Key takeaways:

• The vast majority (85%) of young college graduates work in occupations that have seen strong employment growth in recent years.

• Young college graduates, like college graduates in general, are more likely to work in AI-exposed occupations than the overall workforce—and considerably more likely than young noncollege workers.
• But both young college graduates and young noncollege workers have experienced rising unemployment over the last three years, suggesting AI is not likely to be driving labor market weakness.

In the first blog post of our Class of 2026 series, we showed that the strong labor market for young college graduates of the early 2020s had begun softening in recent years. A growing share of young college graduates are seeking employment, but because their employment rates have not kept up with this job search, their unemployment rate has risen faster than the overall rate. The second blog post in the series discussed the industries where young college graduates worked. We found that recent graduates work in growing industries, but are forced to enter a weakened labor market with less job turnover, deteriorating their ability to break in. Young college graduates work in the tech sector at a similar rate to college graduates, and there is no clear evidence that tech sector employment is significantly decreased despite warnings about the advancement of AI.

In this blog post, we delve deeper into the occupations where young college graduates are likely to work.1 We examine whether it has been relatively more difficult to secure employment in these fields as the labor market has weakened. We also scour the data for signs that exposure to AI-related occupations may disproportionately affect the prospects for young college graduates as they enter the labor market.

Most young college graduates work in occupations with strong growth

Over 60% of young college graduates work in professional and related occupations or management, business, and financial occupations. Figure A displays the share of employment in each occupation or occupation grouping for young college graduates ages 22 to 27, all college graduates, and young workers without a four-year college degree. Occupations in the figure appear in order of the share of young college graduates employed in each, from largest to smallest. Over half (62.8%) of young college graduates work in professional, management, business, and financial occupations. Workers of any age with a college degree are slightly more likely to work in those two occupations (64.5%), though more likely in management occupations than professional occupations. On the other hand, nearly half (48.3%) of young noncollege workers are in service occupations or farming, construction, installation, and production occupations.

Figure A

Figure B shows the change in employment in each occupation between 2019 and 2026 and between 2023 and 2026, arranged in the same order as Figure A for comparison. Since 2019, management, business, and financial occupations and transportation and material moving occupations experienced the most growth, followed by professional and related occupations.

The top four occupations for job growth since 2023 account for 85% of young college graduate employment. The occupations with employment losses over the last three years were more likely to employ young noncollege workers than college graduates. It doesn’t appear that the occupations where young college graduates tend to work have been hit particularly hard in the last couple of years.

Figure B

While there has been job growth among occupations that tend to be filled by young college graduates, some worry about an increase in labor market underutilization, i.e., when workers with a college degree wind up working in jobs that typically don’t require one. Using O*NET data2, the New York Federal Reserve tracks this type of labor market underutilization. While the share of recent college graduates working at a job that doesn’t require a college degree has ticked up slightly over the last three years, it remains lower than it was for workers who graduated in the aftermath of the Great Recession. Even as late as 2017, young college graduates were working at these noncollege jobs at higher rates than they are today.

While college-educated workers are in more AI-exposed occupations, this does not appear to be driving labor market weakness

Much has been written in the last few years about AI exposure and its impact on the labor market. Using data from ADP, a large payroll processing company, Brynjolfsson, Chandar, and Chen find that entry-level workers in AI-exposed occupations—particularly AI uses that automate, not augment their work—have experienced an employment decline larger than that of older workers in the same occupations and all workers in less exposed occupations, explaining some of their stagnant overall employment growth. Atkinson and Yamco also find that declines in AI-exposed occupations are tied to lack of hiring rather than layoffs, hitting harder for young people attempting to enter the labor market. The second blog post in our series noted an across-the-board slowdown in hiring—which hurts the job prospects of all young workers, not only those in the industries most affected by AI.

On the other hand, researchers at the Yale Budget Lab argue that there has only been a slight increase in the shift in the occupation mix of employment, which would be evidence of AI automating jobs. They find that high AI-exposed occupations—determined by the top quintile of AI exposure—have yet to show declining employment, so no “dissimilarity” between young and older college graduates in terms of occupation mix has materialized. Raderman also finds that there isn’t strong evidence that AI is responsible for weaker labor market outcomes for recent college graduates, using evidence from Tillerman on college majors paired with change in unemployment.

Given the variation in assessments, we wanted to take a look at the data ourselves. Gimbel, Kendall, and Kulsakdinun have done an admirable job of summarizing the literature that attempts to classify AI exposure and propose a weighted aggregate measure of AI exposure.3 We employ this measure to investigate whether young college graduates may be more likely to be at risk in AI-exposed occupations than other workers.

In Figure C, we display the AI exposure of occupations weighted by the share of the entire workforce in each occupation. Moving from the left to the right on the figure increases AI intensity. For instance, professional and office & administrative support occupations are more AI exposed (to the right), while production, transportation, and service occupations are less AI exposed (to the left). Overall, the mean AI exposure score is 0.23.4

Figure C

In Figure D, we show the distribution of select demographic groups by occupation and AI exposure. As with earlier analysis, we compare young college graduates with all college graduates and young noncollege workers, in separate panels in the figure.

According to the aggregate measure, college graduates do have higher AI exposure in the labor market than the overall workforce. It is clear there is more mass in the direction of higher exposure (to the right) and their mean exposure is 1.07, higher than that of workers writ large. But the AI exposure of young college graduates isn’t any higher than that of college graduates in general. Mean AI exposure among young college graduates is 1.00.

Figure D

What is striking is that the AI exposure among young college graduates (1.00) is considerably higher than that of young noncollege workers (-0.61). If AI was driving labor market outcomes, we’d expect young college graduates to fare worse in today’s economy, e.g., see larger declines in employment or faster increases in unemployment. But, when we compare unemployment rates as we did in the first blog post of this series, both groups experienced similar increases in unemployment over the last two to three years. Trends in employment rates were also consistent across these groups.

Since the weakening labor market is hitting both young college and noncollege workers alike, it’s hard to argue that AI is uniquely causing job losses for new labor market entrants graduating from college now or in recent years. These findings are consistent with the literature, as there is currently no consensus about the effects of working in AI-exposed occupations on employment thus far.

1. Throughout this brief, we define young college graduates as people between the ages of 22 and 27 with only a four-year college degree. Unlike similar analyses of young workers, we do not exclude young college graduates who are currently enrolled in school, but the results here are robust either way. Unless otherwise noted, data for 2026 represent a 12-month average from April 2025 through March 2026 for the most up to date and reliable estimates, which removes seasonality and increases sample sizes.

2. O*NET or the Occupational Information Network provides the largest up-to-date database of information about workers sorted into detailed occupations. Information provided is about skills, abilities, education, training, and more.

3. We use an updated summary AI exposure PCA score (principal component analysis weighted standardized z-score) provided by the authors, May 13, 2026.

4. The PCA score scale is centered at 0, the unweighted mean across occupations.

Key takeaways

• The depressed overall hires rate is a key driver of new labor market weakness for young college graduates, as it makes it harder for them to break into the labor market. This is true across industries, not just in those that disproportionately employ young college graduates—suggesting the culprit is not a structural change in the economy like AI but a labor market in which employers are hiring less and workers are holding onto the jobs they have.
• The information sector—posited to be more AI-exposed—has experienced recent job losses but employs only 2.3% of young college graduates.
• High-tech industries, which employ about 1 in 10 college workers, expanded at a historically rapid pace in the early 2020s but have shown signs of softening over the last three years.

The early 2020s labor market for young college graduates was strong. But, as we showed in this series’ first blog post, the Class of 2026 is graduating college into a labor market that has notably weakened in the past two years. A growing share of young college graduates are looking for jobs, but their employment rates have not kept pace—meaning unemployment is rising faster for young graduates than for the overall workforce. While their outcomes remain better than those of their noncollege counterparts, the uptick in unemployment has been a rising concern.

In this blog post, we delve deeper into the industries where young college graduates are likely to work,1 examining whether it has been relatively more difficult to secure employment in these fields as the labor market has weakened. Our analysis first examines employment changes, then turns to labor market flows, including hires and separations rates. We also scour the data for signs of contraction in the tech sector that may disproportionately affect the prospects for young college graduates as they enter the labor market.

In the third blog post in the series, we will examine the occupations where young college graduates work with particular attention to occupations that may have grown or shrunk, as well as to those most exposed to AI.

Young college graduates work in industries with strong growth in this business cycle

Over half of young college graduates work in private education and health services, professional and business services, or public-sector jobs. Figure A displays the share of employment in each industrial sector or sector grouping for young college graduates ages 22 to 27, all college graduates, and young workers without a four-year college degree. Industries in the figure appear in order of the share of young college graduates they employ, from largest to smallest.

The types of jobs where young college graduates work look similar to those of college graduates generally. Young workers without a college degree (i.e., noncollege) are far more likely to work in trade, transportation, and utilities; mining, construction, and manufacturing; or leisure and hospitality. All groups of workers are least likely to work in the information sector, closely watched for signs of AI-induced displacement.

Figure A

Figure B shows the change in employment in each sector between 2019 and 2026 and between 2023 and 2026, arranged in the same order as Figure A for comparison. The two fastest growing sectors since the last business cycle peak occurred in the two largest sectors for young college graduates: private education and health services and professional and business services.

Since the rollout of ChatGPT, many have looked at industries and occupations likely to be exposed to AI to see whether this has led to weaker job growth. Among the most closely watched of these industries is the information sector, which has seen an 8.5% employment decline since 2023.2 While these losses are notable—and especially relevant to understanding AI’s fingerprints on the labor market for young college-educated workers—it cannot be overemphasized just how small this sector is in the overall economy. Less than 2% of overall employment is in the information sector, including only 2.3% of young college graduates. Further, the sector saw a rapid employment expansion between 2019 and 2023—the employment loss between 2019 and today is just 2.0%.

Figure B

High-tech sectors have relatively more college graduates but haven’t experienced large AI-induced employment losses

In recent years, the Census Bureau has created an experimental data series on high-tech industries to better understand business dynamics. These include both manufacturing and service sector components of high tech.3 We translate their NAICS classification for high-tech industries into Census Industry Classifications used by the Current Population Survey (CPS) to determine the likelihood of young college graduates working in these sectors.

In the 2026 economy, about 5.6% of workers were employed in what the Census considers high-tech industries. Just about 1 in 10 (9.9%) of the college-educated workforce works in the tech industry. Young college graduates are similarly represented: 10.3% work in tech.

Overall, the Current Establishment Survey tells us that tech industry employment tracked changes in overall private employment in the prior business cycle (between 2007 and 2019) but expanded sharply in the early 2020s and has softened a bit in the last three years. Since 2023, the tech sector has fallen by 0.7%. While overall employment using CPS does show modest growth, neither shows large swings that suggest a large impact for young college graduates.4

Weak hires may be the biggest culprit to labor market weakness for young college graduates

The Job Openings and Labor Turnover Survey (JOLTS) can shed light on the question of whether entry-level workers—with or without college degrees—are facing a harder labor market to break into. While JOLTS doesn’t include demographic characteristics, it presents jobs openings as well as rates of hiring, layoffs, quits, and other separations. Today’s economy has substantially less churn than during the recovery from the pandemic, when millions of workers reentered the labor market after mass layoffs—many quit soon after as they searched for, and generally found, better opportunities.

Figure C shows the hires and separation rates. The lighter colors represent the monthly seasonally adjusted data for each series while the darker colors represent a 12-month moving average that provides a better overall picture of recent trends, smoothing out some data volatility. Over the last five years, the hires rate has steadily fallen and now sits at levels last seen in 2013 and 2014, when the labor market was still struggling to recover from the Great Recession.

The total separations rate includes quits, layoffs and discharges, and other separations.5 As with the hires rate, the separations rate has been declining over the last few years and now sits about where it was in 2014. Much of this is due to reductions in quits. Quits are higher when workers feel confident that they will find better job opportunities. Right now, workers are sitting tight, more so than any point in the past 10 years. Taken together, there is simply less churn in the labor market. But reduced churn is not inherently bad. If the frantic labor market of the early 2020s led to many workers and employers finding satisfactory matches, it could make sense that the following years would see less churn than normal. But for young workers looking to enter the job market, a reduction in hiring can make it harder to find a job.

Figure C

Table 1 breaks down the change in the hires and separations rate over the last three years, again using 12-month moving averages to smooth some volatility in the data. The industries are listed in order of the share of young college graduates they employ, similarly to Figure A. Overall, the hires rate fell 0.8 percentage points and the separations rate fell 0.6 percentage points between 2023 and 2026. The industries where young college graduates are more likely to work saw smaller reductions in both hires and quits than the overall. Industries where young workers without a college degree are more often found—over a quarter are in trade, transportation, and utilities—saw greater losses. Finally, leisure and hospitality, where young noncollege are more than twice as likely to work as young college graduates, saw the largest declines in hiring.

It does not appear that the industries where young college graduates tend to work are experiencing more weakness than other industries. Job gains are just as strong, if not stronger, and hiring hasn’t fallen as far in other industries. In short, there does not seem to be any profound structural change in the economy affecting the industry composition of employment—AI or anything else—that would easily explain the softening of the labor market for young college graduates in recent years. What it does appear to be is a harder labor market for young workers to break into when employers are less likely to hire and workers are more likely to sit tight in the job they have.

1. Throughout this blog post, we define young college graduates as people between the ages of 22 and 27 with only a four-year college degree. Unlike similar analyses of young workers, we do not exclude young college graduates that are currently enrolled in school, but the results here are robust either way. Unless otherwise noted, data for 2026 represent a 12-month average from April 2025 through March 2026 for the most up to date and reliable estimates, which removes seasonality and increases sample sizes.

2. ChatGPT was first introduced in November 2022 but took several months for more widespread usage.

3. High tech industries: Computer and Peripheral Equipment Manufacturing, Communications Equipment Manufacturing, Semiconductor and Other Electronic Component Manufacturing, Navigational, Measuring, Electromedical, and Control Instruments Manufacturing, Aerospace Product and Parts Manufacturing, Software Publishers, Data Processing, Hosting, and Related Services, Other Information Services, Architectural, Engineering, and Related Services, Computer Systems Design and Related Services, and Scientific Research and Development Services.

4.  It’s not unusual for the CPS and CES to display small differences in employment levels or trends considering nontrivial differences in their methodologies. 

5. Other separations include separations due to retirement, death, disability, and transfers to other locations of the same firm.

On Thursday, May 7, 2026, Economic Policy Institute’s Chief Economist Josh Bivens and Senior Economist Ben Zipperer joined Director of Government & Advocacy Samantha Sanders in a conversation on how policymakers should respond to the rise of AI.

