delighted to announce the members of our new Housing Advisory Council:

  
  Scott J. Alter, Co-Founder and Principal, Standard Communities
  Alex Armlovich, Housing Program Officer, Coefficient Giving
  Alfonso Costa, Jr, COO, Falcone Group
  Arpit Gupta, Associate Professor, NYU Stern
  Bobby Fijan, Co-Founder, Building Housing for American Families
  Colin Higgins, Executive Director, National Housing Crisis Task Force
  Edward Glaeser, Professor of Economics, Harvard University
  Emily Hamilton, Senior Research Fellow, Mercatus Center, GMU
  John Zeanah, Chief of Development and Infrastructure, City of Memphis
  Michael Novogradac, Managing Partner, Novogradac & Company LLP
  Mike Kingsella, CEO, Up for Growth
  Paul Williams, Founder and Executive Director, Center for Public Enterprise
  Sean Rust, VP of Development, NexMetro Communities
  Stephanie Kestelman, Director of Housing, Arnold Ventures

  ---

Research & Analysis

AI and Young-adult Jobs: The Real Mystery
To understand what’s happening in the labor market for young adults, it’s necessary to look at the employment rate for college and non-college workers. Both are lagging behind the employment rate for adults older than 25. Something mysterious is happening, but as Adam Ozimek and Nathan Goldschlag write, this does not seem like a story about AI displacing entry-level college graduates.

Place and Male Educational Attainment Gaps
There is significant educational attainment variability between men and women within the nation’s most distressed counties, writes Sarah Eckhardt. She investigates plausible reasons and finds: “We can see that on average, counties that have large black populations, are located in the south, are rural, or were a manufacturing hub in the ‘90s, have a more negative gap [female minus male high school education rates] than those that are not. Those that have a higher-education institution located in the county, or where a large share of households with children are run by a single parent, have a more positive gap.”

How the Housing Market Split in Two
“New homeowners are now burdened with some of the highest housing costs in decades,” writes Jess Remington, “while existing homeowners — those who have owned their homes for longer than a year — are actually spending less on housing than in the past.” The new homeowner penalty, which refers to housing costs for new homeowners above those paid by existing homeowners, hit a 34-year high in 2024. Just as worrying is that too many of the housing policies favored by policymakers would widen the gap even further.

Does a legacy in manufacturing preclude a future in it?
Kenan Fikri looks at the economic geography of manufacturing activity across the country and finds that new manufacturing is now “avoiding hubs of established prowess and instead gravitating towards places with little history in manufacturing. This tendency is not new. A fact of modern U.S. manufacturing appears to be that it trends towards deagglomeration.” Among other discoveries, Kenan also finds that the United States “has only 1.5 young manufacturing firms per 10,000 people today — a figure that has been bumping along at all-time lows for 15 years and counting.”

The New Bazaar

The Roots of our Zero-Sum Moment
Stefanie Stantcheva is an economist at Harvard and the head of the Social Economics Lab, where her team has done extraordinary work investigating how people form their opinions about economic and political topics. She speaks with Cardiff Garcia about the findings in her paper (with co-authors Sahil Chinoy, Nathan Nunn, and Sandra Sequeira), “Zero-Sum Thinking and the Roots of US Political Differences,” which was just published in the American Economic Review.

Stefanie and Cardiff discuss:

• Zero-sum similarities and differences between Democrats and Republicans

• The economic geography of zero-sum thought

• The finding that surprised her the most

• The generational gap in zero-sum thinking between young and old

• The policy implications of her research

• A preview of her upcoming work on zero-sum thinking and AI

Ideas for a Post-YIMBY Housing Future
Arpit Gupta, a finance professor at NYU, speaks with Cardiff about his latest contributions to the study of housing affordability, remote work, artificial intelligence, and finance. Arpit has offered a variety of reform proposals that both complement YIMBY ideas and also prepare for a future after a YIMBY victory. Among them are re-thinking property taxes, accelerating depreciation schedules, and making it easier for factory housing to get to market.

Around the Horn

• Using the story of the player piano, Adam Ozimek writes in The Atlantic on the durable demand for bespoke, human labor even as technological change would appear to make it obsolete.

• Emily Peck in Axios cites Jess Remington’s Agglomerations post on the housing market’s splitting in two.

• Eric Levitz in Vox cites EIG’s recent publication from Google economists Zanna Iscenko and Fabien Curto Millet on how to interpret LLMs’ impact on the economy.

• In the Washington Post, columnist George Will cites Sarah Eckhardt, Ben Glasner, and Connor O’Brien’s geographic analysis of recent electoral politics.

• Also in the Washington Post, Kevin Schaul and Shira Ovide cite EIG’s research as a counterweight to the runaway narrative that AI is harming the labor market for young college grads.

• And Jordan McGillis makes that same case in National Review, arguing that college grads are markedly better off than their non-college peers.

• The Seattle Times cites EIG’s work on noncompetes as the state of Washington prepares a noncompete ban.

  ---

EIG Chart of the Month

Via Jess Remington’s post on how the housing market split in two:

EIG Video of the Month

Ben Glasner riffs on the work of Stefanie Stantcheva, as discussed recently on The New Bazaar. See more EIG videos here.

The first post in this series documented U.S. manufacturing’s stasis ever since the Great Recession of 2008 — low rates of job creation and job losses, low rates of job turnover, and low startup rates.

This post similarly peers under the surface of the sector to examine what geography and business dynamism, together, can tell us about the health of American manufacturing today.

The data paint the picture of new manufacturing activity — in the form of new establishments and new firms — avoiding hubs of established prowess and instead gravitating towards places with little history in manufacturing.

This tendency is not new. A fact of modern U.S. manufacturing appears to be that it trends towards deagglomeration.

What do these location choices reveal about the drivers of U.S. competitiveness today? Why is more manufacturing in an area associated with less manufacturing dynamism? And why does having a legacy in manufacturing seem to make it harder to build a future in it?

The answer to these questions might hold the key to American re-industrialization.

Deagglomeration nation

The total number of manufacturing firms nationwide fell slightly from 2020 to 2023, as it has consistently since about 1998.1 This decline was not pervasive, however. It was primarily due to the slow but steady hollowing out of the nation’s existing manufacturing hubs.

Nikhil Kalathil of Carnegie Mellon and coauthors developed a framework that classifies counties based on the nature of manufacturing sector agglomeration within them. Covering 1,800 counties with sufficiently sized manufacturing bases, the authors identify four different archetypes of agglomeration:2

• High agglomeration areas where firms locate with both peers and suppliers

• Peer (horizontal) agglomeration areas where firms locate with others in similar industries and similar positions in the supply chain

• Supplier (vertical) agglomeration areas where firms locate within a particular supply chain

• Low agglomeration areas where firms locate with few peers or suppliers

High agglomeration counties remain home to the bulk of the nation’s manufacturing firms and jobs, but they lost over 1,300 companies on net between 2020 and 2023. By contrast, low agglomeration (+424), peer (+38), and supplier (+74) agglomeration counties all added to their manufacturing bases on net.

In percentage terms, high agglomeration counties shed 0.7 percent of their manufacturing firms, while firm counts grew by 1 percent in low and peer agglomeration counties.3 Firm dynamics — the constant churn of openings and closings — are gradually pulling the sector’s center of gravity out of traditional hubs.

Economic Geography 101, revisited

At first glance, this trend towards deagglomeration is surprising because fundamental precepts of economic geography around industry clustering and agglomeration — from Marshall (1890) to Krugman (1992) — were forged in a goods-producing world.

In these canonical frameworks, firms co-locate to take advantage of information flows and knowledge spillovers, tap deep pools of specialized labor, reduce transportation costs, and build relationships.

Part of the magic of agglomeration is that it should spur dynamism by its very nature. Mash up a critical mass of complementary people and firms, thinkers and doers, innovators and imitators and they will unlock progress.

If expertise is an advantage and new commercial opportunities are more perceptible with proximity, you’d expect new businesses to start where an industry already has a presence, too.

In the economists’ jargon, agglomerations emerge because they offer firms increasing returns.

That is, until they don’t, at which point they start offering diminishing returns instead.

Diminishing returns can set in with size. As agglomerations grow, so do congestion costs, which include traffic and high prices of land and labor.

Diminishing returns can also set in with time. As firms and industries mature, they tend to seek out more generic and less specialized locations, as they rely less on innovation and agglomeration-based advantages to stay competitive and more on driving down costs of production.

Agglomerations may grow less dynamic over time, too, as a sort of spatial industrial sclerosis develops and winning firms eventually grow older and bigger, workforces age, technologies get locked in, vested interests accrue, and entrepreneurial vim fades.

A map of attrition

Manufacturing’s deagglomeration should be interpreted through this framework as a symptom of a sector that has lost its dynamism. The series of state-level scatterplots below show how decline and deagglomeration have proceeded together.

Each dot represents a state, sized by manufacturing’s share of the workforce. The y-axis represents the startup rate, or the share of all manufacturing firms in the state that started in the past year. The x-axis represents the death rate, or the share of all manufacturing firms that shuttered in the past year. Values are averaged for each decade. The 45-degree line represents balance, where each dying manufacturer is replaced by one new one. States above the line enjoy more manufacturing firm births than deaths; states below, the opposite.

The steady march of states below the 45 degree line shows how manufacturing has faded. The mainly vertical progression shows that American manufacturing has adjusted to economic change on the entry margin — that is to say that dynamism has fallen because the startup rate has collapsed while the rate of failing firms has remained largely unchanged. And the startup rate’s collapse has had a profound impact on the states in which manufacturing constitutes a bigger share of the economy (larger points in the graph) by pushing them more deeply into that attrition territory.

As a result, manufacturing is deagglomerating because the only places still experiencing net entry are those with less of a manufacturing base to start. The sector’s heartlands have lost the ability to launch more firms than they lose each year.

