This is part two of a series on the economics of debt, deficits, and today’s high interest rates—and how policymakers should respond. Read part one here. 

The effects of deficits and debt on the broader economy are largely straightforward—with one big exception: They differ radically depending on whether the economy is operating near its productive potential or is instead operating well below it (which occurs most notably during recessions).

The main economic effects of deficits in these two states of the world are essentially as follows:

• Large deficits during times when the economy is operating near its productive potential (or during times of full employment) put upward pressure on either interest rates (more likely) or inflation (less likely).
  • The higher interest rates in turn can “crowd out” productive private-sector capital investments. If this happens chronically, this can shrink the size of the capital stock U.S. workers have available to them—with the capital stock being factories and other buildings, equipment, software, and research and development efforts. This smaller capital stock in turn reduces productivity, or the income generated in an average hour of work in the U.S. economy, hence shrinking the growth of potential living standards.
• Large deficits when the economy is operating well below potential actually stabilize the economy and aid its return to potential.
  • Economies start operating below full employment when aggregate demand—spending by households, businesses, and governments—falls short of what is needed to fully employ productive resources. By increasing spending and lowering taxes (hence boosting private-sector disposable income that can be spent), higher deficits boost aggregate demand and push the economy closer to potential.
  • Because private-sector investment responds to income growth as well as interest rates, deficits run during times of less than full employment can actually “crowd in” productive private-sector investment.

The one noncontroversial effect of budget deficits: They raise aggregate demand

If there is one noncontroversial statement about budget deficits, it is that they raise the economy’s aggregate demand. Whether this increase in aggregate demand is good or bad depends on the broader economy.

Deficits when private spending is strong

When private-sector sources of demand are strong—consumers are buying a lot, businesses are demanding lots of new capital goods to undertake production, and imports and exports are roughly balanced—all available productive resources (like workers) are fully employed and the economy does not need the public sector to inject extra demand into circulation with larger deficits; additional increments to demand will not bring forth more output. Instead, additional demand sent into a full employment economy will bid up prices rather than cause more output to be produced.

If the Federal Reserve is determined to not let strong demand bid up prices and push inflation above its preferred 2% target, it will respond to this excess of demand by raising its short-term policy rates to try to tamp down private sources of demand. Higher interest rates are generally effective in reducing demand through lower consumption, business investment, and larger trade deficits.

Besides the Fed’s response, higher deficits in a full employment economy can also put upward pressure on interest rates through financial crowding out. At full employment, the supply of savings available to be loaned to households or businesses looking to make long-lived investments is largely fixed. Federal deficits by definition mean that the government has to go into financial markets, where supplies of savings and demands for investment meet, and increase its own demand for a share of these savings. Deficits are financed by selling government bonds to households and businesses—that is, by convincing savers to lend their money to the government for a period of time. The extra demand for borrowing meets the fixed supply of savings and pushes up the price of loans, or interest rates.

Because most capital investment by businesses (building new factories and buildings, purchasing new equipment and software, and paying salaries of workers engaged in research and development) is financed by borrowing, higher interest rates stemming from large budget deficits in economies that are already strong will slow capital investment over time. As the price of something rises—in this case, the price of borrowing to finance new investment—less of it will be demanded.

This reduction in capital investment slows long-run economic growth. A key contributor to growth in productivity—the amount of income generated in an average hour of work in the economy—is how much capital workers have available to them to do their jobs. Construction workers today are far more productive than 50 years ago because they’re more likely to use mechanized earth movers rather than shovels for excavation.

If deficits crowd out capital investment, this can slow productivity growth. Productivity growth is the ceiling on how fast average living standards can rise in an economy. So when large deficits during full employment crowd out of investment, this can damage economic growth. A key point in this analysis is that it is higher interest rates that are the transmission mechanism for this damage: If interest rates do not rise, there is no damage being done.

Deficits when private spending is weak

When private spending is too low to fully employ all available productive resources, the boost to aggregate demand from large deficits actually helps to employ these resources and move the economy closer to its productive potential.

Some increases in deficits that push the economy closer to its productive potential during downturns happen automatically. Spending rises on safety net programs like unemployment insurance, and this supports private consumption. Tax collections fall as private incomes fall, buffering disposable incomes from the full fall in pre-tax incomes.

Some increases in deficits that push the economy back to its potential come from discretionary legislation passed by Congress in direct response to the downturn—relief packages like the American Recovery and Reinvestment Act (ARRA) of 2009 and the CARES Act of 2020.

Weak private spending places sharp downward pressure on inflation and interest rates as it generates idle resources (like rising unemployment). Given this, a boost to demand from larger deficits tends to not manifest as faster inflation or higher interest rates. A boost to demand when there are idle resources mostly causes output, not prices, to rise. And the higher output and income generated by higher deficits create savings that keep interest rates from rising in response to greater demand for investment.