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Today, 40 organizations led by the Economic Policy Institute, We Build Progress, the AFL-CIO Tech Institute, and Workshop delivered the letter below urging Congress to center workers in federal AI legislation. 

Dear Member of Congress: 

Employers’ increasing use of AI systems has the potential to affect the lives and livelihoods of workers across the country. Without appropriate guardrails, employers’ integration of these technologies may jeopardize workers’ rights, put workers at risk of discrimination, violate privacy rights, and dramatically impact the economic stability of working families.

These risks posed by technological change are not new. For years, employers have used algorithmic or automated systems and similar technologies in ways that harm workers. Now, the pervasive and growing integration of AI into the workplace is amplifying these risks. These impacts on workers are further exacerbated by persistent power imbalances in the labor market that favor employers. 

It is urgent that Congress take action. It has been nearly two years since the Bipartisan Senate AI Working Group released its roadmap for AI policy, but the Senate has yet to consider comprehensive legislation. AI adoption is moving forward at breakneck speed, and America’s workers cannot afford to wait. 

We applaud members of Congress who have introduced worker-focused legislation addressing issues like civil rights, surveillance in the workplace, and improvements to labor market data. Efforts at broader federal reform must also center the impacts of AI on workers. Under these circumstances, we urge the newly formed House Democratic Commission on AI and the Innovation Economy to center the recommendations of members with expertise in workers’ need for strong labor protections and AI’s impact on the economy. 

The urgency of this moment is further compounded by the Trump administration’s decision to prioritize corporate capture over the public good. In December, after Congress again declined to preempt critical state efforts to regulate AI, President Trump issued an Executive Order that purports to block states from protecting their own residents—a move that blatantly infringes on states’ rights while offering no federal alternative. The administration has doubled down with a national AI legislative framework that would severely curtail states’ ability to regulate AI. Rather than respecting states’ authority to protect their own residents, the administration is doing the bidding of tech oligarchs. 

The AI industry, venture capitalists, and lobbyists spent hundreds of millions of dollars last year pressuring Congress to pass legislation that would prevent state lawmaking. These attempts have failed multiple times because a significant number of members across both parties recognize the dangers posed by AI, while industry actors continue to push for deregulation. 

This is not what the public wants. Recent polling shows a bipartisan consensus in support of AI safety measures: 88% of Democrats and 79% of Republicans favor maintaining existing rules for AI security. Many people want more guardrails on AI: Majorities of both parties are in favor of new regulations to protect society, including 63% of Democrats and 59% of Republicans. 

Federal action is necessary, but it must also leave states room to innovate. Not all states are taking action, so Congress must provide a baseline of protection for people across the country, with a core focus on workers’ rights and livelihoods. 

But federal legislation should be a floor, not a ceiling. Locking the U.S. into a static, insufficient federal framework would guarantee that protections will swiftly become obsolete. It’s important that policymakers do not build a framework that is so narrow or rigid that it fails to keep up with constantly changing AI risks and shifting economic conditions, leaving workers vulnerable to new risks from new tools and practices.

A strong federal framework can create a reinforced system of guardrails to help working people navigate the growing use of AI. Congress has a responsibility to act now—the well-being of our workers and communities depends on it.

Sincerely,

AFL-CIO 

AFL-CIO Tech Institute 

AFT 

American Federation of State, County and Municipal Employees

Americans for Responsible Innovation 

California Initiative for Technology and Democracy

California School Employees Association 

Care in Action

Center for Democracy & Technology 

Center for Oil & Gas Organizing

The Century Foundation 

Communications Workers of America (CWA) 

Consumer Federation of America 

Data & Society 

Economic Policy Institute 

Encode AI 

Future of Life Institute

Interfaith Center on Corporate Responsibility

Jobs With Justice 

The Leadership Conference on Civil and Human Rights 

Legal Aid Justice Center 

Louisiana Progress 

National Action Network 

National Association of Voice Actors 

National Black Worker Center 

National Domestic Workers Alliance 

National Employment Law Project 

National Employment Lawyers Association 

National Institute for Workers’ Rights 

National Partnership for Women & Families 

National Women’s Law Center 

Open MIC (Open Media and Information Companies Initiative) 

Oxfam America

Public Citizen 

Service Employees International Union (SEIU) 

TechTonic Justice 

United Church of Christ Media Justice Ministry

United Food and Commercial Workers International Union 

We Build Progress 

Working Partnerships USA 

Workshop 

Writers Guild of America West

Originally held Thursday, April 9, 2026.

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Rising authoritarianism and the need to upend the failed Southern economic development model

For generations, Southern politicians backed by powerful business interests have promoted a Southern economic development model—characterized by low wages, regressive taxation, lax environmental regulations, a weak social safety net, and vicious opposition to unions—while claiming such policies will attract business and thereby generate regional economic gains. But data actually show a grim reality. The South lags all other regions on most indicators of economic health including job growth and wages, and Southern workers and their families experience significantly higher rates of poverty than in other parts of the country (Childers 2024a).

The truth is that this Southern economic development model was never designed to benefit most Southerners; rather, it is historically rooted in efforts of white plantation owners to retain their wealth following emancipation and ensure continued access to the labor of Black people for as little compensation as possible (Childers 2025). Foundational to these efforts was an authoritarian approach to state governance that suppressed popular democracy and worker organizing—an approach that also sanctioned prison labor, sharecropping, a century of Jim Crow laws, lynching, and other forms of state-sponsored terror and exploitation. Until partially challenged by federal legal and policy interventions won by post-WWII civil rights movements, many Southern states for decades held elections that served merely to provide a cover of legitimacy to one-party rule of white, wealthy elites—functionally excluding Black voters from the electorate and blocking working-class constituencies from any meaningful participation in governance (Mickey 2015; Perez 2024; Mast 2025).

Today, the Trump administration’s increasingly authoritarian actions echo this troubling Southern history. At their foundation, the administration’s approaches to bypassing constitutional checks and balances—while rolling back civil rights, worker rights, and environmental protections; terrorizing immigrant communities; deploying military troops in U.S. cities; and attempting to engineer election outcomes via gerrymandering and other forms of voter suppression—are rooted in authoritarian models developed and tested in the U.S. South, and that Black, brown, and immigrant communities across the country are no stranger to.

Recent attempts to terminate federal employee collective bargaining agreements, for example, are familiar to public employees in Southern states for whom collective bargaining has long been banned or severely restricted. The Trump administration’s use of military-style policing in communities across the country echoes Southern histories of weaponizing law enforcement (or National Guard troops) to suppress organizing and instill fear, while prioritizing the expansion of the carceral state over investments in housing, education, and public services. Trump’s efforts to override the authority of state officials mirror Southern state uses of abusive preemption laws to strip policymaking authority from local governments. And administration attempts to halt clean energy investments and environmental protections threaten to repeat harms familiar in Black and brown communities in the South, where corporations have insisted on lax environmental regulations that allow them to degrade air, water, and climate quality, while profiting from the exploitation of local natural resources and labor.

Seizing opportunities to reverse decades of anti-worker, anti-democratic policymaking in the South at a moment of rising authoritarianism in the U.S. is a daunting and unavoidably urgent challenge. It will require robust new forms of multiracial organizing and labor-community coalition building across a broad set of industries in the South. Labor-community coalitions can leverage community benefits agreements (CBAs) as a powerful tool to transform economic power relations in Southern workplaces and communities. Because CBAs are private agreements between labor-community coalitions and project owners, they do not rely on government action and can therefore shape economic outcomes of major projects even in otherwise hostile political environments. CBAs have traditionally been fought for and won by labor and community groups coming together and building necessary public pressure to hold developers, corporations, and elected leaders accountable for ensuring that public investments in major new developments truly benefit workers and communities.

In this report, we analyze the potential for labor-community coalitions to pursue strong CBAs that secure significant economic benefits for Southern manufacturing workers and communities, drawing on examples of existing agreements to model potential impacts. We examine the scale of recent public investments in Southern manufacturing and examine how strong CBAs on major publicly-subsidized private projects could improve the quality of newly created construction and production jobs; open up pathways to unionization; ensure equitable hiring and training opportunities for local residents; and address community needs such as child care, affordable housing, and natural resource protection.

We contend that upending the failed Southern economic development model and the authoritarian structures that underpin it will require building new forms of labor and community power to increase union density in the South. Well-known research shows that unions promote economic equality and help workers win improvements in pay, benefits, and working conditions (Economic Policy Institute 2021). But unions also powerfully affect people’s lives outside of work. They help foster solidarity, increase democratic participation, enable working-class communities to shape economic policies affecting their lives, and serve as a counterweight to corporate power in our economy and democracy (McNicholas et al. 2025). Historically, unions have been engines of resistance to entrenched and undemocratic power—mobilizing working people to challenge inequality, defend civil rights, and push back against authoritarianism in all its forms. For all these reasons, strengthening labor-community coalitions and pathways to unionization in growing Southern industrial sectors is not just good economic policy—it is also a democratic imperative amid national authoritarian backsliding.

Worker and community power can ensure new manufacturing investments yield good jobs and community benefits

The latest wave of manufacturing growth in the South presents both opportunities and pitfalls for workers and communities. Southern states continue to lure businesses—including large manufacturing facilities—with promises of low corporate tax rates, low wages, lax regulations, and massive public subsidies. The automotive manufacturing industry has been a key recipient of public subsidies, receiving billions of dollars from Southern states in recent decades (Childers 2024a; Todd 2021). This system of low taxation and corporate giveaways starves other essential public goods, like education and social safety net programs (Mast 2025b). Likewise, weak or nonexistent environmental regulations have contributed to toxic sites and resource degradation that disproportionately affect Black and brown families, reflecting often intentional decisions to site hazardous facilities in low-income communities of color (Bergman 2019).

Some announced manufacturing projects have been cancelled or reduced in size after the Trump administration’s slashing of federal supports for strategic industries, but many projects launched during the Biden administration continue to move forward. These manufacturing investments, both in traditional industries and nascent ones such as electric vehicle (EV) and EV battery manufacturing, are spurring significant job growth in some Southern communities. Yet past experience shows that new investments and resulting jobs are unlikely to generate economic benefits for most Southerners unless local residents are able to ensure that developers and corporations respect workers’ rights, protect local natural resources, and contribute a fair share toward addressing priority community needs.

Community benefits agreements can be powerful vehicles for communities to secure lasting local economic benefits from major industrial development, at both new and existing facilities. A CBA is a legally enforceable contract between a private developer or company and a local coalition—typically made up of labor, community, faith, environmental, and other grassroots organizations—that details how a project will benefit workers and the community, and in turn how the community will support the project (including via potential public investment). Benefits spelled out in a CBA can include commitments to strong labor standards; respect for workers’ rights to organize; equitable workforce recruitment, training, and hiring practices; affordable housing; environmental protections; or a broad range of other community-identified priorities. CBAs are a well-developed model for responsible community development—so far mostly, but not entirely, in regions outside the South—and have been used for many different types of major projects including sports stadiums, events centers, manufacturing plants, airports, transit projects, and more (WRI n.d.).

CBAs can likewise mitigate risks for project developers by ensuring local project support and addressing important concerns early on, whereas failure to engage local communities in major development decisions can otherwise lead to strong community opposition, interruption of development, obstacles to obtaining necessary siting permits or rezoning approvals, or significant legal costs. In an example from June 2024, developers shelved plans for a $1.3 billion data center in Indiana after facing significant local opposition over environmental concerns (Fazili et al. 2025).

Because a CBA is a private, legally binding agreement, it does not require government action and can be used to shape outcomes of major projects even in contexts (as in most of the South) where state legislators have preempted local governments from establishing their own job quality or environmental standards (EPI 2025a). That being said, state and local governments can still have a role in facilitating, negotiating, or enforcing community benefits. Cities like Detroit and Cleveland have ordinances requiring developers of projects using public resources to engage in a community benefits plan process (City of Detroit n.d.; City of Cleveland n.d.). In 2005, Atlanta passed an ordinance specifying worker and community benefits for the Beltline redevelopment (WRI 2025). However, government involvement in community benefits plans does not guarantee strong agreements on its own. A strong labor-community coalition remains essential for securing meaningful community benefits.

Another key strength of a CBA is that it can set standards across all stages of a project’s development to ensure long-term benefits for the community at large. Private developers or public entities sometimes negotiate Project Labor Agreements (PLAs) or Community Workforce Agreements (CWAs) with building trades unions and community partners to set wages, working conditions, and timelines for the construction phase of a complex development project. A CBA can be negotiated alongside a PLA to also ensure pathways to quality jobs for local residents during the operational phases of a project, including any future expansions of the facility or additions to its workforce. A CBA can also secure commitments to build affordable housing, strengthen environmental standards, and provide other benefits to the community such as child care, public parks, or other community spaces.

To be successful, a CBA must also include defined enforcement mechanisms that hold all parties to the agreement accountable. It must clearly establish the obligations of each party, metrics for measuring progress, and ongoing monitoring of compliance with the agreement’s provisions (Last 2025; PWF and CBLC 2016). If the company or the coalition fails to make good-faith efforts on the agreement’s commitments, an arbitration process is initiated. While monitoring of the agreement is an ongoing responsibility of all members of the coalition, providing a pathway for workers to organize in the operational phase of a project is of particular importance. A newly established union at the project site is well-positioned to monitor the commitments of the CBA and hold the company accountable over the long term.

Organizers and advocates should be clear-eyed that while strong CBAs can yield powerful economic outcomes, such agreements are by no means easy to win. There are generally no legal requirements for a particular company or developer to recognize or engage with a labor-community coalition, much less to agree to negotiate and implement a CBA. Building the broad-based, durable coalitions and leverage necessary to compel private interests to engage in CBA negotiations (and then to implement and enforce the terms of a CBA) is unavoidably a challenging, long-term, resource-intensive organizing project. And like any worthwhile organizing, the formation of strong, durable labor-community coalitions is itself a key outcome of successful CBA campaigns. Vastly expanding the capacity of broad-based coalitions and labor, faith, environmental, and other grassroots organizations to gradually build community and worker power in Southern communities is the most essential ingredient for transforming existing power imbalances and, ultimately, upending the failed Southern economic development model.

Indeed, recent initiatives to win CBAs in Southern states have proven so threatening to some corporate interests that they have sought to undermine them. In 2025, Tennessee Republicans passed legislation prohibiting any company that enters into a CBA from receiving state economic development funds—aiming to create obstacles to replication of a highly successful CBA covering Nashville’s soccer stadium, and to discourage a coalition of West Tennessee residents and allied groups calling on Ford and SK Innovation to negotiate a CBA covering its massive BlueOval electric vehicle and battery manufacturing complex (Abrams 2025). In Tennessee and elsewhere, however, labor-community coalitions are nonetheless continuing to organize to ensure that massive, publicly subsidized new facilities yield good jobs and community benefits.