The finding holds for counties and at the establishment level too. Looking at even more recent QCEW data for the past three years, the number of new manufacturing establishments — which includes new firms and branch plants or new outposts of existing firms — increased by 4.3 percent in counties that had no specialization in the sector, compared to around 2 percent for counties moderately specialized in it and a decline of -0.1 percent for counties significantly specialized.4^,5

We’ve become so familiar with new manufacturing establishments opening in empty fields that it’s easy to overlook the shocking revelation in these figures.

Places with large and diversified manufacturing bases appear to be less conducive to startups and less attractive to expanding firms than places that represent a blank slate.

On the one hand, this tendency might attest to the comparative strengths of new locations unsaddled by a legacy in the sector. But on the other hand, it signals that something has gone deeply wrong in our agglomerations.

But what is it? What mix of factors have conspired to send American manufacturing agglomerations-first into the dynamism doldrums?

The sector has been battered by automation, off-shoring, and foreign competition, of course. But local factors, and how places respond to economic shocks and technological change, matter too.

To see the future of manufacturing in America, we need to look beyond today’s companies and ask why new ones aren’t waiting in the wings. We need to look past surface-level decline and into the dynamics of resilience and renewal at the local level.

The stakes are high, because until we have a better understanding of what holds manufacturing back in the places that embody our national expertise in the sector, the country risks continuing down a quixotic and futile path of implementing industrial policy without the industrial base.

Searching for startups

If there’s one measure that best symbolizes renewal, it is startups. New firms with new technologies, products, or business models to replenish the stock of enterprises that inevitably thins through the course of economic churn and change.

Normalized by population, the country has only 1.5 young manufacturing firms per 10,000 people today — a figure that has been bumping along at all-time lows for 15 years and counting. Zooming out, that means for every 1 million Americans, there are only 150 manufacturing firms of any size or speciality that have launched within the past five years.

Bright spots

Those young firms, few as they may be, point to enduring advantages of making it in America, and they have helped bolster an embattled sector.

What is more, dozens of metropolitan areas are nurturing new manufacturers at much higher rates than the country overall.

These include:

• Major metropolitan engines such as Los Angeles, CA, and Miami, FL.

• Competitive manufacturing clusters such as Elkhart, IN, and Holland, MI

• Mid-sized micropolitans like Cookeville, TN, and Somerset, PA

• Emergent western production hubs such as Burley, ID, Evanston, WY, and St. George, UT

• Mid-sized advanced technology centers such as Boulder, CO, and Burlington, VT.

Those bright spots point to a real competitive advantage that explain how the country remains a technological and, yes, a manufacturing superpower. But they remain the exception. Most major metros and historic manufacturing clusters track the nation.

Limited startup activity reigns along much of the East Coast, large stretches of the Southeast, and even midwestern metropolitan areas such as Columbus, OH, Indianapolis, IN, and Pittsburgh, PA, with world-beating research universities and longstanding efforts to integrate leading edge innovation into legacy manufacturing bases. Boomtowns such as Atlanta, Dallas, Nashville, and Phoenix trail the nation on spawning new manufacturers, too.

Los Angeles is a particularly interesting case study. Its manufacturing sector remains more entrepreneurial than most other major cities. The region is garnering renewed attention as it builds on its aerospace roots to become a center of defense-related “hard-tech.” Alumni from SpaceX and other firms are launching new manufacturing startups in the classic spinout process that makes strong clusters (greased, in this case, by California’s prohibition on noncompete agreements). Such entrepreneurial ferment is a key ingredient in dynamism.

Those advanced manufacturing startups attest to the area’s strengths in risk capital, know-how, and talent. They prove that the location itself still has the power to inspire entrepreneurship.

And yet, the number of young manufacturers in metro Los Angeles fell to its lowest level in decades in 2023.

This larger sectoral trend in the region attests to the area’s weaknesses, notably high costs across the board for both firms and workers.

What happens when congestion costs overpower the forces of agglomeration? People and businesses leave. Firms fail to start. The magnetic pull and inherent dynamism of a place like Los Angeles is dampened, leaving manufacturing in the nation’s second metro area smaller and less innovative than it could be.

Virtuous restoration

Los Angeles tells the national story. Metropolitan areas performing below their potential, summing up to a nation performing below its potential, too. Agglomerations past their prime and struggling to battle decay. Agglomerations that enervate rather than invigorate dynamism.

The spread of manufacturing itself is not inherently negative. The sector is an engine of economic development and opportunity for the communities into which it enters. Manufacturers have a long history of making location decisions to avoid having to compete with other firms for labor. Corners of the United States have real comparative advantages based on the costs of land, labor, and energy. New clusters may be forming in some of these low-agglomeration areas, too.

Deagglomeration is only a problem insofar as it is a symptom of the poor health of the nation’s manufacturing heartlands. That is exactly the diagnosis presented here.

Industrial policy now captivates both parties. The federal government has pledged trillions in subsidies to big firms to make it in America. The Trump administration has tried to fundamentally reset the terms of trade with tariffs. Entrepreneurship has been almost completely neglected. The low- to no-cost work of dismantling barriers to commercializing innovations and growing new firms has been largely ignored. Fundamental questions about how to strengthen U.S. competitiveness — and the regional foundations of U.S. competitiveness — remain unanswered.

To put it plainly: The nation will fail to activate a manufacturing renaissance without revitalizing innovation and entrepreneurship within its established agglomerations.

Luckily, there’s no shortage of ways to get started. Ban noncompete agreements so that nimble new firms can spin-out from lumbering old ones. Liberalize housing construction so that more talented people can afford to live in our most productive regions. Streamline regulations so that redeveloping brownfield sites can be cost- and time-competitive with building on greenfield ones.

And the best part of all is that local, state, and national leaders can all do their bit to make progress.

Geography and dynamism, together, help explain how manufacturing in America arrived in its current state. They also show how the sector can climb out of it.

In a new EIG report, my colleagues Adam Ozimek and Sarah Eckhardt demonstrate that H-1B immigration has an immediate and positive fiscal impact. Ozimek and Eckhardt analyze the taxes that H-1B immigrant households — the most numerous in the high-skill category — pay to federal, state, and local coffers, and the benefits they draw from the public budget. They find that each H-1B household has an average positive federal fiscal impact of $30,050 per year, a figure that is 2.6 times greater than that of the average American household. Moreover, at the state-and-local level, the average H-1B impact is over $5,040 annually.

In other words, oft-maligned H-1B workers are plugging the federal deficit and replenishing strained state finances. How?

H-1Bs are remarkably net-positive for two main reasons. First and foremost, they make a lot of money. H-1Bs are very well-paid compared to the average worker because they work, almost exclusively, in high-wage, in-demand sectors like tech and medicine. With an annual income of $130,075, the median H-1B worker earns at the 91st percentile nationally.

The second reason H-1Bs contribute so robustly to government finances is demography. More than 60 percent of H-1B recipients are under the age of 35, which means that H-1Bs tend to consume very little in the way of public services.

Demography does, though, cloud the fiscal impacts of one subset of H-1B workers: those with a dependent spouse and children, a group that makes up 25 percent of H-1B households. While they are still net-positive at the federal level, H-1Bs who bring with them a spouse and children are a net fiscal negative at the state and local level due to state spending on public education.

Critically, this negative fiscal impact mirrors that of Americans of similar age, income, and fertility. Until reaching an income of $250,000, single-earner American families with children, too, are net fiscal negatives at the state level. All of these facts point to a promising reform: authorizing the spouses of H-1Bs to work too.

Reforming the H-1B program to include work authorization for all H-1B spouses — who themselves enter the country on H-4 visas and are generally barred from working — would amplify positive fiscal impacts and reduce the fiscal drag introduced by the one-quarter of H-1B households that currently have children and just one earner. Because H-1B spouses tend to be comparably skilled to the visa holders, our report finds that authorizing them to work would boost the average positive fiscal impact of an H-1B household by 50 percent, to $45,850 at the federal level and $7,670 at the state-and-local levels. Our report also finds that by authorizing work for H-1B spouses, even H-1B families with children would become net fiscal positives in most states.

Another high-leverage reform opportunity is to alter the way we select H-1Bs. The current system is a pure lottery, provided H-1B applicants meet the basic educational and occupational criteria. We propose ending the lottery and enhancing the composition of H-1B cohorts by selecting applicants based on a wage ranking system that rewards age-adjusted earnings. Wage ranking the selection process would alone boost the fiscal impacts per H-1B to $48,700 at the federal level and $7,980 at the state-and-local levels.

Together, selecting H-1B applicants based on wage ranking and authorizing their spouses to work would push their net fiscal impacts each year to $65,020 at the federal level and $10,650 at the state-and-local levels.

The H-1B program has taken heat from conservatives and progressives alike in recent years, yet this report shows that the roughly 700,000 H-1B visa holders in the country at this time are of enormous fiscal value, generating a cumulative federal net fiscal impact of around $20 billion each year. Our proposed reforms would double that figure to $40 billion and provide an ongoing revenue stream, unlike the Trump administration’s one-time $100,000 H-1B fee, which does not directly address the program’s composition problem. It’s important to note that this profound, direct, near-term fiscal impact does not even factor in the tertiary economic benefits of the program that stem from increased innovation, entrepreneurship, and productivity growth. These well-documented spillovers indirectly boost tax revenues even higher in the long term.

As the nation careens toward fiscal crisis, the H-1B program should not only be judged on innovation grounds or on cultural grounds, but on the unifying goal of getting our federal, state, and local budgets in order. Improving the program by picking the best applicants and authorizing their spouses to work is a rare, politically-palatable way to plug fiscal gaps.

The national housing market in recent years has become increasingly bifurcated. New homeowners are now burdened with some of the highest housing costs in decades, while existing homeowners — those who have owned their homes for longer than a year — are actually spending less on housing than in the past.1

If policymakers want to solve the affordability crisis, they first need to understand who it’s actually hurting. Too many of the proposals with strong political momentum — mortgage rate cuts, property tax breaks for seniors — do nothing to address the root causes and are outright regressive, directing relief toward those least affected by the crisis.