Because this scenario sees no rise in interest rates, and because investment responds positively to output growth, larger deficits that boost economic activity almost certainly “crowd in” private investment, leading to more investment and higher productivity than would have occurred had deficits not been allowed to rise.

Excessive structural deficits are hurting the U.S. economy today

The upshot is that deficits can hurt if they’re too large when the economy is already near full employment, and that the data signature for this damage is rising interest rates. For decades, large budget deficits have not been accompanied by higher interest rates, making it very hard to argue that the economy’s long-run growth prospects were being damaged by crowded out private investment.

Over the past year, however, interest rates have risen noticeably. In fact, as of September 2026, 10-year Treasury rates have reached their highest levels since the early 2000s (though still far below highs reached between the mid-1970s and mid-1990s when—among other things—inflation expectations were much higher). Because inflation is a bit higher now than in the early 2000s, real (inflation-adjusted) rates have not risen quite as fast, but they are always noticeably higher than the average of the past 20 years. Between this and the fact that unemployment is likely not too far above full employment at the moment and the fact that inflation remains above the Fed’s 2% target level, it seems clear that too large U.S. deficits are putting unwelcome pressure on the larger economy.

Too large U.S. deficits are not the only driver of recent interest rate increases. Another contributor is larger deficits being run by many European countries. Many of these countries have loosened fiscal rules to spend more on the military in the face of the Russian invasion of Ukraine. The Japanese economy is also stronger than it has been in decades, and interest rates there are rising for the first time in decades. Finally, there has been a huge surge in private investment demand driven by the race between AI companies to dominate the market.

On the one hand, it seems hard to worry that higher interest rates are crowding out private-sector investment if it is precisely strong private investment contributing to higher rates. But the investment in AI is clearly not interest-sensitive (at least so far). It is driven by exogenous events and factors: Now just happens to be the time that the technology has reached tipping points where scalability and profitability seem possible, hence sparking a race between private companies to be the first to capitalize on this. Additionally, investment in sectors in the U.S. not being pulled along by the AI boom is notably weak in 2026, posting negative growth. If U.S. fiscal deficits were lower and this led to lower interest rates, this would help investment in non-AI sectors.

Noting that too large structural deficits are likely slowing U.S. growth today does not map directly onto policy solutions. It’s not the case that the damage from too large deficits right now is so overwhelming that anything that reduces deficits should be done in response. In a later post, I will walk through the considerations about how policymakers should optimally respond to too high structural deficits. They definitely should do something—but it’s not the case that they should just do anything.

This is the first brief in a series exploring comprehensive federal policy recommendations around AI and workers.

Introduction

Amid the seemingly endless predictions about AI, one point is essential to remember: How this plays out is not predetermined. Policy choices determine whether workers are helped or harmed by technological change. Even without AI, many workers already face harmful disruptions. Every year, 20 million workers are laid off in the U.S. economy1—due to employer mismanagement, once-profitable products becoming less so, offshoring, automation, and other factors. For individual workers, losing a job for any reason and for any period of time is traumatic and damaging in large part because we lack adequate social insurance programs to smooth those transitions.

As lawmakers debate how to best regulate AI deployment in our workplaces, today’s priority should be to strengthen the safety net for workers in any scenario and to acknowledge how AI is already affecting workers’ rights, protections, and livelihoods. Decades of underinvestment, coupled with attacks on unions and other labor supports, have left workers without an adequate safety net.

We can improve our current systems to better provide livable benefits for workers who lose their jobs because of large-scale economic shocks that are not their fault and outside their control. We can also learn from other periods of workforce disruption—the COVID-19 pandemic, trade-related shocks, and the 2008 recession—about which policy choices were effective and which fell short.

When workers lose their jobs due to factors outside their control, they are considered “displaced” or “dislocated.” If they meet certain requirements, these workers are eligible for unemployment insurance (UI). To strengthen outcomes for workers, this brief will provide recommendations to:

• increase UI benefit amount and duration
• expand and standardize eligibility to receive benefits
• align employer incentives with worker access to benefits
• center worker needs and power
• strengthen data infrastructure to support workers

These recommendations should be considered amid conversations focused on creating a stand-alone unemployment program geared toward AI-driven job loss. These discussions should weigh considerations and potential pitfalls, including:

• a blank slate for program design
• relevant roles for key stakeholders
• eligibility criteria challenges
• the allocation of resources, time, and awareness to be effective
• reauthorization vulnerability

If policymakers are serious about easing the impact of AI-related job transitions for workers, the priority solution should be strengthening already existing social insurance programs to reduce the economic consequences of losing any job and to provide adequate support to workers while they seek other employment.

Changes to the current system or establishing a new program will require robust resources, including time, money, and expertise. This investment can help all workers, regardless of their risk of AI-based displacement. With a strong safety net, workers who lose their jobs can get the assistance they need, including the opportunity to find work that is well suited to their skills and experiences, and businesses are more inclined to improve their practices because they know workers aren’t trapped in their current jobs by economic necessity.2 This moment presents an opportunity to strengthen the labor market for everyone and to ensure workers have the safety net they deserve.