A new wave of Southern manufacturing is an opportunity to transform working conditions in growing industries—and across the South

Growth in Southern manufacturing industries presents a significant opportunity for labor-community coalitions to shape labor standards and community benefits in new plants and facilities—and to shape economic outcomes for generations of Southern workers to come. In recent years, the South has seen a wave of manufacturing investments. Between 2017 and 2023, manufacturing construction doubled in the East South Central Census division (Alabama, Kentucky, Tennessee, and Mississippi) (O’Brien 2023). The West South Central division (Arkansas, Louisiana, Oklahoma, and Texas) has the highest amount of manufacturing construction spending of any division in the U.S. These investments are part of a long-term trend of manufacturing industries locating in the South, which in recent years was accelerated by large federal investments through the Inflation Reduction Act, Infrastructure Investment and Jobs Act, and CHIPS and Science Act. These federal investments included both direct public subsidies and tax credits to businesses that invested in key clean energy manufacturing industries such as the production of batteries, electric vehicles, solar panels, and wind energy products.

In contrast to the typical economic development approach of many Southern states, some recent federal investments have included incentives meant to encourage strong labor standards on projects receiving public funds. While the future of many of these investments (and accompanying incentives) is now uncertain, the U.S. has in the past two years experienced its largest investment in clean energy manufacturing ever, and much of that has occurred in Southern states.1 Since the third quarter of 2023, more than $125 billion worth of clean energy manufacturing investments were announced across Georgia, North Carolina, South Carolina, Tennessee, Kentucky, and Texas (CET 2025). Advancing even a portion of these projects would result in thousands of jobs for Southern workers.

Independent of the future of federal support for clean energy manufacturing, the South will likely continue to be the largest manufacturing employer of all U.S. regions. Figure A shows manufacturing employment by region in the United States since 1990. While manufacturing employment overall has fallen during the last three decades, the South has retained the largest share of manufacturing employment of any region. In 2024, 35% of U.S. manufacturing employment was in the South. Furthermore, since 2010, manufacturing employment in the South has grown by 17%, the quickest growth of any region.

Figure A

Manufacturing jobs are often considered to be well-paid, benefit-providing “middle-class” jobs, but there is nothing inherent to the sector that determines their quality. Manufacturing jobs in some industries became “good jobs” thanks to relatively high levels of unionization during the mid-20th century, which improved wages, benefits, and working conditions (Bayard et al. 2024; Rhinehart and McNicholas 2020). As Figure B shows, unionization in manufacturing has fallen in all regions since 1983, but the South has almost without exception had the lowest unionization rate of any region.

Conservative Southern policymakers have long been hostile to union organizing. For example, every Southern state except Maryland and Delaware has passed anti-union so-called right-to-work (RTW) laws, which make it harder for workers to form, join, and sustain unions. Southern states like Florida and Arkansas were among the first to pass such laws in the 1940s, amid a wave of big business backlash against new federal labor laws and white supremacist campaigns to maintain racial hierarchies and suppress multiracial worker organizing. RTW laws suppress unionization rates and, as a result, have driven down wages for both union and nonunion workers alike across the South (Sherer and Gould 2025; Childers 2023).

Figure B

In 2025, Southern manufacturing had a 6.7% unionization rate—slightly below the national unionization rate for private-sector workers (6.8%). Unionization in Southern manufacturing grew by more than a percentage point between 2024 and 2025, a notable one-year reversal of the industry’s long-standing unionization decline, consistent with overall union gains in the South (McNicholas, Poydock, and Shierholz 2026). Nevertheless, Southern manufacturing’s unionization rate remains well below the Midwest’s (11.2%), the region where manufacturing is the most heavily unionized. Unions have a strong impact on job quality because they leverage worker power collectively to raise wages, win benefits like health care and retirement, and enact other meaningful workplace improvements, such as improved health and safety standards. These benefits can extend beyond unionized workers themselves, helping set standards across a workplace, and with enough density, across an industry.

As unionization declines in an industry or region, so does job quality. For instance, as unionization rates have fallen in auto manufacturing, the pay advantage for auto workers compared with the median worker has declined significantly (Barrett and Bivens 2021). Figure C demonstrates how this relationship holds across regions in 2025. Manufacturing jobs in the South have a pay advantage of 7%, the lowest of any region. Southern manufacturing workers also experience the lowest median hourly pay of any region ($24.41).2

Figure C

The Southern economic development model clearly hurts the region’s workers by denying them their right to organize and suppressing their wages, but there are harmful spillover effects for their communities as well. Corporate tax breaks with no strings attached provide billions of dollars to corporations that could otherwise be used to invest in schools and other essential government services. These types of tax breaks might be worthy of consideration if manufacturing employers were required to create high-quality jobs for local workers and make long-term investments in local community development needs (i.e., housing, infrastructure, education, etc.). Without such protections, they are simply taxpayer-funded giveaways that often drain the very resources needed to develop the local workforce recruited by large new facilities.

Southern states enact little to no regulation of workplace safety or environmental pollution. This results in unsafe workplaces with greater levels of injury and death (Childers 2024a). Environmental pollution from manufacturing sites can negatively affect public health by contaminating water, air, and soil. New manufacturing investments also can mean significant changes to the demand for housing in a community. A new plant or factory can drive up the cost of living for nearby residents without yielding any economic benefits to a local community. Labor, community, and environmental groups need to collaborate on shared solutions to effectively address these intertwined challenges.

Labor-community coalitions can obtain commitments that ensure “economic development” means shared prosperity for all

Labor-community coalitions organizing around manufacturing projects can secure commitments that offer direct economic benefits to workers and communities, while also establishing groundwork for the growth of worker and community power in the area. While a campaign to win a CBA can be the impetus for forming a local labor-community coalition, the alignment and relationships built through this shared work can lead to longer-term, sustainable coalitions capable of transforming local and state power relationships.

The following section analyzes a set of commitments that can be included in a CBA for a manufacturing project. The CBA framework is flexible and allows for the inclusion of many different types of commitments prioritized by particular groups of workers, community members, and environmental groups. This report focuses on key types of commitments including union neutrality agreements, living wage floors, equitable workforce development practices (such as local or targeted hire policies and programs to expand pathways to apprenticeship training), affordable housing provisions, child care benefits, and environmental protections. Each type of commitment is analyzed in terms of its economic impacts and effectiveness in reshaping local economic development to ensure that public investments generate broadly shared community benefits.

The construction phase and Project Labor Agreements (PLA)

This report mostly focuses on community benefits for workers during the operational phase of a manufacturing plant. Nevertheless, it is just as vital to set high labor standards during the construction phase. Strong community benefits agreements are ideally developed in tandem with strong project construction labor standards set via project labor agreements (PLAs). A PLA is a multiparty agreement between a project owner and a coalition of labor unions that sets out labor standards and dispute resolution procedures to promote stability and efficiency on complex infrastructure projects while also ensuring the project will generate good jobs. PLAs ensure that construction projects run smoothly, are safer, and pay workers fairly (Mangundayao, McNicholas, and Poydock 2022). By setting negotiated wage and benefit levels for each type of work on a project, PLAs level the playing field in highly competitive construction bidding processes; they ensure that contractors base bids on their ability to deliver on quality and efficiency, rather than low-ball cost estimates that reflect intent to pay substandard wages or cut corners on safety. By standardizing wage and benefit levels and taking them out of the competition in the bidding process, PLAs incentivize the use of skilled union labor, which is 14% more productive than nonunionized construction work (McFadden, Santosh, and Shetty 2022). PLAs typically set wages, fringe benefits, and working conditions but can also include requirements to utilize certain numbers of apprentices, hire locally or from certain target worker populations, and/or provide child care or other benefits that open up pathways to good union construction jobs for members of underrepresented groups.

Several of the types of standards for construction workers typically included in a PLA have analogous labor standards in the operational phase. For instance, a CBA can secure commitments for local or targeted hiring and the development of registered apprenticeship programs in a manufacturing facility, extending equitable recruitment and high-quality training requirements that a PLA typically sets for construction into the operational phase of a project.

Removing obstacles to unionization: Neutrality and labor peace agreements

Protecting workers’ freedom to unionize has historically been key to turning manufacturing jobs into good jobs. This remains just as true today. However, like workers across the country, Southern manufacturing workers continue to face formidable obstacles—including weak labor laws, powerful anti-union corporations, and hostile politicians—to exercising their legally protected rights to form or join a union. Employers are charged with violating federal labor law in more than 40% of union elections and spend more than $400 million a year on “union avoidance” consultants (McNicholas et al. 2019; McNicholas et al. 2023). Because existing weak labor laws do not effectively deter employers from union busting, these tactics are treated by many employers as a normal cost of doing business—stacking the deck unfairly against workers seeking to exercise their rights to organize and collectively bargain.

Union neutrality agreements can help safeguard workers’ right to form unions free of the types of interference employers often deploy. Under a neutrality agreement, an employer agrees to remain “neutral” and not interfere with workers’ decisions on whether to unionize. Such agreements typically include joint commitments to a “card check” process for verifying whether a majority of employees have indicated interest in forming a union. Unions and employers sometimes also enter into a labor peace agreement, where unions agree not to engage in certain types of picketing, work stoppages, or other economic disruptions during the organizing process in exchange for employer neutrality.

Employers can also choose to commit to union neutrality as a matter of principle or company policy. Union neutrality—providing workers a more free and fair choice to decide whether to unionize—has been a key component of successful unionization drives in Southern manufacturing. To take two recent examples:

• In 2024, workers at the Volkswagen (VW) Chattanooga plant voted to join the United Auto Workers. Like many European corporations, the German-based VW has an established policy of maintaining neutrality in union election processes, although workers still voiced concerns that in its U.S. facilities, VW management tried to intimidate and dissuade workers from forming a union (Bomey 2024).
• In tandem with community benefits agreement negotiations with New Flyer in Anniston, Alabama, the United Steel Workers and Communications Workers of America negotiated three neutrality agreements with New Flyer and its subsidiaries in 2022. Over the two years that followed, these union neutrality agreements enabled workers to pursue five successful union drives, including at the New Flyer facility in Alabama (Last 2025; Sasha 2024).

Successful recent instances of union organizing in Southern manufacturing facilities have been powerful enough to generate their own backlash. Because of the threat that union neutrality agreements represent to the reigning Southern economic development model, several conservative state legislatures in the South have used model legislation developed by the American Legislative Exchange Council to pass laws intended to interfere with these agreements (Sachs 2024). While the legality of such measures remains in question and has not yet been tested, Alabama, Tennessee, and Georgia now all have legislation in place stating that employers who agree to a union neutrality agreement will be barred from receiving state economic development funds, disincentivizing companies from participating in these agreements (Stephenson 2024).

Importance of unionization to improve manufacturing jobs and wages

Securing unionization in Southern manufacturing can have significant wage benefits for workers. Unionized manufacturing jobs are more likely to provide family-sustaining wages. Unionization in manufacturing is associated with a 17.9% wage premium for workers (Scott et al. 2022). This means that compared with similar workers in terms of education, occupation, experience, race, and ethnicity, unionized manufacturing workers are paid almost a fifth more per hour than their nonunionized counterparts.

Table 1 translates this union premium into how much more unionized workers in the South could make on an hourly, annual, and plant-wide basis. The average nonunionized manufacturing worker in the South earns $34.50 an hour, so with the typical union premium, that worker would be earning an additional $6.18 an hour. If that worker works full time, year-round, the hourly premium translates to $12,846 more a year. To illustrate the potential impact of unionization in an entire plant, we take the example of the BlueOval auto manufacturing investment in Tennessee, which is projected to create 6,000 jobs (TN Office of Governor 2023). For a plant of that size, unionization could mean more than $77 million in additional wages for workers.

Table 1

Wage gains from successful unionization are not hypothetical for manufacturing workers in the South. For example, in 2024, workers at New Flyer in Anniston, Alabama, ratified a union contract with significant pay raises, with some workers gaining raises of up to 38% through 2026 (CWA 2024). Establishing a union contract with transparent pay ladders will also help New Flyer workers combat persistent pay gaps between white and Black workers in Anniston’s manufacturing industry (Erickson 2021).

The benefits of unionization go far beyond hourly wage increases. The workers at New Flyer also achieved significant gains in terms of vacation time and retirement contributions. Unionized workers secure critical benefits like health care and sick days at greater rates than their nonunion peers. Adjusting for differences in industry, sector, and region, union workers are 18.3% more likely to have employer-covered health insurance than their nonunion counterparts (EPI 2021). Almost 9 in 10 private-sector union workers have paid sick days, compared with less than three-fourths of nonunion private-sector workers (EPI 2021).

Unions also contribute to safer and healthier working conditions across a wide range of industries (Dean, McCallum, and Venkataramani 2022). By strengthening workers’ voice on the job, unions empower workers to report safety issues and demand better protocols. One example of this is that unionized construction sites experience significantly lower rates of Occupational Safety and Health Administration (OSHA) violations than nonunionized sites (Manzo IV, Jekot, and Bruno 2021). This is despite the fact that unionized workplaces actually experience greater rates of OSHA inspections than other workplaces, likely because many unions maintain active health and safety committees and because unionized workers have greater access to education on how to recognize safety hazards and are less afraid of reprisals from their employer for reporting them (Leigh and Chakalov 2021).

As the New Flyer agreement demonstrates, a strong CBA includes (or is negotiated in tandem with) union neutrality commitments ensuring that workers have a free and fair choice to unionize, without employer interference or retaliation. Securing a pathway to unionization can provide direct benefits to workers at a particular facility, while also increasing local organizing capacity and coalition strength for future negotiations over new projects and local development decisions. Not only is a new union a legally recognized institution that can monitor and hold the company accountable for commitments in the CBA, but it can also play a critical role in amplifying demands of workers and communities outside of the workplace and building power for working people more broadly.

Living wage floor

CBAs can also include commitments to minimum wage floors for the workers who will operate a new facility. For example, the 2018 Nashville Soccer CBA in Tennessee included a commitment to an hourly wage of at least $15.50 for stadium workers (SUN 2018). This provision set the stadium’s wage floor well above the minimum wage in Nashville, where workers—like all Tennessee workers and many across the South—are otherwise subject to the federal minimum wage of $7.25 an hour.

If a wage floor set by a CBA is high enough, it can help workers achieve a living wage in the place that they live. What constitutes a living wage must be determined by labor and community partners (Gould, Mokhiber, and DeCourcy 2024). For example, a living wage could be defined narrowly as covering the necessities for a single adult, or more broadly as including the needs of a working parent and their children. A living wage target must also make assumptions about nonwage income such as health care benefits and government transfers. Manufacturing workers in the South can also rightfully seek wages that not only cover bare necessities but provide the family-sustaining resources needed to be healthy and thrive.