The Problem: A Housing Market That Rewards Incumbency More Than Ever

It has always been true that new homeowners spend a bit more of their income on housing than long-time owners do. New homeowners, after all, tend to be younger and have lower incomes, and their mortgage payments are larger because they bought more recently.

But in roughly the last four years, the difference in housing costs as a share of income between new and existing homeowners has grown well beyond the historical norm, leading to what we might call the new homeowner penalty.2

Historically, monthly housing costs for new and existing homeowners have tended to move in tandem. From 1990 through the aftermath of the Great Recession, both groups saw costs rise during booms and fall during downturns, with the gap between them remaining relatively stable at two to four percentage points, even in periods of volatility like the mid-2000s housing bubble.

That pattern briefly reversed during the Great Recession, when new buyers were able to purchase homes at depressed prices and consequently spent slightly less of their income on housing than existing owners. By 2017, the typical two-point gap had returned.

The current divergence began in earnest in 2022. By 2024, new homeowners were spending 26 percent of their income on housing, compared to 20 percent for existing homeowners — a six-percentage-point gap, the largest in nearly 40 years. Although new homeowners spent a slightly larger share of their income on housing at the peak of the housing bubble in 2007 (28 percent), the gap with existing homeowners was smaller (four percentage points). Even at the height of this century’s other housing affordability crisis, the housing cost burden was less unequal.

Importantly, this imbalance starts even before homeownership begins. Since 2021, prospective buyers have faced historically high down payment costs. Over the past four decades, the real cost of a down payment has significantly outpaced the growth in household income. Adjusted for inflation, the average down payment has nearly doubled since 1980, while average household income has grown by less than half of that, around 42 percent.

Since 2019, the real average downpayment has risen by more than 29 percent, while inflation-adjusted average and median household incomes have flatlined.3 As a result, buyers need to save for significantly longer just to enter the homeownership market.

These pressures spill over into the rental market as well. New renters — those who have moved within the past year — are also paying a record-high share of their income on housing, further underscoring how affordability challenges are concentrated among those with the least tenure in the housing system.

Taken together, these trends help explain the long-term decline in homeownership among younger and lower-income households. According to an Urban Institute analysis, the homeownership rate for 35-to-44-year-olds has fallen by more than 10 percentage points since 1980. Over the same period, all but the highest-income households have experienced similarly large declines in homeownership.

What emerges is a housing market that increasingly allocates costs and protections based on tenure. Existing homeowners are largely buffered by low interest rates locked in before or during the pandemic, growing home equity, and limited exposure to rising prices. New buyers, by contrast, are facing the dual constraint of elevated home prices and higher interest rates. As a result, affordability pressures are falling disproportionately on households that have recently entered the market or are attempting to do so now, while longer-term owners remain far less exposed.

Regressive Policies That Will Continue to Widen the Gap

The bifurcation of the housing market makes several of the prevailing policy proposals for addressing the housing crisis all the more frustrating. Rather than targeting the underlying forces that create these disparities, several of the most prominent suggestions focus on easing costs for those that are the most insulated from affordability pressures, while leaving the primary barriers confronting would-be homebuyers largely unchanged.

In effect, these proposals would further widen the gap between housing haves and have-nots.

Mortgage Rate Cuts

In a January cabinet meeting, President Trump said the best thing for both existing homeowners and people trying to buy is lower interest rates. It’s an intuitive argument: lower rates mean lower monthly payments. But the relationship between rates and affordability is more complicated than that — and the historical evidence is not encouraging.

From 2019 to 2021, mortgage rates experienced the most dramatic and rapid cut in recent history, falling by roughly 25 percent. Yet new homeowners’ real monthly mortgage payments increased, from $1,717 to $1,736.

The rate cut, in other words, was more than offset by the increase in home prices.

From 1980 through 2024, there has been a modest negative correlation between home prices and mortgage rates: when rates fall, real average home prices and down payments have tended to rise. A recent analysis by Cambridge University finds that interest rate shocks that lower borrowing costs have a significant and persistent causal effect on house prices.

Particularly in supply-constrained markets, lower rates will lead buyers to bid up prices faster than housing supply can respond. Rate cuts likely would help recent homeowners to refinance at lower rates, but it’s unlikely that they would do much to lower the barrier to entry for new buyers. President Trump may be aware of this too, as later in the same meeting, he promised to “drive housing prices up for people that own their homes.”

Property Tax Breaks for Seniors

Governor Gretchen Whitmer recently proposed a property tax deduction for senior citizens in Michigan. Amounting to a $90 million cut from the state budget, it would be the state’s largest property tax break in over a decade. Likewise, a ballot initiative is circulating in California that would eliminate property taxes entirely for homeowners over the age of 59.

These proposals aren’t unique. Many states already offer property tax relief for senior homeowners — and the terms are often strikingly generous. Some programs, like Illinois’ Senior Exemption, apply to all senior homeowners regardless of income. Others are nominally means-tested but set the bar absurdly high: New Jersey’s SAVE NJ cuts property tax bills for seniors earning up to $500,000 a year.

Setting aside that many of these programs are available to even the wealthiest seniors, the purported rationale is to address the plight of seniors living on fixed incomes. A fixed income makes seniors uniquely vulnerable to rising property taxes after their mortgages are paid off, so the narrative goes. But this obscures a crucial fact: even after accounting for their fixed incomes, senior homeowners are far less burdened by housing costs than most other groups in America, and particularly young homeowners.

Homeowners who are 65 or older paid only 17 percent of their monthly household income on housing costs in 2024. Among seniors without a mortgage — who make up roughly two-thirds of older homeowners — housing costs consumed only 13 percent of their income.

Meanwhile, young homeowners (those aged 25 to 34 years old) spent 20 percent of their monthly income on housing in 2024. Those who moved within the past year and are paying a mortgage spent 27 percent — a rate surpassed only by the 2007 peak.

Renters are even more strained: young renters spent 28 percent of their income on housing, while senior renters were the most burdened group of all, spending 37 percent.

Cutting property taxes for seniors would offer a benefit to the cohort of people that is most insulated from the housing affordability crisis in the first place.

There’s also a subtler problem worth taking seriously. Rising property tax bills aren’t the result of higher property tax rates, which have stayed largely flat across the country. They’re the direct result of rising home values, which are themselves a result of insufficient housing supply. (This isn’t idle speculation. Research has found that increasing the share of multifamily developments in a community reduces residents’ effective property tax burden.)

The effect of reducing property tax rates would therefore be not only to redistribute money upward but to weaken one of the few feedback mechanisms connecting housing scarcity to political demand for reforms that would slow the rise of home values and overall housing costs.

Seniors are among the most politically active age cohorts. Offering a tax break exclusively to this protected class of homeowners would disincentivize them from agitating for policies that would address the root causes of the affordability crisis.

What Will Actually Help Affordability for Homebuyers

Without addressing the underlying constraint of housing supply, the homeowner divide will keep widening.

Policies that tinker at the margins — subsidizing non-mortgage costs or cutting interest rates — largely benefit existing homeowners. In a supply-constrained market, demand-side interventions tend to inflate prices, transferring wealth to sellers rather than expanding access to ownership.

By contrast, supply-focused reforms target the source of the problem directly. Increasing affordability will require building far more housing, especially in high-demand metro areas where restrictive zoning, lengthy permitting, and high construction costs have produced chronic scarcity.4 Until those constraints loosen, the housing market will continue to reward those who already own and penalize those trying to buy.

Nationally, 90 percent of women and 89 percent of men over 25 have a high school diploma — close to parity. But this relatively small gap in educational attainment is significantly larger in distressed places, those with higher poverty rates, lower prime-age employment rates, lower median household incomes, among other characteristics.1

When men and women with only a high school diploma, versus those with any college education, are separated out, the attainment gaps widen further. On average, 53 percent of women in distressed counties have received at least some college education, while only 43 percent of men have — a 10 percentage point difference.

Why this matters

That overall education levels are lower in distressed places than they are in prosperous ones is not surprising, and reflects the poorer opportunities available in these places. But the larger gap between men and women in these places matters too. When men in distressed communities fall behind women educationally, it threatens their relative socioeconomic mobility, longevity, and health — and comes with political and cultural consequences.2

High school graduation is particularly important. Because high school dropouts are far more likely to be unemployed and impoverished than those with higher levels of education,3 the share of adults without a high school diploma is a meaningful indicator of disadvantage.

How did we get here?

The last 75 years have seen an extraordinary rise in high school graduation rates overall, a remarkable achievement linked to growing economic prosperity for Americans across the income distribution. As younger cohorts earned diplomas at higher rates, the overall portion of adults with diplomas increased. However, these gains have not been shared equally.

From 1950 to 1980, the share of men and women with a high school diploma rose at similar rates, with men slightly ahead in both distressed and prosperous counties. After 1980, women began pulling ahead everywhere, but the gender gap widened fastest in the most distressed communities.

Today, 86 percent of women and 82 percent of men in distressed places have a high school diploma, a gain of 37 percentage points for women versus 32 percentage points for men since 1980.

Men in distressed places lagging behind has resulted in a larger gender gap than what exists in prosperous ones. In 1980, the gender gap in educational attainment was smaller for both types of communities — differing by only one percentage point. Today, that difference between prosperous and distressed communities has grown to 2.5 percentage points, as shown in the figure above, and appears to be widening further still.

Are these education measures missing something?

The Census Bureau’s American Community Survey (ACS), where this data comes from, does not collect information on alternative educational pathways such as trade and vocational schools.4 These programs are lauded for propelling young men into highly demanded, well-paying jobs in the trades.

If many men living in distressed areas without high school diplomas have received vocational or trade education, the gender gap might simply reflect a measurement problem. The ACS may be failing to capture relevant educational pathways that have similar — or better — employment and earnings outcomes compared to a high school diploma.