How to strengthen our current system

Any effective approach to fixing the UI system must be comprehensive and should apply regardless of whether a worker is displaced due to AI. Numerous experts have weighed in on the best approach—including the National Academy of Social Insurance (NASI) Unemployment Insurance Taskforce,3 the National Employment Law Project (NELP),4,5 and The Century Foundation,6 among others. This brief draws from those recommendations to strengthen outcomes for workers. Other important issues, such as modernizing UI system technology and infrastructure, fraud detection and prevention, and the use of AI in benefit determinations and delivery, also shape worker outcomes but fall outside this brief’s scope.

Increase benefit amount and duration

First and foremost, UI should be designed to ensure that workers can afford housing, food, and other essentials during their period of joblessness. The percentage of wages that UI replaces for workers has declined over time and has not kept pace with inflation.7 In 2025, national average UI benefits replaced well under half of national average wages.8 This gap leaves workers across the income spectrum unable to maintain their standard of living and can push those whose earnings were close to the minimum wage into poverty.

Ensure benefit amounts are consistent across states

Benefit amounts vary across states. In Arizona and Florida, UI benefits replace less than 15% of average weekly wages.9 In some states, the weekly benefit amount is just $222 per week.10 While state variation in benefit amounts hurts all workers in stingy states, it weighs especially heavily on Black and Hispanic workers.

One recent study finds that differences in state rules cause the wage replacement rate for Black workers to be meaningfully lower than the rate for white workers, and that the discrepancies harm overall well-being.11 These disparities compound. Even among those likely eligible, Black workers are less likely than white workers to receive UI benefits—a gap explained primarily by structural factors, not a difference in eligibility, that has held steady for three decades.12

Wage replacement rates should be consistent across states. A recent federal bill has proposed changes to increase the replacement rate to 75% and provide additional weekly funding for dependents.13 Given growing wage inequality, the rate also should vary by income level. In one proposal, the lowest earners would receive benefits that represent 85% of their wages.14

Extend the duration of benefits across states

The duration of benefits also varies greatly from state to state. Until 15 years ago, eligible workers across the country had access to 26 weeks of benefits, a generally agreed-upon standard.15 After the Great Recession however, federal law barred states from cutting benefit amounts, so many states instead cut the duration of unemployment benefits to stabilize their trust funds and lower tax rates for employers.16 Some states now only provide 12 weeks of benefits, a short bridge to find a new job well-matched to workers’ skills and experiences.17 A minimum of at least 26 weeks of benefits should be a uniform requirement across states.

Workers cannot currently depend on the Extended Benefits program, which was intended to provide support during economic downturns by providing additional weeks of benefits during periods of high and rising unemployment. The triggers for Extended Benefits have not been effective, leaving workers to wonder if they are available. In their absence, workers must rely on temporary programs funded by Congress, which depend on political priorities.18 More reliable triggers, higher wage replacement levels, and a longer duration of benefits would make the Extended Benefits program more effective.19

Expand and standardize eligibility for benefits

Only certain workers are eligible for UI, leaving others stranded without benefits if they lose their jobs. Eligibility criteria and the base period used to calculate benefits are set by state law, rather than federal statute. This state-by-state variation can make it difficult for workers to know whether they qualify for assistance and plan accordingly.

Generally, the group of workers ineligible for benefits includes independent contractors, job seekers who have not been recently employed, and those who have exhausted their benefits. Some employers have incorrectly classified many workers as independent contractors, and this misclassification prevents those workers from requesting access to the UI benefits to which they would otherwise be legally entitled.20

UI should cover a broader range of workers, including part-time workers, independent contractors, job seekers who have not been recently employed, and seasonal and temporary workers. During the COVID-19 pandemic, Congress tasked states with implementing new UI programs for gig and platform workers who fell outside of the usual verification system, creating significant challenges.21 Barring changes to account for the unique situation of these workers, these same issues will arise again.22 By incorporating these workers into the UI system now, instead of waiting for a crisis, future challenges can be mitigated.

Policymakers should also consider stronger presumptive eligibility standards, under which workers are presumed eligible for benefits and paid promptly, with the burden placed on employers or the state to prove otherwise. Lowering the barriers for workers to receive benefits could meaningfully increase uptake among workers who currently go without benefits.

Align employer incentives with worker access to benefits

All eligible workers should be able to access UI benefits if they want them. State UI programs are generally financed through employer experience rating: Employers pay more taxes when they lay off more workers who are eligible for UI.23 Therefore, the system’s setup incentivizes employers to limit their tax liability by restricting eligibility for the program in different ways. This can include not hiring certain workers as employees, not proactively informing workers about their eligibility, attempting to reclassify workers as independent contractors, and other forms of suppressing UI program access.