Figure D shows the share of manufacturing workers in the South earning less than $30 an hour, or $62,400 a year in wages for a full-time worker. More than 3 in 5 (60.8%) manufacturing workers in the region earn less than $30 an hour. Around 80% of Southern Black and Hispanic manufacturing workers earn below the $30 threshold. Women in manufacturing are also more likely to earn below $30 an hour (71.8%) than men (59.1%).

Figure D

A $30 wage floor exceeds the minimum costs for a single adult in most jurisdictions in the U.S., but still barely covers needs for many families with children in manufacturing-dense counties nationwide. EPI’s Family Budget Calculator estimates living wage standards by county that cover modest but necessary costs families face like food, rent, and transportation in the United States. Table 2 shows three Southern counties with significant clean energy manufacturing investments in recent years (CET 2025). Each county has significant manufacturing employment, exceeding the U.S. average for manufacturing employment density. For each county, living wage standards from the Family Budget Calculator are listed for different family types. In Morgan County, Georgia, and Maury County, Tennessee, a single adult with a child must earn at least $30 an hour to cover basic needs. For a single economic provider to cover the costs of a four-person family, they must earn over $35 an hour in all the counties listed. These living wage standards indicate that a $30 wage floor would provide significant economic security for workers with smaller families or multiple wage-earners.

Table 2

A CBA that secures a strong living wage standard in a manufacturing facility can create a virtuous cycle that brings about greater prosperity in the area. Higher wages for low- and middle-income workers boost spending in the local economy because these workers spend a greater share of their paycheck than high-income workers (Anderson 2014). Other employers in the area might have to raise their wages to compete for workers with the CBA-bound employer. The establishment of a living wage also demonstrates to other workers in the area that higher wages are a feasible goal through collective action.

Local and/or targeted hire policies

Local and targeted hiring refers to policies that prioritize recruitment of individuals from the local community, or workers from specific groups who are otherwise underrepresented in a given workforce relative to local population demographics, such as women, people of color, veterans, low-income workers, formerly incarcerated workers, or workers with disabilities (Lawliss, Finfer, and Sherer 2022). A local hire policy can require that a certain percentage of hours worked on a project be completed by local workers. These policies can also require giving local workers the first option to apply for jobs on a project. For the prosperity created through manufacturing investments in the South to be shared equitably, it is important that local community members have access to the jobs that are created during both the construction and operation phases of a development. Workforce policies also should be designed to remove barriers to employment for groups of workers—especially workers of color and women—who have historically been excluded from many construction and manufacturing career opportunities. Increasing access to these well-paying jobs can increase economic mobility for workers with more limited opportunities.

Despite these benefits, some state policymakers have been hostile to local hire as a public policy. In 2015, Nashville voters passed a ballot initiative that required city-funded construction projects to dedicate 40% of construction hours to Nashville residents, with 25% of those hours going to low-income Nashville residents (Blair et al. 2020). The Tennessee state legislature then quickly passed a bill that preempted the city from creating its own local hire policy.

As Figure E shows, the harm of Tennessee’s preemption of local hire falls disproportionately on workers of color. The construction workforce in the Nashville metro area has a higher share of workers of color and immigrant workers compared with the state construction workforce overall. Black workers are 8.2% of the construction workforce in Davidson County, but 5.5% of the overall state workforce. More than half (51.5%) of construction workers in Davidson County are Hispanic, compared with less than a quarter (20.1%) of the state overall. Davidson County construction workers are also more than twice as likely to be immigrants (40.2%) than in all of Tennessee (14.8%). State preemption of local hire prevented Nashville from ensuring that public spending would benefit local workers. However, private agreements like CBAs offer an opportunity to incorporate local hire and/or targeted hire requirements into publicly subsidized developments, even in heavily preempted jurisdictions.

Figure E

In 2018, three years after the preemption of Nashville’s local hire policy, the labor-community coalition Stand Up Nashville was able to leverage $275 million in public subsidies for a new professional soccer stadium into a successful CBA (SUN 2018). The Nashville Soccer CBA included commitments to local hire for stadium workers, particularly workers from “Promise Zones,” i.e., high-poverty areas with fewer economic opportunities (SUN 2020). Through the CBA, Nashville Soccer Holding, LLC agreed to consider qualified Promise Zone resident referrals for jobs at the stadium. So far, the program has succeeded in hiring Promise Zone residents. In 2023, Nashville Soccer Club had hired 180 employees, 80 of whom were residents of Promise Zones (SUN 2023).

CBAs in the South and throughout the country are securing similar commitments to local and targeted hiring in clean energy and manufacturing investments. In Alabama, the New Flyer CBA commits the company to ensuring that at least 45% of new hires and 20% of promotions are members of “Historically Disadvantaged Groups” (Sabin 2022).4 In Massachusetts, a new offshore wind terminal entered into a CBA with the City of Salem—setting targets for hiring of local workers, workers of color, and women workers (Sabin 2024). The CBA for Maine Aqua Ventis, an offshore wind facility, includes local hiring opportunities for residents of Monhegan, Maine (Sabin 2017). 

These types of agreements help ensure that local residents benefit from large investments in their communities, particularly when policymakers have invested public dollars in the form of tax breaks or corporate subsidies to support a new facility. Ensuring local workers are prioritized in training programs and hiring processes for newly created jobs also helps community members stay in the area when housing costs are driven up by a large new manufacturing investment. And in the longer term, providing pathways for local workers to benefit directly from these investments strengthens the labor and community alliances needed to hold developers and corporations accountable over time.

Equitable workforce development through apprenticeships and pre-apprenticeships

In addition to local hire policies, which help create equitable pathways for local workers to secure good jobs at a manufacturing site, construction and manufacturing projects require a skilled workforce to operate safely and productively. A robust ecosystem of registered apprenticeship and pre-apprenticeship programs can help ensure both that employers find the skilled workers they need in a large new manufacturing facility, and that local workers can access pathways to newly created jobs.

Registered apprenticeship programs are training programs vetted by federal or state agencies to ensure use of high-quality, best-practice training standards and approved curriculum aligned with skills needed to succeed in a particular occupation. Registered apprenticeships combine paid on-the-job and classroom training and result in a recognized, portable credential certifying that a worker has the skills and experience necessary for a specific occupation. Pre-apprenticeship programs (also known as apprenticeship readiness programs) recruit and prepare participants for registered apprenticeships—often partnering with community organizations—to open pathways to apprenticeship for women, Black and brown youth, immigrants, workers with disabilities, or others historically excluded from skilled trades occupations. The best practice is for these apprenticeships and pre-apprenticeships to be joint programs between unions and employers, providing high-quality instruction tailored to industry needs and training that leads to placement in a high-quality job with wages, conditions, and benefits negotiated into a union contract. Often, a vital building block for successful manufacturing apprenticeship programs is the establishment of a unionized workforce at a facility.

Unlike lower-quality workforce development programs, registered apprenticeships pay workers fairly for their labor during their training—and in joint apprenticeship programs, the wages and benefits of apprentices are negotiated into a union contract and typically include scheduled increases as apprentices progress through the training program. Registered apprentices (across joint and non-joint programs) typically see their earnings increase 49% between the year before they enter the program and the year after completing it (Walton, Gardiner, and Barnow 2022). These increases in earnings are greater than for similar workers who do not enter the apprenticeship during the same time period (Katz et al. 2022). Apprenticeships can also be particularly attractive to workers because they are debt-free. Most apprentices (60%) consider debt avoidance the most important reason for choosing to enroll in an apprenticeship (Walton, Gardiner, and Barnow 2022).

Apprenticeships can be a powerful tool for increasing the diversity of construction and other industry workforces. While participation of women and workers of color in apprenticeships has grown in recent years, this growth has been painfully slow for decades (CEA 2024). Research finds that union-based (joint) apprenticeship programs have been more successful than other types of apprenticeships at increasing diversity in the construction industry (Ormiston and Bilginsoy 2024). Joint apprenticeships enroll a higher share of women, Black workers, and Hispanic workers than non-joint programs, and have higher program completion rates for all workers, including for women and workers of color. Community benefits agreements can secure commitments and partnerships that equitably grow this pipeline of workers and set enforceable local and targeted hiring goals which in turn spur diversification of construction and manufacturing apprenticeship programs.

For instance, the New Flyer CBA creates a partnership between the company and coalition partners to develop pre-apprenticeship and technical training programs that expand access to manufacturing jobs for workers with low incomes and from disadvantaged groups (Sabin 2022). For these programs to succeed, community groups and educational institutions must have an active role in shaping the programs and connecting workers to these opportunities. The development of a growing skilled workforce and a robust, high-quality workforce development ecosystem can in turn be a strong incentive for bringing more facilities to an area over time. In 2015, Polaris stated that a significant factor in its decision to choose Huntsville, Alabama, for a new production facility was the area’s skilled workforce (Polaris 2015). As more workers participate in high-quality training programs that lead to union jobs, the organized workforce of the region will grow, strengthening labor-community coalitions the next time there is an opportunity to shape new development in the region.

Child care

Child care is an essential but extremely costly expense for many working families across the South. Average annual infant care costs in the South range from $6,868 in Mississippi to $14,277 in Virginia.5 The Department of Health and Human Services recommends that 7% or less of family income go toward infant child care costs, but typical Southern families spend significantly more. In Alabama, infant care costs are 9.8% of median family income, while in Oklahoma the share is 15.4% (EPI 2025b).

Increasing access to high-quality, affordable child care not only makes work more accessible to parents (and especially to women, who on average continue to assume disproportionate care responsibilities), but is a powerful investment in children’s development that can help narrow class and racial inequalities (Morrisey 2020). In addition, child care workers tend to work for very low wages and experience poverty at greater rates than the typical worker.

A large manufacturing investment in a locality might produce a significant number of jobs, and in turn increase the demand of workers and their families to live nearby. This is likely to increase the need for child care services in the region. However, data show that child care employment has not kept up with manufacturing growth in Southern counties. Table 3 compares counties with high manufacturing density, where manufacturing employment makes up more than the national average (9% in 2009), with those with lower manufacturing employment density (EPI 2025c).

Table 3

Between 2009 and 2024, manufacturing employment in high-manufacturing-density counties in the South grew 15.9%, achieving faster growth than similar counties in the U.S. overall (12.1%). However, over the same period, child care employment only grew 4.5% in Southern high-manufacturing-density counties, far below the national rate of 14.2%. Child care employment growth in the South for low-manufacturing-density counties (22.3%) is also below the national level (28.5%). The South systematically underinvests in child care, despite its importance to a healthy economy in the region.

CBAs and PLAs have been used to secure both the construction of physical child care spaces and financial support for actual services. The Nashville Soccer CBA reserved 4,000 square feet for the development of a child care center (SUN 2020). In 2001, the CBA for the North Hollywood Commons mixed-use development project in Southern California secured a commitment to an on-site child care center. Fifty child care spaces at the center were reserved for low- and moderate-income families (Sabin 2001). In the Boston area, unions have secured Project Labor Agreements that seek to address the unique child care needs of the construction industry. The PLA for the Winthrop Center in Boston established a child care access fund to research, develop, and implement alternative child care models within the construction industry, with a particular focus on assisting single mothers with child care while supporting their career (NEREJ 2019).

These types of investments are vital supports for working families, particularly mothers, seeking to balance professional and care work. Combined with union neutrality for the child care workers at these facilities, commitments to providing child care can further elevate worker power in the region and help large new facilities recruit and retain the skilled, experienced workforces they need to succeed.

Affordable housing

Without strategies to address the housing needs of a community impacted by a new manufacturing investment, local residents can experience increased economic precarity or forced displacement. The local housing impacts of a large industrial investment can be complex. A significant manufacturing investment can make a local community more attractive as workers move into the area to be close to their place of work. Manufacturing investments are also likely to be paired with prospective real estate investments in anticipation of future development around the original project. State and local governments might use eminent domain and other purchasing mechanisms to secure land for roads and other new infrastructure. These dynamics can increase housing costs for residents, particularly renters who are most vulnerable to the impacts of housing speculation and prospective rent increases. For instance, the BlueOval development in West Tennessee is already reported to have increased property prices and housing rents (TCG 2023). Homeowners, particularly those with fixed incomes, can also be more burdened with housing costs as higher demand in the area increases property tax valuations (Payne 2019).

On the other hand, extreme proximity to an industrial site can expose residents to environmental hazards and noise pollution, and may be considered unsightly, which decreases property values (Currie et al. 2016; Upton and Talpur 2024). The exact distribution of these changes in demand for housing across a community will depend on the type of industry and any other types of development included in the project.

Industrial investments like manufacturing facilities tend to take place in rural and semirural areas, in part because land is relatively inexpensive (Wiley 2015). While the counties with a higher share of manufacturing employment tend to have lower housing costs than urban areas, housing affordability remains a significant issue for workers. On average, across high-manufacturing-density counties in the South, a two-adult, two-child household must cover more than $14,000 a year in housing costs.6 A large share of renters in high-manufacturing-density counties in the South still are cost-burdened by housing, meaning they spend more than 30% of their income on rent, utilities, and other housing costs. As shown in Figure F, across the Southern states, the share of cost-burdened households in high-manufacturing-density counties ranges from 28% in Arkansas to 47% in Florida. More than 2 in 5 (42%) of Texas renters in these counties are also housing cost-burdened.

Figure F

A strong CBA will secure commitments to build a certain number of affordable housing units or dedicate a share of housing at the site as affordable. The Nashville Stadium CBA created agreements that at least 12% of residential units in the development would be affordable and that 20% of those units would be three-bedroom units to accommodate families (SUN 2020). The Staples Center CBA in Los Angeles, California, was another successful example of strong affordable housing benefits. The 2001 agreement for the development of an expanded convention center, theater, and surrounding housing, hotel, and retail space secured commitments that 20% of housing units would be affordable. The developer also agreed to provide $650,000 in interest-free loans to nonprofit affordable housing developers in the local community (WRI 2001).

Even in situations where a labor-community coalition is unable to reach a final CBA with a company, coalition organizing around community demands can still deliver meaningful affordable housing victories. Between 2002 and 2006, a labor-community coalition in Denver pressured Cherokee Investment Partners to provide community benefits as part of their redevelopment of the site of the Gates Rubber Company. The coalition leveraged zoning changes necessary for the project and a potential subsidy package from the city to extract benefits including an affordable housing plan for hundreds of rental and for-sale affordable housing units (Ingram and Hong 2011; PowerSwitch Action 2025).