But vocational school attendance has been low for decades, with no systematic gender gap. According to the Current Population Survey (CPS) education supplement, the share of both men and women between ages 16 and 30 who report being enrolled in vocational training programs has hovered between 2 and 4 percent, with a slight downward trend.

Similarly, the Survey of Income and Program Participation (SIPP) estimates that 14 percent of women and 13 percent of men 18 years and older have educational certificates — which are essentially vocational, technical, and trade degrees. And according to SIPP, less than one percent of either men or women have earned a certificate without graduating high school.

The lack of a national gender certificate gap aligns with evidence that the most popular vocational training programs are healthcare-related, a field that women dominate.5 Even so, perhaps men in distressed areas are more likely to pursue vocational degrees. Unfortunately, detailed county-level data isn’t available to confirm whether this pattern actually exists.

Yet even if we assume that 15 percent of male high school dropouts in distressed areas have vocational degrees and no female dropouts do, the gap remains. Counting these vocationally-educated men as having a high school diploma brings the male rate to 85 percent, still below women’s 86 percent.

There does not appear to be a gender gap in terms of vocational or trade school degrees. However, data from the National Center for Education Statistics (NCES) on high school career and technical education does find larger gaps in high school courses taken in these disciplines. In 2019, 87 percent of male high school graduates and 82 percent of female high school graduates took at least one of these courses.6

A larger course-taking gap suggests a second potential education pathway: that young men are accumulating enough vocational-related course credits to secure jobs before graduating high school, allowing them to sidestep the diploma.

If this were the case, it should show up in employment rates.

In general, employment rates are higher for men than for women, and higher for prosperous places than distressed ones. We can measure a geographic employment gap as the ratio of employment rates in distressed counties to employment rates in prosperous counties. If men are dropping out of high school for jobs, we would expect this distressed-to-prosperous employment ratio to be similar for both men and women. It is not.

Relative to their peers in prosperous counties, men are 24 percent less likely to be employed, while women are only 19 percent less likely — a five percentage point gap. Even if men are taking more trade and vocational courses in high school, this is not translating to improved employment outcomes.

Which people are we talking about here?

People move to seek economic opportunity. Distressed places frequently lose more-educated adults who find jobs in more prosperous areas. This migration implies two potential measurement issues for local-level educational attainment gaps:

1. Adult education levels may not reflect actual graduation rates. The share of adults over 25 with a high school diploma could be lower than the actual graduation rate if educated residents leave. In this case, adult education levels would reflect who stayed rather than who did or did not graduate. Both measures are meaningful, but since we’re interested in how growing up in a distressed county affects educational outcomes, we need to understand actual graduation outcomes.

2. Men and women may differ in geographic mobility. If men have weaker ties to place, higher-achieving men might leave distressed areas while higher-achieving women stay. This would create an apparent gender gap in adult educational attainment even if actual high school graduation rates were equal for both groups.

These concerns can be addressed by identifying which location individuals resided in when they would have been attending high school. Even with this restriction, the gender gap persists: young women who lived in a distressed place at age 18 are three percentage points more likely to have graduated high school than young men in those same places.7 This suggests that the adult education gap was not simply a product of selective out-migration, though differences in mobility may still play a role.8

The gap also appears earlier in the educational pipeline. Male non-enrollment rates among children ages 5–17 have consistently exceeded female rates, with slightly larger differences in distressed places than in prosperous ones. This further indicates that gender disparities in attainment likely begin before high school completion rather than emerging solely through post-graduation sorting.

Worryingly, the COVID-19 pandemic widened these patterns. Non-enrollment rates rose sharply for both boys and girls in distressed places, while increases were comparatively modest in prosperous areas. If these disruptions translate into lower completion rates, educational divergence between distressed and prosperous places may accelerate in the coming years, regardless of gender.

Are all distressed places created equal?

Distressed counties encompass a wide range of places — from former manufacturing hubs that declined in recent decades, to rural farming counties with poorly funded schools, to inner-city neighborhoods experiencing intergenerational poverty.

This variety shows up in the gender education gap. Across distressed counties, the gender gap in the share of adults with a high school diploma ranges widely: in some places, men are 10 percentage points ahead of women; in others, they’re 10 percentage points behind. This range indicates that there is significant educational attainment variability within distressed counties.

We can get a sense of what explains this variability by looking at differences in the mean education gap between different types of distressed places. The figure below shows the percentage point gap in these means between distressed counties that

• Have a degree-granting institution or not

• Have above-median single-parent households or not

• Have above-median manufacturing employment share in 1990 or not

• Are rural or not

• Are in the southern Census region or not

• Have above-median black population share or not

Note that as a baseline, distressed counties have a negative high school education gap (-3.6 percentage points). The coefficients displayed capture the relative difference.

We can see that on average, counties that have large black populations, are located in the south, are rural, or were a manufacturing hub in the ‘90s, have a more negative gap than those that are not. Those that have a higher-education institution located in the county, or where a large share of households with children are run by a single parent, have a more positive gap.

There is substantial variation in educational gaps within groups. The standard errors in this analysis help show whether the difference between groups (e.g., southern or not) is large or small relative to dispersion within groups. For counties with above-median black population share, those in the south, and rural counties, the difference between groups is relatively large compared to within groups.

Greater understanding of the role of economic distress for widening gender gaps in educational attainment has the potential to improve education policy.

Current education policy often focuses on improving test scores, teacher quality, and curriculum standards. These goals matter, but they may miss something crucial: how place shapes educational outcomes differently for men and women.

If distressed communities face unique barriers that particularly affect young men’s education, then effective interventions must be tailored to local economic contexts. Education policy needs to explicitly consider place — recognizing that a one-size-fits-all approach may fail students in communities where economic distress creates gender-specific educational challenges.

View the GitHub repository with code for replicating this analysis here.

The unemployment rate for recent college graduates has moved up substantially over the past two years — more than either overall unemployment or unemployment for young workers without degrees. It is therefore easy to understand why so many people have begun wondering if AI is to blame.

Are they right? Has AI started automating away the entry-level knowledge work of skilled workers?

Digging deeper

The biggest weakness of this story is its exclusive focus on the unemployment rate.

The unemployment rate only counts someone as unemployed if they are actually looking for work. The problem with this becomes obvious when glancing at the labor force participation rate, which shows how many people are either working or looking for work — the labor force includes both — as a share of the total given population.

Over the last year, non-college young adults aged 22–25 have disproportionately given up looking for work. These discouraged workers won’t count as unemployed, which means that the unemployment rate is lower than if it did count them, a misleading sign of labor market health. Young college-educated workers, on the other hand, are now participating in the labor force at about the same rate as they were in the middle of 2023.

And young people in general are lagging behind the rising participation rate among all workers — perhaps suggesting a common cause behind the labor market struggles of both college and non-college young adults alike rather than something that only damages the prospects for college grads.

The labor force participation rate is itself imperfect, however. It does not change when workers lose their jobs and become unemployed, as unemployed workers are still part of the labor force. Though a useful complement to the unemployment rate, any indicator that fails to capture workers going from employed to unemployed is clearly insufficient for understanding labor market health.

The right analysis

For a more apples-to-apples comparison — one that avoids these data issues and best shows how the labor market outcomes of young college graduates compare to those of young people without degrees — the right measure is the share of all 22- to 25-year-olds (working, searching for work, or out of the labor force) who are working. This measure is known simply as the employment rate.

It has at least a couple of key advantages. Unlike the unemployment rate, the employment rate does not show a deceiving improvement if workers become discouraged and stop looking for work. And unlike the labor force participation rate, it successfully captures workers moving from employed to unemployed.

It turns out that employment rates have declined for young adults both with and without degrees. If anything, those without degrees have actually endured marginally worse labor market outcomes. And similar to the trend observed in labor force participation, growth in the employment rates of young adults of all education levels have lagged behind those of all other workers.

More Joes and Janes College?

There is one other lingering measurement issue to address. Many young individuals who are not working are doing so because they are in college. While labor market conditions can affect the decision of whether to attend college, going to college is not the same thing as unsuccessfully seeking employment. If, for example, a rising share of non-college young adults are not working specifically because more of them have chosen to go to college, then their depressed employment rate might be taken as an inaccurate signal of problems in the labor market. All of which suggests a useful tweak to the employment rate — excluding those attending school to see if recent enrollment rates have changed enough to affect employment trends. We have done so in Figure 4:

As should be clear from viewing Figure 4, the trends are nearly the same. In fact, when excluding those in school, outcomes for non-college youth are actually a bit worse relative to college graduates than when including young adults in the measure.

The absence of a big effect from taking college students out of the calculation is not surprising, as college enrollment rates for this age group are about where they were three years ago, with only small fluctuations since:

Identifying the real mystery

A growing body of complex econometric studies is now examining the impact of AI on the labor market.1 This work is valuable and essential. But before we begin searching for explanations, we first need an accurate understanding of the patterns being explained. Young workers of all education levels are lagging the rest of the labor market. Focusing too much on education rather than age as the main labor market weakness starts us in the wrong direction.

So is AI nonetheless to blame for the broad-based weakness in the labor market for young people? It’s true that some lower-skilled jobs can be replaced by AI. Call center workers and data entry jobs are potential examples. But there are not enough of these jobs to really drive the youth labor market. And this explanation certainly does not fit the media narrative focused on AI displacing computer science majors and entry level college graduates.

Just what exactly is causing young workers to be left behind is a genuine economic mystery. But to solve a mystery, we must first accurately identify it. The whodunnit is not about recent college graduates, but about young people of all types.

View the Github with code for replicating this analysis here.

A dynamic economy needs a dynamic housing market. America doesn’t have one. This month EIG launched a new housing policy vertical to help rectify that.