A Bureau of Labor Statistics survey found that only about a quarter of workers who were unemployed in 2022 applied for benefits.24 Further, a recent survey found that 19% of recently unemployed workers encountered actions from an employer that were meant to deter them from applying, in addition to a quarter of all UI filers who said that their employer contested their benefits.25 Low-income workers more frequently reported employer contestations , and workers who reported contestations of their claims by employers were less likely to receive benefits and to experience greater material and psychological burdens during the filing process.26 There is a real opportunity to better align employer incentives to support workers in accessing UI benefits.

Presumptive eligibility, discussed above, would address some of these access barriers by shifting the burden of proof away from workers, but it would not resolve the underlying incentive employers have to minimize their tax liability. There have been many proposals for how to address this issue: broadening the tax base, payroll contributions from employees to UI, or increasing state taxes to fund programs.27 Most basically, employers could pay taxes based on the total number of workers they lay off, not just those eligible for UI.28 These questions are tied to broader considerations about how to finance a more effective program.

Center on worker needs and power

The UI system should be structured around ensuring that workers have the support they need as they seek reemployment. This requires a real investment in active labor market policy, including communicating with workers and unions about their needs in order to align training and placement. Congress should allocate funds to revitalize the Employment Service in the U.S. Department of Labor, which once played a key role in supporting reemployment.29 For example, in a 1986 New Jersey pilot program, job search assistance measurably helped dislocated workers return to work sooner and increased their earnings.30 A well-resourced and staffed U.S. Employment Service can leverage state labor market information—frequently used by employers—to help job seekers find opportunities aligned with their skill sets.31

Worker organizations can also play an important role in the delivery of benefits, including facilitating training and connections to job search assistance.32 In 2022, unemployed people who were members of a union were twice as likely to apply for UI benefits, and among applicants, 30 percentage points more likely to receive those benefits.33 This sizable gap suggests that when workers have organizational support navigating the UI system, they are far more likely to receive the benefits they are entitled to, reinforcing the case for involving worker organizations directly in UI outreach, filing assistance, and perhaps even delivery. Furthermore, unions routinely negotiate with employers, both in collective bargaining agreements and when bargaining before layoffs, to secure agreements that employers will not contest workers’ applications for UI benefits.

In some Northern European countries, unions are responsible for administering unemployment benefits, and scholars have found that this system leads to stronger unions and worker voice in the economy.34 Policymakers in the United States should consider how unions and other worker organizations could deliver unemployment benefits to workers.

Strengthen data infrastructure to improve outcomes

State workforce agencies collect quarterly wage records from employers to facilitate UI administration to gauge eligibility for benefits as well as benefit levels. States share wage records with the federal Census Bureau, and these data are included in longitudinal data sets that can follow workers over time. These data are used for both research and the administration of other policies (such as facilitating child support payments or tracking the performance of job training programs). Wage records provide an important but limited set of information that varies by state. A growing number of states have expanded their wage records to include additional information such as job titles, total hours worked, and job location.35

State-level data are a valuable source of information about what is happening to workers, but the lack of standardization across states makes it difficult to gain a clearer picture of employment. To remedy this, lawmakers should create standardized requirements for enhanced wage records to analyze who is losing their job and where workers are finding new opportunities.

The enhanced information coming from states would be especially useful with the advent of AI since no single federal data source can reliably track how AI adoption is reshaping demand for specific occupations in real time or identify when firms are reducing jobs through attrition. Further, workers would benefit from better information about opportunities that align with their skill set.36 Better integration with job posting data—including the National Labor Exchange—and reemployment systems would help workers navigate the job-search process.

Considerations for a stand-alone program

The UI system should be improved regardless of how AI affects employment and job opportunities. Additionally, some policymakers have suggested creating a stand-alone program geared toward AI-driven job loss. There is precedent for this approach. Congress has acted nine times since 1958 to create temporary, additional UI benefits, most recently during the COVID-19 pandemic.37 There is much to be learned from successes and challenges of these previous programs.

Some of these programs also included benefits or services beyond unemployment insurance for workers. For example, Trade Adjustment Assistance has, at various points, included relocation assistance, training programs, a health coverage tax credit, and wage insurance for older workers who took a lower-paying job.38 A new stand-alone program could similarly go beyond cash benefits, offering workers support to develop new skills, cover the costs of a transition, or find new opportunities.

Blank slate for program design

A new program can address the key structural issues with the current system discussed in the previous section. Any new program should be uniform across states so that benefit levels, duration, and eligibility are consistent for every worker regardless of where they live. A new program also presents the opportunity to build in the other improvements outlined above, including benefit levels set to a livable amount, expanded eligibility, and stronger data infrastructure. A new program could also adopt more fundamental changes to the claims process, such as the presumptive eligibility standards that Michigan moved toward during the COVID-19 pandemic.39

As discussed in the worker power section, a new program should be explicitly structured around building worker power, for example, by involving worker organizations in program administration or benefit delivery. Further, a new program can focus on effective job placement by leveraging relationships between employers, unions, and other worker organizations.