In 2005, the labor-community coalition organized by Georgia STAND-UP was able to attach community benefits to an Atlanta city ordinance allocating $2 billion in public funding for the Atlanta Beltline transit-oriented development project. The city resolution shaped by the coalition established an affordable housing trust fund and a goal of developing 5,600 affordable housing units (PowerSwitch Action 2025). As of 2024, more than 4,100 affordable units have been created as part of the project (Atlanta Beltline, Inc. 2024).

Labor-community coalitions can also pursue other land-use commitments beyond the development of affordable housing. The BlueOval Good Neighbors coalition in West Tennessee has demanded commitments to protect land for farmers in the area. The development of the Ford factory has pushed Tennessee’s Department of Transportation to pursue land for new roadways through purchase and eminent domain. The area targeted for new roadways is a majority Black farming community, and several farmers are engaged in lawsuits with the state over the state’s meager compensation offers for their land (Wadhwani 2023). The coalition has demanded that farmers be offered replacement land in exchange for their sold land, as well as the creation of a 10,000-acre community land trust (BlueOval Good Neighbors n.d.).

Creating or protecting affordable housing is essential for protecting the communities that are necessary for any effective labor-community coalition. Large developments can cause instability within the community as new residents arrive, and existing residents are buffeted by rising housing costs. Because of historic and ongoing racial discrimination in housing policy, labor policy, and real estate practices, the costs of these changes are most likely to impact Black and Hispanic workers. Black families and other workers of color are the most likely to be cost-burdened by housing (JCHS 2024). Creating housing for workers and families to remain in the area is vital for continued collective action to secure benefits from developers and hold those developers accountable for their promises.

Environmental standards, funding, and monitoring

Large-scale manufacturing projects often have significant environmental impacts, both during construction and once they are in operation. Air, noise, and groundwater pollution; harm to wildlife habitats; and residents’ exposure to toxic byproducts are just a few examples of common concerns, and these consequences can be severe when projects are approved without sufficient environmental consideration. The consequences of large manufacturing projects often disproportionately harm communities of color and low-wealth areas throughout the South (Brouk 2024). For decades, poor and Black residents in the region have been exposed to toxic chemicals, pollution, and other environmental dangers at alarming rates (Bergman 2019).

In 2021, the Tennessee governor approved the construction of a General Motors lithium battery supplier in the city of Spring Hill, on the banks of the Duck River. Though the project was seen as an economic success, the plant’s operation has taken a toll on the fragile river ecosystem. The lithium battery factory is not the only strain—just eight companies along the river drain tens of millions of gallons of water daily (Wadhwani 2024). This enormous water usage has lowered river water levels, threatened biodiversity, and harmed local tourism and recreation. Advocates for the river’s health blame the state’s prioritization of manufacturing expansion without regard to the long-term environmental or economic consequences for local residents or other existing local industries.

CBAs are a tool that may help community-labor coalitions address the environmental impacts of data centers in the South. Data centers are booming across the United States, but particularly in Southern states like Georgia, Texas, and Virginia (Walker and Goldsmith 2026). New centers are heavy users of water and energy, create noise and air pollution, and are driving up electricity costs nationwide both by increasing demand for energy and requiring utilities to invest in new infrastructure paid for by all ratepayers (Merchant and Guerra 2025; Bizo et al. 2021; AI NOW 2025; Reed 2025). For example, in Virginia, electric bills were on track to increase as much as 25% in 2025 because of data centers (Penn and Weise 2025).

Growing community concerns surrounding data centers could create leverage for labor-community coalitions to pursue CBAs and other community benefits strategies. In 2025, community opposition blocked or delayed $64 billion in data center projects across the nation (Data Center Watch 2025). As community resistance to data centers continues to grow, more developers may recognize the need to come to the table with local coalitions to negotiate binding commitments on environmental and economic outcomes to secure project approvals. A handful of localities have begun to create agreements with data center developers regulating water use and securing commitments to green energy use (Turner Lee and West 2026).

Past development projects provide examples of how communities have used CBAs to secure long-term commitments to clean energy transition and protection of local natural resources in a multitude of ways, from mandating that any new construction must meet specific sustainability standards to requiring companies to contribute a set dollar amount to a city’s renewable energy transition fund. In Virginia, the City of Richmond Resort Casino CBA ensured the developing and operating company would design and construct all project buildings to Leadership in Energy and Environmental Design (LEED) Silver standards and would use previously existing pavement where possible (WRI 2021). The agreement also required the developer to attempt to reduce the urban heat island effect by planting shade trees along sidewalks and using other landscaping methods (WRI 2021). These agreements can mitigate additional environmental harm in areas that have already been polluted. A CBA between the Town of Waterloo, New York, and Seneca Meadows, Inc. regarding a landfill expansion commits the waste management company to pay for the development of new public water lines and other potable water infrastructure if existing public water wells become contaminated (WRI 2005).

CBAs can also be used to expand the positive impact of an already climate-friendly project. In New York, a CBA with an offshore windfarm developer stipulates that the company must contribute $2 million to the town of East Hampton’s Ocean Industries Sustainability Program (WRI 2018). Additionally, Deepwater Wind South Fork, LLC must spend $200,000 to establish an Energy Sustainability and Resilience Fund to support East Hampton’s transition to 100% renewable energy (WRI 2018). CBAs with environmentally focused companies provide valuable opportunities for communities looking to address climate change, especially where state governments have failed to invest in environmental programs.

A CBA can achieve a variety of climate and environmental commitments from a company but is also a strong starting point for building local capacity to monitor resource use, pollution, and other environmental priorities. A strong coalition of community, labor, and environmental groups can play essential roles in implementing and enforcing CBA commitments in contexts where understaffed government agencies have limited ability to monitor or investigate pollution and other environmental harms. Instead, workers and community members are often the first to report harmful practices and safety concerns. A strong CBA can provide opportunities for labor and environmental groups to work together to monitor and protect worker and community health, natural resources, and ecosystems.

Conclusion

For decades, Southern economic policies shaped by dominant business and corporate interests have resulted in poor working conditions and failed to ensure that profits generated by publicly subsidized development are shared with local workers and communities. Confronting the deep, long-standing imbalances of power that have entrenched this failed economic development model will require significant organizing and coalition-building to increase the collective power of workers and community members to shape different outcomes from the latest Southern manufacturing boom. Building new forms of worker and community power will be equally necessary to counter escalating authoritarian actions of the Trump administration, which closely parallel many features of the failed Southern economic development model that by design prioritizes corporations over workers and communities.

Our analysis shows that community benefits agreements could be powerful tools for Southern labor and community groups building the shared power necessary to reshape local and eventually regional economies. When strong coalitions of labor, environmental, faith-based, and other grassroots community organizations are able to build the necessary power to bring a company or developer to the table to negotiate an enforceable agreement, such coalitions can secure measurable economic benefits like higher wages, respect for workers’ rights to unionize, local or targeted hiring, protection of natural resources, or more affordable housing. Such economic gains are beneficial in themselves, but they also raise expectations, build local capacity to pursue additional gains, and demonstrate to the community at large that local residents can shape their own economic futures, and that these types of victories are achievable in the face of the Southern status quo.

While the urgent project of upending the Southern economic development model will require vigorous and persistent organizing across many sectors and geographies, community benefits agreements are one key strategy for turning manufacturing jobs into good jobs, ensuring long-term local economic gains from new industrial investments, and even renewing democracy in contexts where it has long been suppressed. Forming strong, long-lasting labor-community coalitions is essential to winning concrete gains for local workers as well as reshaping the political fabric of Southern communities and increasing working people’s influence over broader state or regional economic policy decisions. Winning and implementing any strong CBA requires the formation of an empowered labor-community coalition, which ideally endures and gains greater strength, experience, and influence over time. Just as the economic benefits of unionization extend far beyond an individual workplace, establishing a strong CBA coalition can create broader positive impacts across a community or region—delivering higher-quality jobs; more equitable tax systems; stronger public services; and healthier, more inclusive political systems.

Acknowledgements

The authors wish to thank the AFL-CIO Center for Transformational Organizing for their partnership and invaluable contributions in the production of this report. The authors are also grateful to Athena Last and Ian Elder at Jobs to Move America and Ben Beach at PowerSwitch Action for their expert feedback.

Appendix

Appendix Table 1

Notes

1. Clean energy manufacturing includes manufacturing of batteries, electric vehicles, mineral products, solar energy products, and wind energy products.

2. Workers in Southern states experience lower wages than in other regions even after adjusting for cost-of-living differences (Childers 2023).

3. The facilities covered by these agreements included plants in Alabama, California, Kentucky, Minnesota, New York, and Wisconsin.

4. This category includes workers who are Black, Indigenous, and/or people of color; women; LGBTQ+ persons; systems-impacted people (formerly incarcerated people); persons emancipated from the foster care system; residents of Anniston, Alabama, lacking GED or high school diploma; and veterans.

5. Southern states excluding D.C., Delaware, and Maryland.

6. EPI analysis of Family Budget Calculator and Quarterly Census of Employment and Wages data.

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AI-related spending is providing much of the growth in the U.S. economy today. Business investments in structures and equipment (capex) that are driven by AI firms have accelerated noticeably in the past year. How much of this investment consists of imports rather than U.S.-based production is an open and important question. Even more important is the wealth effect on consumption from the AI stock boom, which seems to have firmly entered bubble territory. Combined, the capex spending and the consumption spending spurred by the stock market bubble are adding over a percentage point to GDP growth.

Worse, both types of spending seem fragile as medium-term sources of growth. The stock market bubble could deflate at any time, and when it does, it will almost certainly pull down much of the capex spending as well. After all, the entire reason for the frenzied capex build-out is the expectation of future profits. If these expectations radically change, the capex spending will evaporate.

If AI spending growth slows and the economy falls into a recession, policymakers should follow the typical recession-fighting playbook and use monetary and fiscal policy to boost the demand that was erased. The Federal Reserve should cut interest rates, and Congress and the president should direct fiscal aid to struggling families.

I then point to a couple of long-run observations about AI and its effect on labor markets, mostly echoing our arguments made in this report. One key finding: Despite widespread concern that AI will be strongly capital-biased, the profit share in the non-financial corporate sector has actually declined markedly since 2022.

For those interested, the PowerPoint and notes from the briefing are below.

PowerPoint presentation

Briefing notes

The Database of Biden Administration Actions on AI is a joint project from EPI and Workshop

Recent advances in generative artificial intelligence (AI) have sparked increased awareness and adoption of AI tools in the workplace. While AI systems, automated systems, algorithmic management tools, and similar tools have been used in the workplace for decades, this acceleration in capabilities and greater public attention have motivated more concerted and urgent policy efforts, including a focus on protecting and empowering workers.

During the Biden-Harris administration, leadership and agencies across the federal government acted to better understand and respond to the potential implications of expanded AI use for workers—a promising start for meaningful federal action to mitigate related risks.

Since taking office, the Trump administration has actively undone much of this progress, and industry is devoting significant resources to preventing meaningful protections at any level of government.A broad and ambitious vision for responsible AI governance is essential at a time when protections are under attack and AI-related risks are more pressing than ever. This Database of Biden Administration Actions on AI can serve as a resource for state, local, and federal efforts to tackle these challenges and opportunities.

The ban on state regulation of artificial intelligence (AI) contained in both the House and Senate versions of the Republican megabill is overly broad, dangerous to workers, and out of step with public interest. This provision – a ten-year blanket ban on state and local governments’ ability to protect their residents from the harms of AI – is a reckless giveaway to Big Tech that will have far-reaching consequences for economic fairness, worker power, and public trust. 

Both the Senate and House versions of the provision use an extremely broad definition of AI—including automated decision-making systems – tying the hands of state lawmakers from taking any meaningful role in how AI technologies are being rapidly rolled out in many sectors of society. The Senate version ties the moratorium on regulating AI to federal funding for broadband internet infrastructure – a program on which all 50 states and territories rely to make critical progress to improve connectivity.  

The provision is opposed by a broad, bipartisan coalition – including unions, civil rights groups, state attorneys general, members of Congress across the political spectrum, and the public, who understand it as a rash giveaway to big tech. Banning state regulation of AI gives even more power to a handful of billionaires, while reducing the power of working people and communities. 

Congress can still act to remove this harmful provision and ensure that AI can expand in ways that are responsible, innovative, and grounded in public trust—while protecting the rights of workers, consumers, and communities.

Congress has a responsibility to develop and adopt federal standards that ensure new technologies lead to positive economic outcomes – and to ensure that workers have the power to control how AI and related digital tools are used in their workplaces, ideally  through collective bargaining agreements. 

However, in the absence of federal action, it is critical that states be permitted to step in and act – and those lessons can hopefully inform federal policymaking. All 50 states have been working to regulate uses of AI that harm communities and society. This ban would stop all of that progress in its tracks, blocking commonsense AI laws in development or already on the books.  

Here are a few key ways this unprecedented ban on state action to protect workers and consumers could harm workers and erode public trust:

Make it easier for employers to discriminate

The right to equal opportunity at work is already under threat from the Trump administration’s attacks on federal anti-discrimination protections and enforcement.  Unregulated AI systems could speed up and cement discrimination even further. 

Major employers increasingly rely on predictive AI software and algorithmic analysis to choose who they interview, hire, promote, discipline, or dismiss. We know these untested tools for “automated decision making” can fuel discriminatory outcomes, such as a preference for resumes with white- and male-associated names. 

With no federal guardrails in place, and with federal enforcement on anti-discrimination weakened, banning state action would allow discrimination to flourish unchecked. States must be able to step in to address algorithmic bias and enforce anti-discrimination protections so that everyone has a fair shot at a good job. 

Make it easier for employers to drive down wages

With a low federal minimum wage, rampant misclassification of contract workers, and no guardrails on how employers use AI and algorithms to make decisions about pay, the race to the bottom already experienced by gig workers could become the norm in all industries. 

Employers already use algorithms and automatic decision systems to dynamically determine the lowest possible pay for each task, location and individual, with little transparency for workers. If states are blocked from even investigating wage suppression by algorithm, these exploitative practices will spread across all industries.

Increase retaliation, union-busting, and surveillance

Automated surveillance systems and AI-powered monitoring can track everything from workers’ keystrokes and voices to their precise location in their workplace. These tools can be weaponized against workers who organize, speak up about unsafe conditions, or simply take too long in the bathroom. Workers already are vulnerable to unfair – and sometimes illegal – retaliatory discipline and firing. If workers don’t even know the extent to which they are being surveilled, they will struggle to exercise their legal rights or defend themselves from wrongful termination – especially the majority of workers who lack the protections of a collective bargaining agreement.

As one school bus driver told researchers from UC Berkeley Labor Center:

“The bus cameras are the worst— they were originally installed to protect the kids, but now three cameras are pointed directly at us and recording at all times, even when no kids are on the bus. We know now that they use this footage in personnel matters, they listen to us through the bus cameras, and that they use the cameras to read our text messages when we are parked and using our phones while the children are off the bus and we are on breaks from work.”