Housing costs are pricing families out of opportunity-rich regions, distorting labor markets, and weakening national growth. EIG argues that affordability requires large-scale supply expansion across all housing types, not just targeted “affordable housing” programs. While zoning barriers are local, federal policy can reshape incentives at scale. The initiative launches with a proposal for Right to Build Zones, a federal framework designed to increase housing production by aligning market forces with local reform rather than micromanaging outcomes.

Follow our new housing work here.

One More Big Thing (February was a Big Month)

In his State of the Union address, President Trump announced a plan to offer retirement accounts to American workers who lack access to them via their employer. The retirement accounts are modeled on the Thrift Savings Plan, which is the retirement vehicle available to federal employees.

As EIG explained in response to the speech, the president’s announcement highlights the bipartisan recognition that too many Americans are left behind by the retirement system. Solving this problem has been a central focus of EIG’s work:

Just how big is the problem? Our research has consistently shown the scale of the access gap.

• 42 percent of full-time working Americans do not have access to retirement plans

• 50.5 percent do not receive an employer match

• Lower-income workers are disproportionately left out of the current system. A staggering 78.7 percent of full-time workers in the lowest-earning decile (earning less than $27,400 a year) lack access to a retirement plan.

Establishing accounts for low-income workers is only the first step. The administration has also indicated a desire to work with Congress to automatically enroll eligible workers and expand the pool of eligibility for matching benefits. The bipartisan Retirement Savings for Americans Act (RSAA) — which incorporates an array of EIG’s policy recommendations — offers just such a framework.

Policy

• Adam Ozimek, Jess Remington, and Tina Lee introduced their concept paper for Right to Build Zones, an idea first proposed by EIG in October 2024. “A tangle of regulations has made it impossible to build enough housing in America, a problem that has been worsening for decades,” they write. “The result is a nationwide shortage of millions of homes, rising housing costs, and growing pressure on federal policymakers to address an affordability crisis that is largely driven by rules set at the local level. Right to Build Zones (RBZs) is a new proposal designed to help municipalities unlock housing supply while preserving local control.”

• With Opportunity Zones having become permanent last year as part of the One Big Beautiful Bill Act, Catherine Lyons has published for Novogradac a handy guide for governors and mayors on how to proceed next. Governors are responsible for nominating 25 percent of their state’s eligible census tracts for OZ 2.0 designation. EIG’s guide “provides a clear roadmap for navigating the OZ selection process, creating a map that sets communities up for a decade of investment and impact once new designations come into effect Jan. 1, 2027.” You can also read the comprehensive version of the guide here.

  ---

Research & Analysis

• The Impact of Opportunity Zones on Housing Supply

  Benjamin Glasner, Adam Ozimek, and John Lettieri have updated the findings of their working paper on the effects of Opportunity Zones on housing supply. From their summary: “By making novel use of HUD data sourced from U.S. Postal Service address counts, the study finds that the OZ incentive increased new housing construction by 70 percent in these areas, generating more than 416,000 new residential addresses between 2019 and the first quarter of 2025. The authors also find that the new development and investment did not merely shift from nearby neighborhoods: For every 100 new residential addresses caused by the OZ incentive, roughly 97 represents net new supply that would not have been built in the absence of OZs.”
  
  

• The Flawed Paper Behind Trump’s $100,000 H-1B Fee

  A recent paper by economist George Borjas attempted to compare the earnings of H-1B workers against those of native-born American workers. But as Jiaxin He and Adam Ozimek explain in their comprehensive critique, the Borjas paper “not only reached flawed conclusions but also influenced the Trump administration’s recent $100,000 H-1B fee policy.”
  
  

• AI and the Economics of the Human Touch

  “There are many jobs and tasks that easily could have been automated by now — the technology to automate them has long existed — and yet we humans continue to do them,” writes Adam Ozimek. Think of piano players, servers, actors, and so many more jobs that in part or in whole could have been lost to new technologies. The reason these jobs still exist is because of the perpetual demand for these jobs to offer a human touch. What are the lessons for the future effects of AI?
  
  

• AI and the Real Bottlenecks to Growth

  Some economists argue that ideas have become harder to find, a problem that AI can solve and thereby unleash a new era of rapid productivity growth. But what if the barriers to this golden epoch aren’t new ideas, write Nathan Goldschlag and Adam Ozimek, but rather a broad suite of societal obstacles that make these ideas harder to translate into economic growth?
  
  

• Yes, high-skilled workers should have to compete

  Populists are fond of protectionist ideas, including policies that insulate highly paid workers from having to compete against high-skilled immigrants. This is a mistake, writes Adam Ozimek: “To achieve the bipartisan goal of improving the labor market for the working class, vigorous competition among the top earners is important and even necessary. Not only would the rest of the country benefit — from more innovation, more entrepreneurship, and faster economic growth — but in the long run even the skilled American-born workers exposed to fiercer competitive pressures will fare better too.”

  ---

The New Bazaar

Cardiff Garcia and Adam Ozimek discuss Adam’s piece on AI and the economics of the human touch (see above). Plus:

• The job that inspired Adam’s post

• Why the Olive Garden represents a hopeful future for work in an age of AI

• The perils and promise of AI for caregiving jobs

• How Adam himself prepares for the eventual automation of his daily tasks

Listen to the episode or read the full transcript here.

Around the Horn

• More outlets than we can count cited EIG’s work on retirement in response to President Trump’s announcement that the government would offer retirement accounts modeled after the federal Thrift Savings Plan (TSP). A small sampling: Washington Post, Wall Street Journal, CBS News, Reuters, Axios.

• Jordan McGillis writes in the Wall Street Journal that the real reason the middle class feels poor is the widening gap between the median worker and the 80th percentile worker.

• Ross Douthat of the New York Times cites Adam Ozimek’s post on AI and the human touch in his assessment of AI’s potential effects on the labor market.

  ---

EIG Chart of the Month

Via Adam Ozimek’s post on why high-skilled workers should have to compete:

EIG Video of the Month

Presented by Ben Glasner, a companion video to our 2025 Charts of the Year. See our YouTube and Instagram pages for more.

But perhaps the most strained demand for protectionism comes from critics of H-1B visas. Apparently the best-paid, highest-skilled workers in the richest country in the world should be spared from competing too.

Nothing could be further from the truth.

To achieve the bipartisan goal of improving the labor market for the working class, vigorous competition among the top earners is important and even necessary.

Not only would the rest of the country benefit — from more innovation, more entrepreneurship, and faster economic growth — but in the long run even the skilled American-born workers exposed to fiercer competitive pressures will fare better too.

Won’t somebody think of the doctors?

Bernie Sanders argues that we must “make sure that corporations are required to aggressively recruit American workers first before they can hire workers from overseas.” Governor Ron DeSantis has complained that even university professors — usually not objects of pity from Republicans — shouldn’t have to compete.

The Heritage Foundation laments that “American graduates have to compete with nearly 200,000 foreign students working under OPT and another 400,000 H-1Bs issued or renewed every year.” Heritage even worries that there is too much competition faced by American PhDs and graduates of master’s programs.

These comments reflect a desire to make labor markets less competitive for skilled workers. By “less competitive” I mean a world in which jobs are plentiful relative to the number of applicants, people who want a job can easily get one, and wage growth is strong.

A less competitive environment for workers looks like what we think of as tight labor markets — a fine motivation considered in isolation. Who doesn’t like tight labor markets?

But there are two problems with this approach. First, it is not actually possible to create less competitive labor markets overall via reducing the number of workers. Second, if you try to make labor markets tighter for some types of workers (say, for college-educated skilled workers) by reducing their supply, then you make labor markets weaker for other types of workers (say, the working class).

The critics simply have not thought this through. Let’s look at their argument in more detail.

More people does not mean weak markets

One simple fact that these critics miss is that immigrants represent both supply and demand. They make goods and services with their labor, but they also buy goods and services that require labor. The net impact of more immigrants on labor demand and supply is nil, which means it puts no pressure on wages in either direction.1

Smarter conservative populists recognize this. Oren Cass correctly observes that “economically speaking, immigration is a form of population growth.” In his 2018 book he puts it quite eloquently:

The economy has no fixed number of jobs that immigrants might “take” from native workers, nor do higher or lower levels of immigration necessarily lead to higher or lower wages. Immigrants are both producers and consumers, so their presence increases both the supply of labor and the demand for it.

In short, the idea that you can create tight labor markets overall by reducing immigration is a pipe dream.

Tilting the scale

You can, however, create more or less competition for some workers.

To understand this dynamic more clearly, let’s consider a thought experiment that holds population constant and exclusively focuses on shifts in relative labor supply.2

Imagine if we suddenly made college degrees completely free, but only for those majoring in accounting. Four years later, as all the new accounting majors graduated, the labor market for accountants would be flooded by the surge of applicants, which would make that labor market more competitive. But all those young people pivoting to accounting en masse would mean fewer job applicants for every other type of job. The labor market for non-accountants would be less competitive.

In economics jargon, what has happened is that non-accountants have become relatively more scarce, while accountants have become relatively less scarce.

This example illustrates that there is an inevitable balance when it comes to changing the composition of the labor force: a relatively less competitive labor market for some workers must be matched by a relatively more competitive labor market for others.

You can think of the effects of shifting the composition of the workforce as operating like a scale. If you change the height (competition) on one side by adding or reducing weight (labor supply), there will have to be an offsetting change to the height (competition) on the other side. The height of the fulcrum point, representing overall labor market competition, does not change.

It is this balance precisely that H-1B critics miss.3

Yes, immigration can undoubtedly tilt the scale by changing the composition of the labor force in one direction or another. Critics of H-1Bs want to tilt the scale to benefit native-born skilled workers. What they fail to understand is that you can only reduce competition for skilled workers by increasing it for everyone else, including the working class. The scale must balance.

College vs non-college

Perhaps some critics will simply bite this bullet and continue demanding that we rebalance the labor market to benefit skilled workers. After all, a number of intellectuals have argued that we have “too many college graduates” in the United States.