Finally, a new program should also be designed to avoid UI’s employer-financing structure, which—as discussed—in most states, disincentivizes employers from making sure workers get the benefits they are due. Left unaddressed, that same structure would allow employers to game or underreport AI-related job loss in a new program.

Relevant roles for key stakeholders

A successful program requires effective coordination among stakeholders. As with UI and previous stand-alone programs, states would likely retain a central role in delivering benefits and services, given their existing infrastructure and more direct knowledge of local labor market conditions. Local agencies can provide labor market information to workers, worker organizations, and employers to facilitate job placement. Any program should carefully consider how to balance flexibility for states to tailor the program to meet local needs, while maintaining strong, uniform standards across states.

As discussed, worker organizations and unions can play a central role in program administration. Worker organizations can support workers in accessing relevant benefits and training opportunities, ensuring that worker needs and outcomes are central to programmatic strategy and implementation.

Employers bear significant responsibility for the program’s success. They are often the first to know a layoff is coming and should proactively connect workers to available services, for example, by supporting on-site services when requested by state or local Rapid Response teams. Employers can proactively share specific information about their talent and skills needs with labor agencies and worker organizations, and hire workers during or after relevant training.

Eligibility criteria challenges

Creating clear and consistent eligibility criteria will be a significant challenge in establishing a stand-alone program for workers affected by AI-related job displacement. For instance, a program to compensate Department of Energy workers harmed by nuclear materials has faced significant challenges determining eligibility, including the complexity of reconstructing historical exposure to nuclear materials to establish causation.40 Challenges in establishing causation have also been an enormous barrier to worker relief through the Trade Adjustment Assistance program.41

In another example, some pandemic unemployment assistance benefits are still hung up over eligibility disputes years later.42 And from the start of the pandemic, there have been hotly contested debates about who should receive benefits.43 Moreover, concerns about fraud can also complicate eligibility determinations. In the wake of fraud during the COVID-19 pandemic, many states adopted stricter verification requirements to confirm eligibility.44 A new program focused on AI-driven job loss would need to balance similar integrity concerns against the risk of preventing eligible workers from receiving the benefits they are due.45

It is virtually impossible to systematically link job loss with AI adoption. In many instances, AI’s effects on a worker or industry may be indirect. For example, workers may lose their jobs in part due to AI adoption months or years earlier or because of broader economic disruption caused by AI’s impact on different industries. Even setting aside these implementation challenges, restricting eligibility to AI-driven job loss raises a separate concern: fairness. AI is not the only source of economic disruption. Global trade (including offshoring and outsourcing) and the green energy transition continue to displace workers across industries.

Other workers should be entitled to the same benefits as those directly displaced by AI. Drawing arbitrary boundaries around a single cause of job loss will create inequities, a risk compounded if states must independently interpret what counts as AI-driven job loss, therefore creating inconsistent standards nationwide.

Resources, time, and awareness to be effective

Standing up a new program will take significant time and resources. In addition to providing benefits, this includes making meaningful investment in technological systems and program administration, a particular challenge in light of years of underinvestment in modernizing UI systems.46 Federal and state governments need adequate resources for user experience testing, efficient application determinations, coordination with relevant partners, data collection and analysis, and programmatic evaluation. The challenges of implementing relief programs during COVID-19 are instructive. Slow initial payments, overwhelming caseloads that strained public servants, technological breakdowns, and significant confusion about eligibility requirements demonstrated the need to proactively develop systems that can respond more nimbly in times of crisis.47

Starting a new program also requires sustained public outreach, as new and stand-alone programs have historically struggled because of a lack of public familiarity. For example, over half of workers did not know they had access to emergency sick leave during COVID-19.48 In another example, when the Reagan administration made changes to the Trade Adjustment Assistance program, applications plummeted, perhaps because many workers believed the program had been abolished.49 Further, most workers did not know that other services including training, job counseling, and relocation assistance were available through the program.50

A stand-alone program could also come at the expense of the existing UI system, which the vast majority of states say is underfunded.51 From 1999 to 2019, federal funding for UI administration declined by 30% on an inflation-adjusted basis.52 If policymakers decide to divert resources that would otherwise go to the existing UI system, the system’s further deterioration could harm workers served by that system in both the short and long term.

Reauthorization vulnerability in Congress

If an AI-specific UI program requires congressional reauthorization, it will be vulnerable to the difficulty of coordinating bipartisan action in Congress. Several previous programs have been subject to a sunset date, requiring periodic reauthorization to continue accepting new claims. Sunsets and other reauthorization requirements leave workers vulnerable to congressional inaction, especially if the impacts of AI on the workforce play out over an extended time period.

For an example of a program caught up in this type of reauthorization limbo, Trade Adjustment Assistance lapsed multiple times, including most recently in 2022, leaving additional workers without access to the program. Another example is The Emergency Unemployment Compensation program, which was created in 2008 and lapsed five times between 2010 and 2013, before expiring for good at the end of that year.53 This immediately cut off benefits for 1.3 million workers and for more than 3.6 million workers by the following September.54

Financing and the path forward

The current worker safety net has failed to expand enough to cover the basic realities of today’s labor market, and inadequate benefit levels, incomplete coverage, and diminished worker representation are persistent problems. Deployment of AI and related technologies in the workplace adds urgency to addressing these issues.