Preventing states from regulating this kind of surveillance will leave workers more vulnerable to unlawful retaliation and to employer wrongdoing, unsafe conditions, and unfair wages.

Worsen worker privacy

A decade of unregulated AI will allow the aggregation and sale of vast amounts of highly specific data on individual workers in ways that can never be truly erased. For instance: With the aid of AI, data collected from GPS systems and wearable technology could be used to identify an employee’s private medical conditions, even before the worker has the chance to invoke the protections of the ADA or FMLA. If this data is sold to other hiring managers or the open market without any regulations, that same individual will find it difficult to secure future employment. 

Workers and consumers need transparency and tools to  control how AI-powered systems use their personal data – not a decade-long ban on state oversight.

Steal creative work

Artists, writers, musicians, and performers are already seeing their work scraped and reused by AI systems without consent or compensation.  The ban would make it more difficult for creative workers to protect their work, including their own images and voices. Those whose work is stolen without compensation to fuel large language models may have no recourse, even as big tech companies continue to profit. 

Harm public safety and public services

In critical sectors like healthcare, education, and transportation, AI systems are already being used to override expert human judgment. The ban would restrict the ability of workers and their advocates to respond. For example, in healthcare, nurses are fighting to protect patients and provide the care they know is best – even when an algorithm advises otherwise. This is equally true in education, childcare, public safety and transportation – all fields with vulnerable lives and worker safety at risk. 

A better alternative is possible

Corporations do not need a blank check and a deregulated landscape to succeed in creating and selling artificial intelligence, automated decision systems, and related technologies.  The balance of power already tilts too far in favor of employers. Congress should remove this dangerous 10-year preemption of state action from the budget megabill, which already poses serious harm to low-income people in this country. Instead, policymakers should consider responsible AI policy frameworks  through the normal legislative process, where these critical issues can be debated and assessed fairly. 

This report provides a brief overview of what is known so far about the economics of AI, highlights some accounts of how it is being deployed in anti-worker ways across different industries, offers a landscape analysis of federal legislation, and then presents policy recommendations on what federal legislation should be aiming to achieve given the latest research on the likely impacts of AI on workers and our economy. A key theme of the policy recommendations is to keep sight of the broader economic and institutional contexts in which AI might be deployed and to avoid tunnel vision in crafting policies that are too narrowly focused on the latest tools used by employers (rather than the underlying harmful practice).

The deployment of AI-powered technologies in the workplace has been associated with an array of worker experiences and outcomes that warrant concern, including discrimination, unsafe working conditions, seemingly arbitrary disciplinary action or discharge, among others. This has led many policymakers and advocates to put AI on the top of their priority list for drafting new legislation or regulations meant to protect workers. Policymakers have generally focused on assessing how the introduction of AI might degrade the on-the-ground experience of workers. While these assessments are illuminating, they have often steered policy responses toward solutions that are tailored to current forms of AI technologies or use cases. This approach, however, leaves policymakers in the position of potentially failing to keep up with other tools and practices available to employers that result in the very same outcomes (especially as technologies continue to advance) and runs the risk of falling short of providing meaningful long-term benefits to workers.

For years, employers have used algorithmic or automated systems in ways that harm workers—including through discrimination, diminishing workplace safety and privacy, and limiting decision-making power. And for decades, employers have been empowered to use garden-variety management practices that do all these things. Policymakers should rightly be alarmed by the experience of workers today, but they should look beyond the latest tool businesses are using to achieve these outcomes and instead look deeper to the underlying conditions that enable these results.

At the most simple level, the impact of AI technologies on workers is a function of the balance of power between businesses and their workers. If there is balance in the labor market because policy has empowered workers, then most new technologies (including AI) will be steered into generating productivity gains that can be shared across the economy. If the labor market remains unbalanced and workers disempowered, then lots of new technological tools will instead be deployed in zero-sum ways to keep wages suppressed and the incomes of capital owners and managers high.

The rest of this report builds to a set of recommendations for how policymakers should approach empowering workers in the age of AI. First, we review the likely economic impacts of AI technologies. Second, we provide a landscape analysis of existing federal legislative proposals. Finally, we outline three key pillars of a worker-centered policy strategy in the age of AI that aim to achieve a simple objective: increase the ability of workers to meaningfully engage their employers in how AI technologies are deployed in the workplace. These pillars are: 1) expand and expedite pathways to collective bargaining, 2) reduce AI-specific barriers to worker voice and strengthen employment protections, and 3) increase workers’ ability to leave employers who create exploitative conditions with AI (or other technologies and practices) and ease job transitions.

Labor market impacts of advancements in AI

There has been a great deal of speculation and attention given to the potential labor market impacts of new AI technologies over the last few years. In particular, many people have voiced worries about mass job loss and the impact of technologies on skills required for various occupations.1 Others have raised concerns about AI undermining core workplace rights and altering conditions of employment.2 In this section we identify what we believe are the known labor market impacts of AI. These evidence-based outcomes should serve as guideposts for evaluating current legislative proposals and inform additional policy recommendations.

Aggregate labor market impacts: Employment effects and employer demand for skills

While anecdotes and limited data analysis have fueled conversations about mass job loss, it’s important to consider a longer-run assessment of the impacts of technology on the labor market, drawing on a rich body of economic research and measurable outcomes during prior periods of technological advancement. 

Earlier this year, Bivens and Zipperer (2024) published a thorough assessment of AI’s likely impact on the labor market and workers, given the evidence from past waves of major technological progress. Their analysis highlights that technological advancements and subsequent productivity gains are highly unlikely to lead to mass joblessness.

Concerns that technological advancements will create significant job loss rest on the basic theory that AI technologies will increase productivity such that firms will use less labor, resulting in mass layoffs and a rise in unemployment. However, this theory muddles firm-level experiences with aggregate outcomes and glosses over a crucial piece of the equation: whether there will be other jobs in the labor market available—including those created by technological change—for workers who may be laid off from an individual firm.

Unemployment increases when the potential output of the economy (how much could be produced if nearly all of the labor force were employed) exceeds aggregate demand (total spending by households, businesses, and government). Thus, even if AI-based technologies result in an increase in productivity, whether this will result in an increase in unemployment depends on if aggregate demand doesn’t similarly increase. Bivens and Zipperer (2024) further note that fiscal and monetary policies enable aggregate demand to be boosted much more quickly than potential output can move, allowing policymakers to minimize the magnitude and duration of any disconnect between potential output and aggregate demand.

Relatedly, a review of the relationship between productivity growth and unemployment highlights that productivity gains do not occur at the expense of rising unemployment. In fact, Bivens and Zipperer (2024) find that “fast productivity growth is associated with lower average rates of unemployment across business cycles.” Moreover, their analysis shows that faster productivity growth does not hamper the pace of falling unemployment over a business cycle. In short, there is no compelling evidence to support the theory that AI-induced productivity gains will result in mass workforce displacement. 

Beyond aggregate employment effects, there has been a corollary concern among some policymakers about how businesses’ use of AI technologies may impact the demand for workers without a college degree. That is, as more complex technologies are developed and deployed in the workplace, will firms hire more highly skilled individuals for jobs that previously required lower skills? Here too, the theory for why this may occur is straightforward, but a dig into the data reveals why this is unlikely to occur in practice.

First the economic theory: An increase in use or advancements in technology will lead to an increase in demand for more highly skilled workers relative to workers who don’t have the skill set to use these technologies. Rising demand for higher-skilled workers will, as the theory goes, increase wages among those relative to lower-skilled workers, increasing income inequality. In general, economists have often used a college degree as a proxy for workers who obtain the skills necessary for new technologies. 

As Bivens and Zipperer (2024) discuss in greater length, a review of the data shows that this theory hasn’t played out in practice over the last 20 plus years. Since 2000, there has been little to no change in the college wage premium despite significant technological advancements. In fact, over the last few years the college-to-high-school wage premium has fallen, resulting in a decline in wage inequality between higher- and lower-skilled workers. Therefore, there is little evidence to suggest that continued advancements in AI will trigger a greater need for “upskilling” to counterbalance decreasing demand for lower-skilled workers (Bivens and Zipperer 2024). 

In the period since 1979, there has been a consistent shift in labor market power away from typical workers and toward corporate managers and business owners, resulting in a redistribution of income. However, as Bivens and Zipperer’s analysis shows, a close review of economic research does not support the notion that technological advancements over this period have been the causal driver of worsened outcomes for workers. While technological change may have coincided with rising income inequality, it was itself not a headwind for workers or our economy. Conversely, under the right conditions—more equal distribution of market power between businesses and workers—advances in technology can and have resulted in improved outcomes for workers.

It’s not difficult to see how improvements in technology will help spur greater economic growth by making production processes more efficient. The question then becomes whether workers will be able to share in the gains that flow from new advancements in technology. The answer hinges on the relative labor market power of workers. Given today’s significant imbalances and the erosion of core labor rights, it is unlikely that workers will share fully in the economic gains brought about by AI technologies or any other driver of productivity growth. 

Industry-, firm-, and worker-level impacts of AI 

Beyond aggregate-level impacts, many studies and reporting chronicled the experience of workers using AI and how the use of these technologies have played out within specific industries or occupations. Below we discuss the types of AI technologies that are being used across three industries—warehousing, call centers, and health care; how the specific manner in which they have been implemented has exacerbated negative outcomes for workers; and the efforts workers and their representatives have taken to mitigate these effects.

AI in warehousing

The use of AI technologies in the warehousing industry is perhaps among the most publicly recognized. Over the last few years, there has been extensive documentation of the use of technologies in this industry to set productivity targets and task management, particularly among large retailers like Amazon and Walmart.3 For example, handheld devices track individuals’ pace of work including the number of packages they may be scanning in an hour, error rates, and time between scans (Bernhardt, Suleiman, and Kresge 2021). These data are then used by companies to establish productivity quotas or inform disciplinary decisions. Additionally, robot carts can be used to direct where workers should go in a distribution center, identify which products to move, and set the pace of work (Bernhardt, Suleiman, and Kresge 2021). 

While this is an industry with historically higher injury and illness rates than national averages, these incidents have ticked up in recent years.4 To be sure, not all injuries are a result of using AI technologies, but it is clear that the manner in which these technologies are currently being used in many establishments is associated with severe injuries.

The National Employment Law Project (NELP) found that 1 in 15 Amazon employees experiences a recordable injury each year in an analysis of Occupational Safety and Health Administration (OSHA) injury reporting records for establishments with more than 1,000 workers (Tung, Marquez, and Sonn 2024). Amazon’s injury rate has consistently been higher than the national average and even exceeds other industries with notable workplace hazards such as coal mining, forestry, and logging (Athena Coalition et al. 2020). As the researchers note in an earlier study of OSHA’s records, the most common injuries are to “workers’ backs, shoulders, knees, wrists, ankles and elbows. These types of injuries are often caused by workers assigned tasks involving ergonomic hazards including forceful exertions, repetitive motions, twisting, bending, and awkward postures” (Athena Coalition et al. 2020).

Almost all cases (95%) result in employees missing work or being assigned to different job duties; these injuries can have lifelong impacts and associated problems (Tung, Marquez, and Sonn 2024). The report goes on to explain that “the high rates of serious injury at Amazon are directly attributable to the way that the company manages its workforce using intensive surveillance, automated discipline, and constantly changing quotas generated by algorithms” (Tung, Marquez, and Sonn 2024).

Injuries in the warehousing sector are not unique to Amazon, though research by NELP indicates rates of injuries are far higher at their facilities. Recognizing the prevalence and severity of injuries in the warehousing sector caused by a range of practices, the Occupational Safety and Health Administration launched a national emphasis program (NEP) in 2023 to address comprehensive hazards in this sector (OSHA 2023a). Notably, the NEP’s inspection procedures included an ergonomics screening. Specifically, during interviews with workers, while reviewing injury logs, and site walkthroughs, OSHA inspectors will assess whether workers are exposed to ergonomic hazards and if so, then expanding the scope of investigation (OSHA 2023b). Importantly, in the absence of a specific ergonomics standard, OSHA can only issue citations related to ergonomic hazards against employers under the general duties clause of the OSH Act (OSHA 2024).

In response to poor working conditions, workers and advocates have sought solutions through labor organizing efforts and state-level legislation. While strides are being made in certain states like California, Minnesota, New York, and Washington, new organizing efforts at individual establishments have been arduous. For example, reporting has shown that Amazon may be using electronic monitoring and productivity quotas as a means to retaliate against employees engaged in union organizing. Specifically, in one case at a Kentucky fulfillment center, an employee filed a complaint with the NLRB claiming that Amazon used his failure to meet specific performance targets as pretext to retaliate against him for leading a union organizing campaign (Rosenberg 2022). While Amazon employees at a Staten Island fulfillment center successfully voted to join a union in 2022, they have not yet secured a first contract (Chapman and Hadero 2024).

In an analysis and discussion of injury rates at Amazon, Julia Lang Gordon (2021) explains how workers’ diminished market power—particularly, limited alternative employment opportunities and the prospect of being fired without cause—has enabled “Amazon to push its employees to the physical brink while facing little to no repercussions.” At establishments where workers are able to secure representation by a union and a collective bargaining agreement, there is a clear pathway to improving working conditions, including those that are undermined by AI technologies. It would be exceedingly unlikely, for example, for a unionized employee to be fired for failing to meet unrealistic productivity quotas that if met, would require engaging in unsafe practices; workers would similarly have a clear process to protect basic rights like accessing the bathroom and taking rest breaks.

The warehousing industry is a clear example of how workers’ lack of bargaining power has directly limited their ability to engage businesses in how technologies are used in the workplace and prevent unsafe practices that have resulted from their use. 

AI in call centers 

The impact of electronic monitoring and management on working conditions is not unique to the warehousing sector. In call centers, AI technologies have been deployed to replace, assist, and manage workers’ activities. For example, automated systems are being used to screen calls and route them to employees; chatbots are being used to provide workers with prompts and answers to callers’ questions; other systems are generating notes and transcripts of employees’ calls with customers, identifying patterns in behavior or deviations from call scripts; and other technologies are providing real-time management to employees, including prompts related to their tone, pace of speech, and cues to signal specific emotions (Bernhardt, Suleiman, and Kresge 2021; Doellgast et al. 2023).

In a recent survey of call center workers, researchers explored how the use of these technologies impacted workers’ outcomes. While the experience with these technologies varied, most workers held negative views of AI management tools. About two-third of workers reported that automated monitoring systems made their jobs more stressful and did not feel that monitoring or coaching systems increased fairness on the job (Doellgast et al. 2023).