This is a misread of the evidence. As Claudia Goldin, Larry Katz, and David Autor argued in “Extending The Race Between Education and Technology,” over the last few decades the demand for college-educated workers has gone up even faster than the supply.4

One clear piece of evidence for their claim is that the college wage premium — meaning how much more the average college worker earns than the average non-college worker — is near its historical high. Goldin and her coauthors estimate 62 percent of the growth in the college wage premium from 1979 through 2017 is due to a slowdown in the growth of the college-educated workforce.

Hardly a sign of too many college graduates.

Indeed, what this suggests is that in recent decades, college workers on the whole have faced an increasingly less competitive labor market than non-college workers. To the extent we want to tilt the labor market to make it tighter for one group of workers, it seems like workers with less education would be the reasonable choice.

The real wage inequality

There’s a problem, however, with just looking at the disparity between college and non-college wages. Consider the following sets of facts.

In the early 1960s, only about 15 percent of workers in their 30s had four years of college or more. By 2025, the share had risen to nearly 50 percent.5

Furthermore, for the last few decades, the most disproportionate gains in the economy have not been the top half leaving behind the bottom half. Cumulative median earnings growth since 1980 has been 33 percent for the bottom half of workers and 45 percent for the top half. This has even narrowed recently, with the ratio of median earnings for the top and bottom halves of the distribution falling from 2.4 in 2014 to 2.2 in April of 2025.

Instead, inequality in recent decades is really about the most highly paid pulling away from everyone else. Since 1980, the earnings of the median worker have climbed by 33 percent. The 80th percentile earner has enjoyed much bigger gains of 53 percent, while the 90th percentile worker has blown them all away with earnings climbing 78 percent.

It should be clear by now that college vs non-college is no longer the right comparison for understanding which classes of workers have reaped the biggest benefits of the American labor market in the last four decades. Instead, to the extent that the labor market has been very tight for one group of workers compared to the rest, it is those at the very top of the income and skill distribution who have benefitted the most.

Goldin, Katz, and Autor draw a similar conclusion, arguing that “most of the recent rise in wage inequality has occurred within, rather than between, education groups.”6

It is for that reason specifically that policy should focus on tilting the labor market not just to increase more college workers of any type, but specifically the highest 10–20 percent of earners. The good news is that H-1Bs are doing just this.

Lies, damned lies, and badly mischaracterized statistics

There is a massive disinformation campaign trying to convince the public that H-1Bs are not highly paid workers. The Heritage Foundation, for example, headlines a 2025 report with the claim that “most H-1B positions pay below-median wages; just one in six reaches the highest level.” But this is a wildly inaccurate description of what the report’s own findings show, which is that H-1B workers on average earn less than the median wage of the occupation they work in, in the city they work in.7

Consider that the median annual wage in the United States was $49,500 in 2024. If I told you that someone making $200,000 was being paid a below-median wage, would you find my claim to be accurate? That worker is in fact making four times the actual median. Would it matter that the worker happens to be a software developer in San Jose, California, just because the median pay for someone in that job is $208,270 in that metro? Yes, technically someone making $200,000 a year would be below the median for that job in that place. Nevertheless it seems incredibly inaccurate, bordering on the absurd, to describe that person as “below-median.”

In fact, nowhere does the report actually state the plain fact that the typical wage of H-1B workers is $120,0008 — more than double the median for the country. This fact should furthermore not be surprising given that 57 percent of workers on an H-1B visa have a master’s degree or higher.

These are the kinds of mental acrobatics that people go through to mislead the public on this issue. H-1B workers are highly paid. Full stop. Anyone claiming otherwise is lying to you.

Who competes with H-1Bs?

When thinking about who in the United States should compete more, the question of whether H-1Bs are highly paid is not the right one to ask. We should instead ask whether those who compete with H-1Bs are themselves highly paid.

To answer this question, my colleagues and I have created a dataset showing the occupations of H-1B workers in detail.9

We find that 74 percent of H-1B workers are in occupations that are in the top decile by the salaries they pay. Another way of saying this is that roughly three out of every four H-1B workers are in the group of occupations that pay an average salary of $164,000 dollars per year.10 The American workers competing with the vast majority of H-1Bs are in jobs that require the most skill and are the highest-paying. The workers in these jobs make up exactly the same group that has disproportionately benefited from growing wage inequality in the past few decades.

Another 19 percent of H-1Bs are in the 80th to 90th percentile, again competing with a highly skilled, highly paid group of American-born workers whose earnings have pulled away from the working class and median worker in recent decades. The average occupation in this group pays $93,000 per year.

All of which means that 93 percent of H-1Bs are competing with workers in the top fifth of earners in the U.S. economy.

Given an accurate understanding of how new workers affect the composition of the labor market — discussed above — it is evident that H-1B workers reduce labor market competition for American workers in the bottom three fifths — a group that includes the working class no matter how you choose to define it.

Perhaps even more striking is our finding that fewer than 0.1 percent of H-1B workers are in occupations where the average pay is below median. To repeat, less than one out of every thousand H-1B workers is in an occupation with an average salary below the nationwide median.

It is a bizarre form of populism that refuses to allow the bottom three fifths of workers, the entirety of the working class, to benefit from greater competition at the top.

Making the good even better

The remarkable thing about this result is that it’s happening even though the H-1B is in fact a seriously flawed program. The program has hundreds of thousands more applicants than visas every year. To decide who gets one of the scarce visas every year, the system uses a lottery. This approach fails to ensure that the best and brightest applicants are chosen and that labor market competition is focused as much as possible on the highest-paid occupations.

Given these flaws, it is remarkable that the system still works as well as it does.

And the good news is these problems are fixable. As we at EIG have proposed, the system would be so much improved if it prioritized the workers with the highest expected lifetime incomes, which we calculate by using two simple pieces of information about the applicant: their age and the pay of their job offer. We then project their expected lifetime earnings using the average growth in pay by age for skilled workers in the American economy.

Changing to this approach would tilt the program meaningfully towards higher pay occupations.

Using data provided by Bloomberg, my colleagues and I simulate what would have happened if this approach had been used instead of a lottery for the years 2021–2024.11 Under the status quo lottery, 71 percent of initial H-1B applicants are in occupations that are in the top decile by earnings. In our preferred approach, which lets in those with the highest expected lifetime earnings, this share would rise to 82 percent, while the total share of H-1B applicants in the top fifth of highest earning occupations would rise from 94 percent to 97 percent.

The H-1B visa system, flawed though it is, overwhelmingly directs more competition to those workers in the American economy who have most benefitted from the rise in inequality. In doing so, it massively benefits the broad working class — and if we fix its flaws, those working-class gains will be larger still.

Positive Sum

But the benefits of high-skilled immigration are in fact much broader.

Tilting the labor market by changing the workforce composition is a relative effect, which means making some workers end up marginally better compared to others. That is a zero sum change. But high-skilled immigration also has overall effects, which mean more for everyone. That is a positive sum change.

I briefly discuss the overall effects of high-skilled immigration below. But for readers who want a deeper dive into the literature — including the detailed sources for all of the following data points and claims — please see our report, Exceptional By Design. The overall effects of high-skilled immigration include the following:

• Innovation: In the long-run, innovation is one of the biggest drivers of wage growth, and immigrants undoubtedly boost innovation levels. One study found that immigrants are responsible for 32 percent of the aggregate innovation in the US since 1990.

• Entrepreneurship and dynamism: A substantial body of research shows that through funding their own companies and making it easier for others to start companies, high-skilled immigrants help lead to the creation of new businesses that challenge large, old, incumbent firms.

• Industry growth: Throughout history, immigrants have helped found and grow industries throughout the United States. The nation’s lead in a variety of high tech sectors is fueled by the work, innovation, and entrepreneurship of high-skilled immigrants.

• Fiscal effects: Unsurprisingly given their high incomes, H-1B holders contribute more than $40,000 in net federal government revenues each year, which means that they deliver some combination of lower taxes, lower interest payments, or more spending for American-born natives.

Many types of immigration can have these positive overall effects. Without guest worker visas, for example, some4 rural parts of the country would lose substantial parts of their agricultural industries. Native-born workers in these communities would be worse off without the overall effects of industry growth. This overall effect can, and in many cases likely does, offset any negative effects on manual laborers of greater labor market competition.12 Positive sum effects are one reason why low-skilled immigration doesn’t necessarily leave low-skilled American workers worse off despite more labor market competition for some of them.

For high-skilled immigrants, though, the overall effects are so overwhelmingly large that even the high-skilled natives who most closely compete with them almost certainly benefit in the long run.

Okay, what would really happen?

If the nation kicked out every H-1B worker, would the remaining workers in the same occupations get a raise? I think the right answer is to admit that such an outcome is possible — but this answer is just not even close to capturing the whole story of the H-1B program’s effects.

To understand why, it’s necessary to ask a slightly different question: If the H-1B program had never been enacted in 1990, would natives working in competing occupations today have higher wages? Almost certainly not, because of the overall effects.

Start with the 32 percent higher aggregate innovation from 1990 to today that, according to researchers, was generated by high-skilled immigration.13 Not only does that higher innovation benefit workers and consumers across the skill spectrum — everyone gains from new medicines and better airbags — but more than half of that innovation was the result of high-skilled immigrants boosting the innovation of their native-born collaborators. A huge chunk of innovation from native-born workers since the 1990s, in other words, would not have occurred without skilled immigrants.

Native-born workers also benefit from the growth of companies and industries founded and powered by skilled immigrants. As Elon Musk has argued: “The reason I’m in America along with so many critical people who built SpaceX, Tesla, and hundreds of other companies that made America strong is because of H-1B.”

It’s true that you could make some native workers better off in the short-run by banning SpaceX and Tesla from hiring skilled immigrants. This is a point H-1B critics cling to. But those companies, and the supply chains that support them, only exist in the first place because of skilled immigrants.