Implementing the recommendations in this issue brief requires a significant investment that should be considered, along with program parameters and design. As AI drives up stock prices and generates wealth for a select few, policymakers are considering how the public should share in these gains. Ideas include increasing taxes for wealthy individuals, closing the gap between labor and capital tax rates, taxing AI use, and taxing data center development, with the proceeds from these new taxes financing programs that redistribute income more broadly across the economy. Considerations for potential taxation approaches will be discussed in a forthcoming brief.

This moment requires big thinking and corresponding action. While AI drives changes to the economy, improvements to the worker safety net are long overdue. We can learn from the past to improve the labor market for everyone.

Acknowledgments

Angela Hanks, Alex Hertel-Fernandez, Ben Zipperer, Gillian Gutierrez, Jacob Leibenluft, Janelle Jones, Josh Bivens, Michele Evermore, Patrick Oakford, Raj Nayak, and Stephen Nuñez.

Notes

1. Bureau of Labor Statistics, “Layoffs and Discharges: Total Nonfarm” [JTSLDL]. Accessed from FRED, Federal Reserve Bank of St. Louis, October 2026.

2. Patrick Oakford, Josh Bivens, and Celine McNicholas, Federal AI Legislation: An Evaluation of Existing Proposals and a Road Map Forward, Economic Policy Institute, September 2024.

3. National Academy of Social Insurance, Unemployment Insurance Task Force, Unemployment Insurance Task Force Final Report, July 2024.

4. Amy Traub, Boosting Economic Resilience: Seven Ways States Can Support More Workers with Unemployment Insurance, National Employment Law Project, July 2025.

5. Michele Evermore, AI Is New. The Challenge of Mass Unemployment Isn’t. National Employment Law Project, September 2026.

6. Michele Evermore and David Balducchi, A Roadmap for Unemployment Insurance Reform, The Century Foundation, October 2023.

7. National Employment Law Project, Policy Advocacy Brief: Benefit Amounts, November 2023.

8. U.S. Department of Labor, Employment and Training Administration, “UI Replacement Rates Report,” Benefit Accuracy Measurement data. Public data prepared by the OUI Division of Performance Management, Accessed August 7, 2026.

9. Megan Rivera, Why Unemployment Insurance Alone Is Not Enough in the AI Era, Washington Center for Equitable Growth, July 2026.

10. Julie Su, Andrew Stettner, and Michele Evermore, Our Unemployment System Needs Modernizing. Trump Is Doing the Opposite. The Century Foundation, September 2025.

11. Daphné Skandalis, Ioana Marinescu, and Maxim Massenkoff, Racial Inequality in the U.S. Unemployment Insurance System, Washington Center for Equitable Growth Working Paper, July 2023.

12. Elira Kuka and Bryan A. Stuart, Racial Inequality in Unemployment Insurance Receipt and Take-Up, National Bureau of Economic Research Working Paper no. 29595, December 2021.

13. Office of U.S. Senator Michael Bennet, “Bennet, Wyden, Beyer Reintroduce Bicameral Bill to Overhaul Unemployment Insurance” (press release), July 16, 2025.

14. Josh Bivens, Melissa Boteach, Rachel Deutsch, Francisco Díez, Rebecca Dixon, Brian Galle, Alix Gould-Werth, Nicole Marquez, Lily Roberts, Heidi Shierholz, William Spriggs, and Andrew Stettner, Reforming Unemployment Insurance: Stabilizing a System in Crisis and Laying the Foundation for Equity, a joint report of the Center for American Progress, Center for Popular Democracy, Economic Policy Institute, Groundwork Collaborative, National Employment Law Project, National Women’s Law Center, and Washington Center for Equitable Growth, June 2021.

15. National Academy of Social Insurance, Unemployment Insurance Task Force, Unemployment Insurance Task Force Final Report, July 2024.

16. National Academy of Social Insurance, Unemployment Insurance Task Force, Unemployment Insurance Task Force Final Report, July 2024.

17. Julie Su, Andrew Stettner, and Michele Evermore, Our Unemployment System Needs Modernizing. Trump Is Doing the Opposite. The Century Foundation, September 2025.

18. Michele Evermore, “There’s a Growing Bipartisan Consensus to Fix Unemployment Insurance” (commentary), The Century Foundation website, July 17, 2024.

19. Michele Evermore, “There’s a Growing Bipartisan Consensus to Fix Unemployment Insurance” (commentary), The Century Foundation website, July 17, 2024.

20. Ismael Cid-Martinez, Nina Mast, Margaret Poydock, and Valerie Wilson, Misclassifying Workers as Independent Contractors Is Costly for Workers and Social Insurance Systems, Economic Policy Institute, April 2026.