Among the most concerning impacts is the potential for real-time recording systems to generate performance evaluations, and then inform disciplinary decisions and firing without employees having an opportunity to understand what is driving these decisions, the accuracy of the data underlying them, or avenues for appeal. In a written statement for a U.S. Senate forum on AI, Ameenah Salaam (2023) of the Communications Workers of America describes how CWA workers are experiencing this very dynamic: 

These systems are often ineffective and have negative impacts on the workplace and the quality of service our members can provide. We’ve heard from workers of color about discriminatory bias from systems that purport to judge expressions of emotion, like empathy; workers also say the systems do not recognize certain pronunciations and styles of speech. Agents report that the scripts enforced by these systems slow down the work of helping customers and often advise wrong solutions that may violate company policies, creating a situation where agents can face discipline for following the system’s prompts.  

As with any workplace, the presence of a union enables workers to engage their employers and address how specific business practices affect job quality and their well-being; this is particularly true for call centers where employees have long grappled with electronic surveillance and monitoring. Research on the impact of unions in call centers has found that unions not only increase worker well-being but are able to successfully negotiate over how information gathered from performance monitoring is used (O’Brady and Doellgast 2021). Sean O’Brady and Virginia Doellgast (2021) write, “unions place a strong emphasis on challenging ‘discipline-based’ performance management practices and encouraging more developmental ones focused on training and development, as well as in establishing more fair and transparent processes for evaluating and rewarding performance.” In fact, in their study on the impact of unions on key worker outcomes, the authors found that union activities were positively associated with developmental performance monitoring and a greater perception among workers of the fairness of performance metrics. Overall, the authors found that union engagement improved worker well-being (measured by emotional exhaustion) (O’Brady and Doellgast 2021). Similarly, as Aurelia Glass (2024) has written, the CWA has over the last three decades secured contracts with major companies, including AT&T and Verizon, that have placed limits on the frequency of monitoring employees calls and the ways in which recordings can be used to inform disciplinary action.

Thus, it is clear that to the extent workers and policymakers are concerned about how AI management systems or performance metrics will be used and the fairness of the underlying AI technologies, unions have a clear track record of being able to address these very issues.

AI in health care

In the health care sector, workers have raised significant concerns over how AI-related technologies have impacted staffing ratios, limited practitioners’ discretion in patient care, and resulted in incomplete or incorrect care plans. In a recent survey of registered nurses, more than 50% said their employers utilized an algorithmic system to analyze patients’ health records to assess patient care needs (NNU 2024). The survey, conducted by the National Nurses Union (NNU), also found that a growing number of shift handoff reports from one nurse to the next was partially or wholly generated by AI technologies. And between 30% and 40% of nurses whose employers use AI systems are not able to override or correct care assessments or outcome predictions associated with discharge recommendations (NNU 2024).

The NNU’s research alarmingly finds that there are notable deficiencies in the accuracy of AI systems in use. For example, nearly 70% of nurses who use algorithmic systems to assess patient acuity reported that their own assessment did not match that of the computer. Similarly, nearly 50% of nurses who receive AI-generated handoff reports said their own assessments differ from the information provided to them (NNU 2024). 

Like warehousing, call centers, and other industries where AI technology is being deployed, the use of AI in the health care setting is exacerbating long-standing issues that health care professionals have bargained over, including staffing ratios, and discretion in patient care. While NNU and others are ringing alarm bells for policymakers and the public on the impact of AI on nurses and patient care, the impact of AI on health care professions goes well beyond nurses. Physicians and others who are less commonly represented by labor unions may face greater challenges in raising awareness of these issues and mitigating harmful outcomes.

The three industries discussed above, in addition to research on the use of AI in other industries and occupations, highlight three general themes that should inform policy evaluation and development. First, AI technologies are often the latest iteration of prior technologies and management strategies. While the use of certain AI technologies is becoming standard practice in certain industries and occupations, there remains significant variation in use across firms. Second, there is a lack of transparency around when technologies are being used, how they function, and how they inform employment-related decisions. Third, and perhaps most importantly, under certain circumstances AI-based technologies are undermining existing labor and employment rights. It is important to note, that these three broad findings and trends do not focus on the inherent impact of AI technologies but rather the impact of businesses using these technologies in the context of today’s labor market—most crucially the current balance of power between workers and businesses. Across a range of sectors, unions are negotiating over employers’ use of AI in the workplace and have secured crucial wins for workers (Glass 2024). Given weakened labor laws, though, it is exceedingly difficult for most workers today to successfully secure representation and bargain over AI and other issues.

Given the aggregate-level labor market impact of AI as well as the variety of ways AI technologies are being used across industries and firms, the objective of federal legislation should be to increase the ability of workers to meaningfully engage their employers in how AI technologies are deployed in the workplace.

Landscape of federal legislation 

There is a bevy of federal legislation, principles, and road maps intended to curb negative impacts of AI on workers. In the appendix below, we provide a complete review of these bills, highlighting the most common policy interventions. After reviewing these proposals, it’s clear that few bills include comprehensive solutions for workers in a period of AI advancement. For example, many bills include new employer disclosure requirements, ensuring workers are aware of when certain practices like workplace surveillance and data collection are occurring. However, given workers’ lack of bargaining power and low unionization rates, it may be challenging for the typical worker to adequately leverage this new information on their own to change their employers’ practices.

Notably, one proposal—the Warehouse Worker Protection Act—takes a novel approach by aiming to address head-on unique barriers to organizing in the warehousing industry. The bill does this in two ways. First, it limits the ability of employers to fire workers for failing to meet productivity quotas that haven’t been previously disclosed to the employee and requires greater transparency of data disclosure to allow employees and their representatives to assess whether employment actions based on performance monitoring are consistent across employees. Second, the bill would amend the National Labor Relations Act and the Occupational Safety and Health Act to strengthen the ability of these agencies to enforce workers’ rights. But even here, the amendments are narrowly tailored.

Most of the proposed bills to date aim to address concerning issues workers are facing—discrimination, invasion of privacy, lack of recourse for hiring, disciplinary or other employment actions, and safety risks. But unless AI-specific proposals are paired with solutions to address workers’ bargaining power, these AI policies will fall short of their very objectives. Congress may soon be caught in a game of whack-a-mole, chasing the latest use case of AI systems as employers adjust practices in response to new legislation and technologies, all while working conditions continue to deteriorate.

Policy discussion and recommendations

In the prior sections, we discussed the likely impact of AI on the labor market as well as the current experience of workers who use or are subject to AI-powered technologies. Taken together, these economic realities have led policymakers to question what policy interventions are needed during a period of AI advancement. However, we believe the success of any AI policy will hinge on its ability to address the underlying conditions that have, for decades, been enabling businesses to use the latest tool available to them—in this case, AI technologies—to undermine worker power and erode workplace conditions and outcomes. Fundamentally, the impact of AI on workers is not solely a function of the design of the tool itself but instead the institutions and policies that shape how it is implemented in firms across America.

Workers should have a voice in how policies are implemented in their workplaces. But decades of weakened bargaining power and increased reliance on employers for basic necessities such as health care and retirement have made it exceedingly difficult—if not impossible—for most workers to bargain over how AI technologies are being used in the workplace, let alone walk away from poor working conditions. These broader dynamics have led to the very outcomes policymakers are rightly concerned about when it comes to AI: diminishing workplace safety, discriminatory hiring, limited decision-making power, lack of data privacy, and disciplinary actions without explanation or appeal.

Therefore, any set of AI policies must first address the systematic erosion of core labor rights and meaningful exit options for workers in exploitative job situations. Conversely, to the extent policymakers aim to construct AI-specific policies, they need to do so strategically. This means assessing whether proposed solutions to common outcomes for workers who use or are subject to AI will 1) meaningfully remove unique impediments to the exercise of worker voice on the job or 2) create new protections that will be realized by workers beyond today’s latest version or use of AI systems. Policymakers may be tempted to craft solutions that are narrowly focused on addressing the consequences of the use of AI technologies today. But this approach runs the very real risk of crafting solutions that quickly become ineffective as employers’ practices change or technologies evolve. Instead, a wider lens approach to creating standards and protections that happen to also stand up against employers’ current use of AI technologies will be more likely to meaningfully improve worker outcomes for years to come.

Indeed, Congress has long had to balance this dynamic—deciding whether to regulate an outcome or the mode by which an outcome is realized—when designing some of the nation’s most fundamental employment protections. For example, when Congress passed the Fair Labor Standards Act, legislators debated whether to explicitly include a list of the common employment structures that they believed businesses would use to evade coverage under the FLSA such as piece work, off-premises work, and commissions, among others. As Kati Griffith (2019) writes in a historical analysis of the FLSA:

Congress eventually rejected this much-discussed list of specified devices of evasion in favor of a much broader and more flexible concept of employment. It provided definitions that could adapt with the times and adapt to new “devices.”…By moving away from a specific list of tools of evasion, Congress gestured that it did not want to slide into an endless game of whack-a-mole, to preempt different business structures and strategies that might emerge to sidestep the FLSA’s coverage. 

While not directly analogous to the choices Congress faces today, the legislative history of the FLSA is nonetheless informative as Congress considers how and to what extent they should regulate AI in the workplace. If AI-related outcomes highlight weakness in existing rights (like health and safety) then the foundational statutes, regulations, and enforcement efforts should be strengthened to ensure the outcomes arrived at by the use of AI are indeed protected as intended. Similarly, to the extent AI-related outcomes have shined a light on new or unlegislated risks to workers, then Congress should address the issue broadly; for example, issues related to data privacy should be addressed in a manner that ensures clear lines of privacy in the workplace are drawn for employees and personal information—no matter how it is obtained—is not used for illegitimate purposes.

Below we discuss three pillars of a worker-centered AI policy strategy that draws on the themes discussed above with the aim of achieving a straightforward objective: increasing the ability of workers to meaningfully engage their employers in how AI technologies are used in the workplace. 

Pillar one: Expand and expedite pathways to collective bargaining

Much of workers’ experiences with using or being subject to some form of AI technology today is mostly a function of low unionization and weak bargaining power, not of the current state of technology. Relatedly, efforts to increase transparency around the use of AI technologies (discussed below) will only be effective at catalyzing change if workers and their representatives are able to act upon that information. Therefore, a key pillar of any worker-centered AI policy strategy is expanding and expediting pathways to collective bargaining. The most powerful way to do this would be for Congress to pass the PRO Act and the Public Service Freedom to Negotiate Act (PSFNA). However, this much needed fix to our labor laws has languished in Congress for far too long. 

While considering policies to support workers and create good jobs in an age of AI, policymakers should also look closely at other ways to restore collective bargaining rights and increase bargaining power of workers, even if it is short of the more comprehensive solutions of the PRO Act and PSFNA, or limited to AI-related issues.5 In many other countries, sectoral bargaining is used to reach industrywide agreements on core conditions of work. In the United States, though, sectoral bargaining has not taken hold, but the Clean Slate for Worker Power project and others have put forward a rich set of policy recommendations on how to advance sectoral bargaining in the U.S. (Clean Slate 2021; CLJE Lab 2024a). For example, tripartite boards composed of representatives from employers, workers, and the public could be convened to negotiate new standards for specific industries or issues (Madland 2019). Congress should consider whether emerging dynamics related to AI may be ripe to address through sectoral bargaining, which is particularly useful in sectors in which there is already strong union representation or, conversely, high levels of monopsony power. 

Pillar two: Reduce AI-specific barriers to worker voice and strengthen employment protections

Workers in the United States—particularly nonunion workers—are far too constricted in how they can express voice over how their workplace is organized and run. One key impediment is the opaque management practices and production processes in workplaces. AI—a highly specialized technology that very few understand deeply—threatens to make expressing voice over workplace practices even harder.

Policymakers have, therefore, focused much of their attention on using legislative interventions to increase firm-level awareness and knowledge of technologies that are in use. However, it’s important to realize that legislative solutions that fill the knowledge gap that results from weak bargaining power is only part of the equation. In other countries with strong labor unions, employees have been able to use their market power to not only extract a clear understanding of what technologies are being used, but to use that information to further engage businesses to bargain over how technologies should be used. It is this latter part of the equation where current federal proposals seem to fall short. If, in the absence of adequate worker bargaining power that would allow workers to effectively demand transparency from employers on their own, Congress is attempting to step in and require disclosure of technological practices, then these proposals should be created in a way that maximizes the ability of labor unions, worker advocates, and nonunion employees to realistically use that information.

Therefore, policies that include new employer disclosure requirements to workers should also create a public repository of businesses’ disclosure reports. Greater public access to this information will reduce barriers for labor unions, worker advocates, and researchers to access the information, enabling these entities to identify and take action on industry trends and worker outcomes. Similarly, proposals that create new data privacy protection for workers, which often include a right for workers to receive and review their data, should also consider how workers could realistically understand and act upon this information. Other experts, for example, have suggested the use of AI monitors in the workplace, which would help create a venue for workers—union and nonunion alike—to better understand disclosures provided by employers and engage with them directly on complex issues (CLJE Lab 2024b).

Beyond increasing transparency related to the use of AI, there are a number of existing labor and employment laws that limit its use. While these existing laws give workers some voice and power in their workplaces, it is crucial to ensure that the laws are not subverted by the deployment of AI in workplaces.

The EEOC, NLRB, and DOL have all issued various forms of guidance clarifying the applicability of relevant laws to the use of AI technologies. The EEOC has multiple resources for employers related to the use of AI in hiring-related decisions and is actively engaged in enforcement actions against businesses that have violated the law (EEOC 2023). Similarly, the NLRB general counsel has issued a memorandum identifying how the use of surveillance and monitoring technologies may violate core employee rights under the NLRA (NLRB 2022). Finally, the Department of Labor’s Wage and Hour Division has issued a Field Assistance Bulletin to their investigators, clarifying how AI technologies may result in violations of minimum and overtime standards, as well as employees’ rights under the FMLA, and other laws (DOL 2024).

Looking ahead, policymakers should first and foremost ensure enforcement agencies are adequately funded. While the size of the workforce and complexity of businesses continue to grow, for too long agencies’ enforcement capacities have not kept up. Enforcement agencies will be limited in their ability to ensure employers’ use of AI technologies does not run afoul of existing laws if Congress does not provide them with adequate funding. Relatedly, Congress should consider what new requirements might aid in the compliance and enforcement actions under existing laws. For example, policymakers should leverage the authority and expertise of existing federal entities, such as EEOC, NIST, or the FTC, to standardize auditing frameworks and tools that can be easily used for pre-deployment and ongoing testing. The first-order objective of these audits should be to identify and eliminate outcomes that violate existing labor and employment laws. Additionally, as Congress considers new proposals, whether they are disclosure requirements or new employment standards, policymakers should take steps to ensure there are avenues for workers and their representatives to more fully participate in the enforcement process, including a private right of action.