These aren’t isolated examples. Silicon Valley and the American tech sector broadly would be shadows of themselves without high-skilled immigrants. If the best and brightest were prevented from moving to the United States, the many tech firms they have created either would not exist or would be located in some other country.

Whether they realize it or not, this is the world H-1B critics are pining for: America not as a center of innovation, technology, and world class research, but as a minor backwater and also-ran, lacking in opportunities for skilled workers both native and foreign. The critics would have us be North America’s France.14

If we stop allowing in H-1B workers, that is what America would eventually become. The nation would lose out on all of the future innovations, startups, and industry growth that skilled immigrants would have created.

At best, economic suicide like this could only help skilled American-born workers in the very shortest run. Cutting off H-1Bs would be like taking our economy’s seed corn and feeding it to the top 10 percent highest-paid among us. It would be bad for the working class, and in the long run it would be bad for native-born skilled workers too.

What does high-skilled immigration do?

To be fair to the critics, not all of them want to end the H-1B program. Some merely want to reform it. The problem is not the desire for reform, which I share, but that their specific plans for reform so often reflect a completely backwards and highly restricted vision for how the skilled immigration system should work.

Some critics argue, for example, that we should allow in H-1Bs, but in such small numbers that they “fit in a bus, not a stadium.” Other critics argue that high-skilled immigrants only benefit us when they “address labor shortages,” and that the goal of policy should be to make sure that American workers are guaranteed to get first dibs at every job opening. An ideal system to these critics is one in which a high-skilled immigrant can be hired only once an employer definitely, certainly, and convincingly proves that “no American will do the job” and when government bureaucrats have verified a “shortage.”

This is a vision of immigration that attempts to micromanage away any competition for American workers. But it is completely disconnected from a true understanding of the actual benefits of high-skilled immigration.

We should not only allow competition, but welcome it. We should design H-1B visas to direct competition to where it is most needed. It is fine and good actually if the top-earning skilled workers in the United States compete more. The top 10 percent of natives should not get first dibs on job openings as a birthright. This could only happen at the expense of the working class.

A system that focuses on shortages will deliver fake bureaucracy and limited benefits. A system that focuses on bringing in the highest-paid workers and welcomes competition will lead to more innovation, more entrepreneurship, more productivity, and greater wage growth for the working class.

If we ban H-1Bs entirely or try to shrink them down to fit into a bus, we will all be poorer — including the high-skilled native workers that H-1B critics are so eager to protect.

That is Anthropic CEO Dario Amodei’s vision of the world after the arrival of truly powerful artificial intelligence. He foresees millions of independent, highly intelligent agents capable of being sent off to do projects, like an army of smart employees.

Sounds like science fiction, and yet it becomes more plausible by the day. What does economics research have to say about such a world, one where non-human scientists flood the economy with new ideas?

A puzzling disconnect

The typical answer, according to the economic models, is that more researchers leads to more growth.

One complication with this story is that we have already added a ton of researchers over the last few decades, during which productivity growth has been historically weak.

Economists have been puzzled by these seemingly contradicting trends. To understand why, we need to use just a little bit of math. We promise it won’t be painful.

In 1990, Paul Romer wrote down a model of economic growth that he would eventually win a Nobel Prize for. In that work, Romer structurally incorporated the impact of new ideas on growth.

This equation says that the growth rate of output and consumption (g) is equal to the growth rate of ideas, which is in turn driven by the number of researchers (H­_A) and how efficient those researchers are (𝛿).

Standard models of economic growth, like the one above, tell us that if you increase the number of researchers, growth should follow.

By pretty much any measure, the amount the US spends on innovation has increased significantly over the past several decades. The share of inventors in the workforce, to take just one indicator, more than quadrupled between 1980 and 2020. Productivity growth has nonetheless lagged.

This is where the growth theory is helpful. If the number of researchers has gone up, but growth remains flat, the models tell us that the efficiency of those researchers must be falling. And indeed this is exactly what one team of economists believes happened.

The cost of new ideas

Economists Nick Bloom, Charles Jones, John van Reenen, and Michael Webb have a worrying conclusion about the ideas economy: “everywhere we look we find that ideas, and the exponential growth they imply, are getting harder to find.”

One place they look for evidence is Moore’s Law. This is the empirical regularity that it takes two years for the number of transistors that fit on a computer chip to double. Moore’s Law has held true for decades, but they found that it takes 18 times more research inputs to keep it going today than it did in 1971.1

They find similar results across a variety of industries. The amount of research input has gone up, while the real outputs of research have gone down. In other words, researcher efficiency has fallen.

If this is true, this finding has big implications for how we think about AI as an army of researchers. It would mean — again, assuming Bloom and his coauthors are correct — that the main constraint to productivity growth is simply lower gains per researcher, and therefore all we have to do to usher in a new era of rapid productivity growth is to add even more (and potentially better) researchers. A country of more and more geniuses, as it were, is exactly what is called for.

The real constraints to growth

But what if the problem is something else?

New research from Teresa Fort, Nathan Goldschlag, Jack Liang, Peter K. Schott, and Nikolas Zolas calls into question the finding that ideas are getting harder to find. Looking at more than four decades of firm-level data on research investments, patents, and the growth outcomes of all non-farm employer businesses, they break down the relationship between ideas and economic growth into two parts: (1) the relationship between R&D investments and ideas generated, and (2) the relationship between ideas and firm growth.

Fort and coauthors find:

1. R&D→Ideas: Ideas are NOT getting harder to find. If anything, firms are getting more ideas per dollar spent on research.

2. Ideas→Firm Growth: Ideas are NOT getting harder to translate into firm growth. The ability of firms to translate ideas into growth is strong and relatively stable over time.

But we are now left with a mystery. If the returns to R&D aren’t falling, and the relationship between ideas and growth is stable, then why hasn’t the significant rise of investment in innovation led to booming growth?

The key turns out to be secular trends in firm growth — patterns that are common to all firms after accounting for ideas. Fort and her coauthors find that these secular trends, which are experienced by all firms equally, are pushing down firm growth rates. Which means that we need to focus our attention on other trends in the economy as the culprit for why increased research expenditures are not translating into growth.

AI and the Bottleneck(s) to Growth

Even if AI ends up helicoptering millions of new geniuses and their ideas down onto the economy, the work of Fort and coauthors suggests it might not automatically translate into a productivity boom. If researcher efficiency wasn’t the bottleneck, we need to place more focus on the secular trends occurring across the whole economy that are dragging down firm growth rates.

What might these secular trends be? That question falls outside the scope of their paper, but we can think of a few candidates.

1. Diffusion of Ideas: Ufuk Akcigit and Sina Ates show that the slowing diffusion of ideas from the most productive firms to others is consistent with a broad set of macroeconomic trends including rising market concentration, increased average markups, declining firm entry, and decreased dispersion in firm growth.

2. Demographic Change: The path of dynamism is intimately linked to changing demographics. Fatih Karahan, Benjamin Pugsley, and Ayşegül Şahin show that the slowdown in labor supply growth since the 1970s was an important driver of the long-run decline in the startup rate.

3. Regulation and Rent-seeking: Large incumbent firms often attempt to leverage state power to gain advantage and stifle competitive pressure. Even though Nathan Goldschlag and Alex Tabarrok find that federal regulation alone cannot explain declining dynamism, Ufuk Akcigit, Salomé Baslandze, and Francesca Lotti show that market leaders are much more likely to have political connections and much less likely to innovate. Mancur Olson provides a more sweeping account of collective action problems and how concentrated interest groups slow growth.

One common thread from these other factors is that our productivity problems are not about how much innovation is happening at one particular firm, but how that innovation does or doesn’t spread throughout the economy.

This possibility is supported by research showing declining skewness in firm growth rates and declining responsiveness of firms to shocks. In plainer language, the idea is that we have fewer high-growth firms, and also that those firms that come up with a new product or process, or see a surge in demand for some other reason, on average don’t create as many jobs as they used to. Complementary research shows increased concentration among market leaders, with market leaders increasingly pulling away from their competitors in how productive they are (in economics jargon, there is rising skewness in productivity between firms). Firms on the frontier appear to be doing fine, but laggards are falling further behind market leaders over time.

Improving the allocation of funding for science is an important and necessary policy goal. So is future-proofing our institutions, readying them for a higher rate of discovery. But ideas alone might not be the economic sparks we hope them to be if they don’t lead to fires that spread throughout the economy.

This is not to say that AI won’t increase growth through other channels, such as reducing the cost of providing certain goods or services, or even that the ideas we get from AI won’t increase growth. Instead, we are suggesting that there will remain many important bottlenecks.

Researchers and policymakers need to place more emphasis on understanding and addressing a broader set of drags on growth — perhaps with AI! — that go beyond the Eureka moment. We need to reinvigorate entrepreneurship, economic churn, and the flow of labor, capital, and ideas across firms. In short, they need to pursue policies that increase America’s economic dynamism — or risk squandering the talent of our future AI geniuses.

Either AI is so useless that we are in the middle of a bubble that’s about to burst and take the economy down with it, or AI is so powerful it’s going to replace us all and devastate the labor market.

The pessimism in speculation about the economic effects of artificial intelligence is often so overwhelming that these opposing concerns can even come from the same person. AI is evolving fast enough that we should not entirely ignore the economic doomers, though it would be nice if they could at least be consistent.

But it is essential to balance the discussion with some optimism. I can see glimmers of hope in a simple fact: There are many jobs and tasks that easily could have been automated by now — the technology to automate them has long existed — and yet we humans continue to do them. The reason is that demand will always exist for certain jobs that offer what I call “the human touch.”

The specific jobs that require the human touch may themselves change or evolve, but I suspect that such jobs will continue to exist long into the future.

The music never stops

To understand why, we begin with an example from the past. The distant past.

The player piano, or pianola, was invented by Edwin Votey in 1895. At first it was a stand-alone machine that would be pushed up against an existing piano, like the one shown below.