21. Julie Su, Andrew Stettner, and Michele Evermore, Our Unemployment System Needs Modernizing. Trump Is Doing the Opposite. The Century Foundation, September 2025.

22. Adam Grundy and Lucie Alleyne, “Nonemployer Businesses Are Key Contributors to Gig Activities and Economic Growth Nationally” (blog post), U.S. Census Bureau website, July 1, 2025.

23. Michele Evermore, “There’s a Growing Bipartisan Consensus to Fix Unemployment Insurance” (commentary), The Century Foundation website, July 17, 2024

24. Bureau of Labor Statistics, U.S. Department of Labor, “Most Unemployed People in 2022 Did Not Apply for Unemployment Insurance Benefits” (blog post), The Economics Daily (TED), April 14, 2023.

25. Amy Traub, Alexander Hertel-Fernandez, and Sanjay Pinto, The Unemployed Worker Study, National Employment Law Project, April 2025.

26. Alexander Hertel-Fernandez and Alix Gould-Werth, “Employer Contestations and Access to Unemployment Insurance: Implications for Benefit Receipt, Material Hardship, and Administrative Burden,” Social Service Review 100, no. 1 (March 2026): 38–68.

27. Michele Evermore and David Balducchi, A Roadmap for Unemployment Insurance Reform, The Century Foundation, October 2023.

28. Josh Bivens, Melissa Boteach, Rachel Deutsch, Francisco Díez, Rebecca Dixon, Brian Galle, Alix Gould-Werth, Nicole Marquez, Lily Roberts, Heidi Shierholz, William Spriggs, and Andrew Stettner, Reforming Unemployment Insurance: Stabilizing a System in Crisis and Laying the Foundation for Equity, a joint report of the Center for American Progress, Center for Popular Democracy, Economic Policy Institute, Groundwork Collaborative, National Employment Law Project, National Women’s Law Center, and Washington Center for Equitable Growth, June 2021.

29. Michele Evermore, AI Is New. The Challenge of Mass Unemployment Isn’t. National Employment Law Project, September 2026.

30. Stephen A. Wandner, Solving the Reemployment Puzzle: From Research to Policy, W.E. Upjohn Institute for Employment Research, January 2010.

31. Michele Evermore, AI Is New. The Challenge of Mass Unemployment Isn’t. National Employment Law Project, September 2026.

32. Alexander Hertel-Fernandez, “Solidarity Insurance: Reimagining Unemployment Insurance as a Platform for Worker Power.” (policy brief), Political Economy Research Institute, University of Massachusetts Amherst, July 2026.

33. Bureau of Labor Statistics, U.S. Department of Labor, “Characteristics of Unemployment Insurance Applicants and Benefit Recipients—2022” (economic news release), March 29, 2023.

34. Bruce Western, Between Class and Market: Postwar Unionization in the Capitalist Democracies (Princeton, N.J.: Princeton Univ. Press, 1997). 

35. Michele Evermore and Will Raderman, The Case for Enhanced Wage Records: Lessons from States, National Academy of Social Insurance and The Niskanen Center, January 2026.

36. Michele Evermore, “There’s a Growing Bipartisan Consensus to Fix Unemployment Insurance” (commentary), The Century Foundation website, July 17, 2024.

37. Katelin P. Isaacs and Julie M. Whittaker, “The Fundamentals of Unemployment Compensation,” Congressional Research Service, IF10336, updated January 12, 2026.

38. Jacob Leibenluft, Lessons from Past Trade Adjustment Policies to Support Displaced Workers in the Era of Artificial Intelligence, Washington Center for Equitable Growth, March 2026.

39. Michele Evermore, “Michigan Unemployment Claims Processing,” testimony on behalf of National Employment Law Project to a hearing before the Michigan Legislature, Joint Select Committee on the COVID-19 Pandemic, May 21, 2020.

40. U.S. Government Accountability Office, Energy Employees Compensation: DOL Generally Followed Its Procedures to Process Claims but Could Strengthen Some Internal Controls, GAO-16-74, March 2016.

41. Jacob Leibenluft, Lessons from Past Trade Adjustment Policies to Support Displaced Workers in the Era of Artificial Intelligence, Washington Center for Equitable Growth, March 2026.

42. Julie Kay, “‘No Accountability’: Pandemic-Era Unemployment Benefit Delays Persist, 5 Years Later,” WCNC Charlotte, April 3, 2025.

43. Annelies Goger, Tracy Hadden Loh, and Nicole Bateman, Debunking Myths About COVID-19 Relief’s ‘Unemployment Insurance on Steroids,’ The Brookings Institution, May 2020.

44. National Employment Law Project, ID Verification, November 14, 2023.

45. National Academy of Social Insurance, “Testimony of Michele Evermore, Senior Fellow, National Academy of Social Insurance,” testimony before the U.S. House of Representatives, Committee on Ways and Means, Work and Welfare Subcommittee, March 5, 2026. 