Additionally, policymakers and federal agencies should consider how to more fully leverage existing authorities to address workplace outcomes that have been exacerbated by the use of AI technologies by issuing new guidance and regulatory standards. For example, OSHA should consider which industry-specific standards would better protect employees from workplace injuries that can arise when certain AI technologies are deployed in a specific manner; NIOSH should study the mental health impacts of specific working conditions and practices, including those with AI technologies, creating the evidentiary basis of potential OSHA standards. Finally, where AI has shined a spotlight on unregulated harms to workers that warrant broader interventions like issues related to data privacy or invasive workplace surveillance, Congress should advance new employment standards.

Pillar three: Increase workers’ ability to leave employers who create exploitative conditions with AI (or other technologies and practices) and ease job transitions

In earlier sections, we discuss how aggregate job loss is unlikely to occur as a result of advancements in AI technologies. For each job displaced by AI, there is highly likely to be one created by the ripple effects it creates (as has happened with other technological changes).  However, job churn creates stress and income losses for workers in the sector seeing job displacement from AI (Bivens and Zipperer 2024). If Congress is serious about easing the impact of AI-related job transitions for workers, then the most effective solution is strengthening social insurance programs to reduce the economic consequences of losing any specific job and provide adequate support to workers while they search for employment. 

In particular, Congress should strengthen unemployment insurance protections and take steps to reduce the cost of health care and increase access to retirement security. These, along with other improvements to social insurance systems and workforce development programs, will not only ease the financial burden and pain of specific job transitions, but increase the bargaining power of all workers—especially those who aren’t represented by a union. The single greatest bargaining chip nonunion workers have is the ability to leave their jobs. Therefore, efforts that improve our social insurance systems and reduce workers’ reliance on employers for health care and retirement, among other benefits, will increase their ability to walk away from subpar working conditions, creating pressure on businesses to change their practices. 

Similarly, it is far better to lose one’s job when overall unemployment is very low than when it is high. Further, threats to leave an exploitative workplace and find a better job elsewhere are far more credible when the aggregate labor market is experiencing very low unemployment than when unemployment is high. Macroeconomic policy that targets sustained periods of very low unemployment and that quickly restores the labor market to health after recessionary shocks is vital for workers to carve out a decent career during periods of technological changes. Of all the policy failures that created the rise in inequality and the anemic wage growth in recent decades, macroeconomic policy failures likely top the list. Sound macroeconomic policy will dwarf any level of AI penetration in its importance to the trajectory of workers’ wages and employment over the next few decades.

Conclusion

Over the last few years, researchers, unions, and workers have drawn much needed attention to how the deployment of AI technologies has exacerbated harmful workplace conditions and outcomes for many workers. It’s understandable why policymakers are focused on developing legislation that addresses the tool—AI technology—used by businesses to achieve these outcomes. However, we believe that legislation aimed at improving outcomes for workers must also address the underlying conditions that enable businesses to deploy AI in this manner. Legislative interventions must carefully consider and address why workers don’t currently have the bargaining power to directly engage employers when they use AI—or any technology—in a manner that skirts their rights or creates poor working conditions. That is why we believe any serious worker-centered AI policy must restore core labor rights, strengthen social insurance programs, and maintain full employment. If these underlying economic conditions are not addressed, then we fear that legislative interventions that regulate how AI is developed or deployed will fall far short of their objectives.

In the coming months, we will release a series of follow-up pieces that will examine each pillar, laying out in more detail how Congress and states could design specific proposals to achieve the policy objectives of each pillar. We believe the three broad policy recommendations discussed above would most directly and effectively increase workers’ ability to meaningfully engage employers over the deployment of AI technologies in the workplace. The advancement of technology creates an opportunity for greater prosperity and economic growth. Under the right conditions, workers can and should fully share in those gains.

But we will sound a note of caution: It is possible for policymakers looking to help workers thrive in labor markets to focus too much attention on AI-specific issues. Simply put, there is no compelling evidence that AI is enormously different enough or more powerful than other earlier waves of technological change in its effect on labor markets. Further, the pre-AI status quo in labor markets was terrible for workers’ opportunities to thrive and for needed fundamental policy change across all sorts of policy areas that remain unaddressed. Given this fact, U.S. workers would not be well served by having policymakers, researchers, and advocates focus disproportionate amounts of attention on AI deployment at the expense of other crucially needed reforms. To put it more bluntly, it would serve the interests of exploitative employers to have the pro-worker policy community lose focus on many other issues to concentrate large amounts of attention and influence on AI specifically.

Appendix

The summary below aims to distill federal bills related to AI by highlighting the most common policy interventions and strategies. By focusing on specific types of interventions, we hope to highlight both the nuance of the policy strategies as well as how even the same form of intervention can be structured and implemented in a variety of ways.

Disclosure requirements

Across branches and levels of government, there are efforts to increase basic awareness among workers (and the public) of the presence of AI technologies used in the workplace.

The Stop Spying Bosses Act broadly requires employers to disclose—to employees and the public—their practices related to workplace surveillance and how these affect or influence employment-related decisions. More specifically, the legislation requires employers to disclose the following: what data are collected, how the data are being collected, where and when data are being collected, how frequently the collection occurs, what the business purpose of the collection is, and which, if any, third party service providers are engaged for the surveillance, data transfer, or sale of worker-level data, among other disclosures.6

Similarly, the No Robot Bosses Act generally addresses employment-related decisions that are informed by outputs from automated decision systems. The bill would require employers to provide employees with a plain-language explanation of outputs from automated decision systems including: a description of the system used, description and copy of the data inputs to the system, an explanation of how the outputs were used in making the employment-related decision, and the reason for using the automated decision system outputs in making the employment-related decision.7

The Algorithmic Accountability Act directs the Federal Trade Commission (FTC) to implement, through regulations, a new requirement for companies to assess and disclose the impact of automated decision systems, including those related to “employment, workers management, or self-employment.” Specifically, covered entities would be required to submit an initial summary report of their assessment to the FTC prior to the deployment of newly covered technologies; entities would also be required to submit annual reports on the ongoing assessment of technologies that are already on the market. The bill identities specific topics that should be disclosed in the summary reports to the FTC including the following: the purpose of the product and a detailed description of the decision(s) the system intends to make; any publicly stated guardrails or limitations on use of the product; documentation of the data used as inputs during the development, testing, and maintenance of the systems; and any transparency mechanisms that are included in the system to allow end users to contest, correct, or appeal decisions made by the system.8 

The Warehouse Worker Protection Act broadly aims to address productivity quotas that are opaque to employees, increase risk of injury, or are used as pretext for exercising labor rights; the bill includes a number of disclosure requirements. Specifically, the bill would require covered employers to provide a written description of each quota an employee is subject to. The description must be provided in plain language and include the following: the number of tasks that must be completed within a specified time period; what, if any, disciplinary actions could result from failure to meet quotas; how the employer measures work speed, including where and when measurements occur; and the businesses’ purpose for collecting work speed data. Employers that take adverse employment actions against workers as result of failing to meet established quotas would be required to provide employees with a written explanation of how the quota wasn’t met and a copy of work speed data.9 

Auditing and impact assessment requirements

As discussed above, one of the key labor market impacts of AI is the potential for technologies to undermine existing labor and employment rights, including anti-discrimination protections. A number of policy proposals and state-level laws attempts to address these issues by creating new pre-deployment and ongoing auditing requirements of technologies, particularly those used in hiring or other employment-related decisions. 

Under the No Robot Bosses Act, employers would not be able to use any automated decision system to inform employment-related decisions unless it underwent pre-deployment testing and validation. Specifically the bill requires validation with respect to compliance with employment laws including: Title VII, ADEA, ADA, FLSA, Rehabilitation Act, and Pregnant Workers Fairness Act, among other specifically identified protected classes. The proposed legislation would also require, at minimum, annual independent auditing to ensure continued compliance with these laws.10

Under the Algorithmic Accountability Act of 2023, companies that develop AI technologies would be required to conduct pre-deployment and ongoing impact assessment for automated decision systems. The bill identifies an extensive list of issues that developers should include in their assessments including: comparison of performance outcomes under test conditions and deployment conditions; evaluation of differential outcomes associated with race, color, sex, gender, age, disability, religion, family status, socioeconomic status, or veteran status; assessment of the need for guardrails or limitations on the use of products; and assessment of the explainability and transparency of systems for consumers.11

Prohibition of specific technologies or use cases 

Beyond increasing transparency through disclosure requirements and imposing new auditing requirements, a number of legislative proposals prohibits specific uses of AI technologies. In some instances, these prohibitions simply reiterate that certain actions are not allowed under existing laws; in other instances, the prohibitions attempt to curtail the use of AI technologies in ways that may be harmful to workers given their limited bargaining power and ability to alter employer practices on their own. 

Under the No Robot Bosses Act, employers would not be able to rely exclusively on automated decision systems to make hiring, firing, disciplinary, or leave-related decisions, though they could still use the systems as an input or factor in a decision-making process. This type of prohibition attempts to remove perhaps the most extreme use cases of the technology as it relates to employment decisions. Importantly, though, it is currently quite common for business groups and companies to claim that technology alone doesn’t make these decisions.12

Under the Stop Spying Bosses Act, employers would be prohibited from using surveillance technologies when covered workers are off duty or in sensitive areas such as a locker room or restroom. Additionally, the bill would prohibit the use of these technologies to identify individuals who are engaging in labor organizing activities— a use of technology which the NLRB general counsel has noted is, in her office’s view, unlawful under the NLRA. Additionally, the bill would prohibit employers from using surveillance data to identify workers’ political opinions, religious views, and health conditions and outcomes that are unrelated to the performance of job duties.13

The Warehouse Worker Protection Act includes provisions that would prohibit employers from requiring a quota that would prevent the following: compliance with any required meal or rest breaks; compliance with health and safety standards (required by federal, state, or local laws); employees’ use of bathroom facilities; and compliance with reasonable accommodations and nondiscrimination provisions of federal, state, and local laws. Additionally, the bill would prohibit employers from setting quotas that establish a performance target over a period that is less than a day and prohibits quotas that would prevent or discourage employees from exercising their rights under the National Labor Relations Act.14

Amending existing labor and employment laws; agency directives 

As discussed above, businesses’ use of AI technologies has exacerbated violations of existing labor and employment standards. Some legislation recognizes this by explicitly prohibiting technology to be used in a manner that violates these rights, such as nondiscrimination protections or labor organizing rights (see above). In addition to this intervention, some proposed legislation goes further to amend underlying statutes to bolster and expand protections, and direct agencies to issue regulations and reports. 

The Warehouse Worker Protection Act would notably amend the National Labor Relations Act to make it an unfair labor practice for employers to “impose on an employee a quota that significantly discourages or prevents, or is intended to significantly discourage or prevent, an employee from exercising the rights guaranteed in section 7.” Additionally, the bill would amend the NLRA to create a rebuttable presumption of discrimination if an employer takes an action to impose a quota against an employee within 90 days of exercising their rights under section 7 of the NLRA. Additionally, the bill includes a series of directives to OSHA regarding rulemaking and amends the OSH Act. Specifically, the bill would require OSHA to issue the following: a proposed ergonomics standard (within 3 years) and a proposed standard requiring covered employers to provide employees access to trained first aid professionals at the facility (within 1 year). Finally, the bill would amend the OSH Act to include provisions related to the correction of serious, willful, or repeated violations pending contest and procedures for a stay. 15

The Eliminating Bias in Algorithmic Systems Act generally requires every federal agency to establish an office of civil rights that is focused on combating AI bias and discrimination; the bill is not limited to agencies that regulate employment practices but is inclusive of them. While the bill does not direct agencies to issue any specific piece of regulation or even amend their underlying authorities, the bill directs agencies to engage in specific activities in order to better identify and reduce prevalence of bias and discrimination. Specifically, the bill requires agencies to submit a report to Congress on the state of the technology with respect to the jurisdiction of the agency and any relevant steps the agency has taken to mitigate algorithmic bias and discrimination.16

Workforce and training

As discussed above, one concern among policymakers is the impact of AI on job displacement and skills. There are a few bills that address this issue, though primarily by directing agencies to engage in further research. 

The Technology Workforce Framework Act generally expands the functions of NIST to include a workforce framework for emerging technologies. Specifically the bill would direct NIST to define AI-related jobs and the necessary knowledge, skills, and abilities needed to fill those jobs.17

The Jobs of the Future Act of 2023 directs the Department of Labor and the National Science Foundation to submit a report to Congress on AI and its impact on the workforce including: industries that are projected to have the most growth in the use of AI technologies and whether that use would result in job enhancement or job replacement; analysis of the skill necessary to develop or use AI technologies; methods to ensure necessary skills, expertise, and education are accessible to all segments of the current and future workforce; recommendations to minimize job displacement; and workforce training needs.18

Notes

1. See for examples: Ellingrud et al. 2023; Goldberg 2023; Kochhar 2023; Tamayo et al. 2023.

2. See for examples: Bernhardt, Suleiman, and Kresge 2021; Yang 2020.

3. See for examples: Khan 2024; Long 2022; Mims 2021.

4. See BLS 2023. In 2019 total nonfatal injury and illness rates per 100 full-time workers was 4.8; in 2022 (latest data available), the rate was 5.5.

5. Richard L. Trumka Protecting the Right to Organize Act of 2023, [H.R.20] 118th Cong. (2023) and Public Service Freedom to Negotiate Act of 2024, [S. 4363] 118th Cong. (2024).

6. Stop Spying Bosses Act, [S.262] 118th Cong. (2023).

7. No Robot Bosses Act, [S.2419] 118th Cong. (2023).

8. Algorithmic Accountability Act of 2023, [H.R.5628] 118th Cong. (2023).

9. Warehouse Worker Protection Act, [S.4260] 118th Cong. (2024).

10. No Robot Bosses Act, [S.2419] 118th Cong. (2023).

11. Algorithmic Accountability Act of 2023, [H.R.5628] 118th Cong. (2023).

12. No Robot Bosses Act, [S.2419] 118th Cong. (2023).

13. Stop Spying Bosses Act, [S.262] 118th Cong. (2023).

14. Warehouse Worker Protection Act, [S.4260] 118th Cong. (2024).

15. Warehouse Worker Protection Act, [S.4260] 118th Cong. (2024).

16. Eliminating Bias in Algorithmic Systems Act of 2023, [S.3478] 118th Cong. (2023).

17. Technology Workforce Framework Act of 2024 [S.3792] 118th Cong. (2024).

18. Jobs of the Future Act of 2023, [H.R.4498] 118th Cong. (2024).

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