Within a few years, player pianos could be built into the pianos themselves. The machines “read” music that was encoded onto rolls of paper. The notes were represented as holes in the paper that directed pneumatic airflow, which then pushed down the levers that depressed the piano keys.

The only role for humans to play in the functioning of a player piano was to pump the pneumatic foot pedals to keep the piano playing. No need for a skilled human piano player.1

And yet, despite the technology to fully automate the job having been invented more than a century ago, people still make a living playing the piano today.

The job is not just limited to piano players performing in ticketed concert events, which of course are quite common. Hotels, bars, and restaurants continue to hire live piano players to provide background music as if it was 1894, the year before the invention of the pianola, which itself is hardly ever used anymore.

Listeners simply prefer music from a piano player rather than a player piano.

The greater competition for live musicians these days comes from a different and a far less literal type of automation: music recordings.

The act of creating recorded music obviously requires human musicians. But once the music exists, it is easy to fully automate what could otherwise be a live musical performance. Like the player piano, this form of automation has been around for a very long time. Thomas Edison invented the phonograph in 1877. The first commercial version was released in 1895.

From tinfoil phonograph cylinders to Spotify today, live music has long faced competition from recorded music, which over time has been produced at lower and lower cost and heard by listeners with greater and greater convenience.

This competition has aroused plenty of worry in musicians through the years. In 1927, the movie The Jazz Singer debuted as the first full-length movie with synchronized recorded music. It pointed to the eventual end of silent movies — in addition to the need for the live musicians to accompany them. Musicians panicked. The Music Defense League was launched to spend hundreds of thousands of dollars campaigning against “canned music,” arguing that it would take work from musicians.

And yet despite 130 years of automation and the continued rise of “canned music,” live music performances are ubiquitous today. The demand for them is not limited to the upper reaches of talent, such as violinists playing Schubert in a concert hall,2 or superstars like Taylor Swift. While top tier performance artists are as in demand as ever, so too are those at the bottom of the quality spectrum.

So high is the demand for live human music that there are now more than 200,000 individuals employed as musicians or composers across the United States — more than at any time back to 1850.

Consider that recordings of the greatest musical acts ever, and indeed some of the greatest live shows ever, can be heard almost for free thanks to the magic of technology. Nevertheless in almost every town in the United States, the very night you are reading this sentence, terrible bands are being paid to perform live in bars. Good bands, too. They just aren’t playing during the matinee in your local movie theater, to the dismay of the Music Defense League.

Why haven’t most of these jobs been automated? Why is it that the piano player remains when the piano playing automaton has been around for a century? Why is it that people would rather listen to a bad bar band than the masterpiece that is the recording of Johnny Cash playing Live At Folsom Prison from 1968?

The demand for the human touch is visible in other parts of the arts and entertainment sector as well. Many people fear that AI will replace human actors. But CGI has been technically capable of replacing human actors and special effects for some time. Audiences dislike the uncanny valley that often results, and Hollywood still uses practical effects like blowing up actual airplanes. I would bet on the continued existence of both Broadway and demand for human film actors. Something about the experience of watching performances makes the audience simply prefer to be impressed by other talented humans rather than by machines.

Craft fairs also remain popular, even though any specially designed DIY goods you’re likely to find at one of them can also be found on Amazon, and nearly always at a lower price.

Perhaps the art with the highest value placed on the human touch is paintings. In 2008, an alleged Pablo Picasso painting that had sold for $2 million was found to be a forgery. Although it was a perfect visual replica of Picasso’s work, the owner was not happy. In the market for expensive fine arts, the human touch can be worth millions.

The human touch in services

The demand for the human touch is not limited to the arts. In 2007, a restaurant entrepreneur named Jack Baum was teaching an executive MBA program at Southern Methodist University. He challenged the class to come up with a way to help restaurant customers pay their bill faster than simply waiting for the server to bring the check. Three students arrived at such a compelling answer that the four of them turned it into a company called Ziosk.

Ziosk’s tabletop ordering system provides customers with a tablet that allows them to order, pay, play games, enter coupons, and much else. Thus was born the ability to automate away the job of waiter.

The tablet debuted at 125 Chili’s locations in 2013, and today they are in thousands of restaurants. Ordering devices like this are much more commonplace today, including QR codes that allow customers to order from their own smartphones.

On paper, the job of waiter has been fully automated for over a decade. And yet, today there remain 1.9 million waiters across the US. It’s true that this number has dipped recently, and is slightly below the historical peak. Under the pressure of automation, the BLS forecasts that it will further decline within the next decade… by 1 percent.3 Is that the worst that full automation can do to this job?

What is it that makes the waiter resistant to being fully replaced by a long available automation?

Consider first that even some restaurants that have implemented automation nevertheless have wait staff. At Olive Garden, you can order and pay from a provided tablet at any point, but you still have a waiter who greets you, offers to take your order if you don’t want to use the tablet, and checks in on you throughout the meal. If you wait long enough, they will even bring the check.4 That is a strong signal that the waiter is adding value above and beyond automation.

Note also that the more expensive the restaurant, the less likely it is to have automation via tablet or QR codes. At fine dining restaurants, the number of people serving you can rise along with the added work they do, with different staff pulling out chairs, cleaning the table in between courses, manning cheese and desert carts, advising on wine, and much else.5

The human touch is a specific and valued characteristic of the service. It turns out that people don’t just go to a restaurant to order and pay as fast and with as little friction as possible. A good waiter contributes to service quality, and service quality is as essential an ingredient to many dining experiences as the ambience. Automation can save costs by replacing all of the literal tasks of the waiter, but so would stripping restaurants bare of all decorations and finishes. This is simply not what customers want, least of all the highest income customers paying the most for the highest quality experiences.

A normal good, and it’s everywhere

The ability to stand face to face with another human and sell them something is a widely needed skill in the economy. One can browse the BLS Occupational Handbook under the category of sales occupations and find a variety of jobs where the human touch matters, some of which have been automateable for quite some time:

• There are still 67,500 travel agents despite the widespread availability of leisure, hospitality, and accommodation reviews and booking options online.

• Self checkout has failed to replace 3.2 million cashiers and 4.2 million retail sales workers.

• At the higher end of the pay spectrum are 56,800 sales engineers, more than half a million insurance sales agents, and 1.6 million wholesale and manufacturing sales representatives.

The real economy is filled with jobs, across a variety of industries, where sales ability or some other aspect of the human touch is an important ingredient, and in some cases very thorough automation has failed to replace them.

Empirical evidence is hard to come by, but the human touch also appears to be what economists call a “normal good,” which means the demand for it goes up as income goes up. Make more money, and you’ll choose to eat at nicer restaurants with more attentive service. You will also be unlikely to buy expensive watches, cars, or suits via automated kiosk. There’s a reason that those goods are typically sold by people with high levels of training and social skills.

What to do if AI actually is disruptive

Nothing that I have said is evidence that AI won’t drive rapid and disruptive change. After all, there are plenty of jobs where the human touch is not relevant and automation would not leave consumers wanting.

Even in jobs where the human touch does matter, that is no guarantee they won’t be automated. In some cases, the extra value consumers place on the human touch can be outweighed by the higher quality or lower costs that automation provides. Movie theater musicians from the silent film era would certainly agree with that. Handmade furniture sold by artisans at craft fairs might still exist today, but the vast majority of furniture is mass produced.

In economics jargon, it is absolutely possible that for many workers, competition with AI leaves their marginal product low enough that it falls below their reservation wage (the minimum they are willing to work for). This is especially likely if change comes so rapidly that many workers end up moving from one automated career into another career that is then also quickly automated away.

What can we do if the level of change ushered in by AI leaves many workers behind? If income and employment do fall for substantial numbers of people, how should policy respond? I believe the existence of demand for the human touch makes this outcome a more surmountable challenge than is commonly assumed, and should therefore be a source of optimism.

Let’s start with income.

If productivity surges from AI, the United States will become a far richer country per capita. It’s not clear whether this will translate into much faster income growth for the median workers. In recent decades, after all, median wage growth has lagged mean wage growth — likely reflecting the trend that overall productivity growth has exceeded the growth in productivity of the typical worker.

Median wage growth has been positive, so it is not true that the typical workers fails to benefit from faster productivity growth. But the benefit for the typical worker is not proportional to the economy-wide growth in productivity, raising the spectre that future productivity growth could be even less proportional.

The result would be rising income inequality — which can straightforwardly be offset with policies that redistribute income. Redistribution might be expensive, but the same AI-driven economic growth that generated the rising inequality would also create the fiscal space needed to offset it. In short, spreading income around is a political challenge, not a policy or economic challenge.

The potentially more challenging problem arises if you believe, as I do, that work matters for the human spirit and general well-being. What do we do to ensure there are plenty of jobs for humans?

The point of this essay is that this threat to work is itself overrated.

First, if the politics are sorted and there is meaningful redistribution, then a nation of far wealthier individuals will lead to a surge in demand for the human touch in a variety of industries. Higher incomes mean more fine dining, more luxury goods and services, more personal trainers, more handmade goods, and so much else. The increased demand for the human touch will by itself help counterbalance some of the potential jobs replaced directly by AI.

Second, to the extent that the new jobs created are not as high paying as workers would like, or that redistribution reduces the desire to work, these are problems that can be addressed by a policy or policies that increase both the returns to work (for workers) and the demand for work (from employers). My favorite variant of such a policy, which I have proposed along with my colleague Ben Glasner, is a wage subsidy.

Put another way, a policy like a wage subsidy converts some demand for work into much more demand for work. It also raises the wages of relatively low-paying jobs.

For a wage subsidy to have a positive effect in an AI future, however, there does have to remain some demand for work rather than no demand at all. And that’s where the constant, unwavering demand for the human touch plays a role. It suggests that a substantial demand for human work will indeed remain. Policymakers are thus free to stay focused on effectively boosting the demand for work and raising the pay of the workers who do it.

The desire for the human touch will do the rest.