46. Julie Su, Andrew Stettner, and Michele Evermore, Our Unemployment System Needs Modernizing. Trump Is Doing the Opposite. The Century Foundation, September 2025.

47. Michele Evermore, AI Is New. The Challenge of Mass Unemployment Isn’t. National Employment Law Project, September 2026.

48. Emma Jelliffe, Paul Pangburn, Stefan Pichler, and Nicolas R. Ziebarth, “Awareness and Use of (Emergency) Sick Leave: US Employees’ Unaddressed Sick Leave Needs in a Global Pandemic,” Proceedings of the National Academy of Sciences 118, no. 29 (July 20, 2021): e2107670118.

49. U.S. Congress, Office of Technology Assessment, Trade Adjustment Assistance: New Ideas for an Old Program, 1987.

50. U.S. Congress, Office of Technology Assessment, Trade Adjustment Assistance: New Ideas for an Old Program, 1987.

51. Bipartisan Policy Center, “Administrative Failures Plague State Unemployment Insurance Programs,” July 1, 2020.

52. Bipartisan Policy Center, “Administrative Failures Plague State Unemployment Insurance Programs,” July 1, 2020.

53. Katelin P. Isaacs and Julie M. Whittaker, Emergency Unemployment Compensation (EUC08): Status of Benefits Prior to Expiration, Library of Congress, Congressional Research Service, CRS Product Number R42444, August 11, 2014.

54. U.S. House of Representatives, Ways and Means Committee Democrats, Unemployment Insurance (fact sheet), 2014.

Federal statistical agencies (FSAs) produce the gold standard economic data that employers, investors, job seekers, workers, and policymakers rely on to assess the health of the U.S. economy. Today, FSAs face historically unprecedented threats to their capacity and even their independence. This raises the specter of a future where FSA data cannot be relied upon to honestly report whether the U.S. economy is experiencing dysfunction.

This dashboard was initially created to display a range of data not collected or disseminated by FSAs to shed some light on the economy during the pause in federal data collection during the 2025 government shutdown. Now—perhaps even more importantly—it serves to provide an accountability check against efforts to manipulate FSA data in the future.

This set of “next-best” data sources is clearly inferior to the datasets that have historically been collected and analyzed by the nonpartisan, expert professionals who staff FSAs. Among many other relative weaknesses, these next-best data offer no insights on how the economy is affecting U.S. households differently by race, gender, or ethnicity.

Indeed job postings vs. BLS JOLTS

       

Indeed job postings correlate strongly with total job openings from the Bureau of Labor Statistics’ Job Openings and Labor Turnover Survey (JOLTS). JOLTS job openings tend to fall in recessions and rise in expansionary periods.

The Indeed Hiring Lab, the research arm of job search website Indeed.com, publishes a job postings data index that aggregates a complete count of all job postings on the Indeed website. This includes a measure of new job postings, which only counts job postings the first time they are visible. The data—available starting in February 2020—are collected daily and reported as a seven-day average. For this comparison, we take monthly averages of the seasonally adjusted daily data.

In the figure, we show that Indeed new job postings track closely with job openings from JOLTS. The correlation between these measures between February 2020 and August 2025 is quite high (0.95).

While Indeed data are only available for the last five years, JOLTS data go back to 2000 and show a clear relationship between job openings and business cycles. Job openings tend to fall in recessions and rise in expansionary periods. That relationship is clearest in the 2001 and 2007 recessions and expansions, where a fall in job openings preceded the start of the recession.

In the wake of the government shutdown, the Indeed new job postings can give use some information on the state of new positions needed by employers. Moving forward, a strong and sustained divergence of trends between the Indeed and BLS measures could provide a worrying signal of degraded data quality or integrity in BLS reports.

ADP employment vs. BLS private-sector payrolls

       

Monthly changes in ADP employment levels tend to track changes in private-sector payroll employment from the BLS Current Employment Statistics, and both measures show that private-sector employment growth falls in recessions and rises in expansionary periods.

The ADP National Employment Report is a monthly measure of the private-sector labor market based on aggregated payroll data of more than 26 million U.S. workers. ADP Research, a research arm of the ADP payroll processing firm, releases the report monthly. While the report provides much detail of employment by firm characteristics, the topline number in the report is private-sector employment changes, available since January 2010.

ADP employment changes track closely with the Bureau of Labor Statistics (BLS) measure of private-sector payroll employment in the Current Employment Statistics survey, data that is published every month as part of the Employment Situation Summary, also known as jobs day. In the figure, we compare seasonally adjusted monthly changes in employment for each measure smoothed to three-month moving averages. These smoothed changes are highly correlated (0.76).

While ADP data are only available as of 2010, BLS private-sector employment data goes back to 1938 and shows a clear relationship between the number of jobs and business cycles. Employment falls in recessions and rises in expansionary periods. The close relationship between these measures allows us to track any notable and sustained divergence, which would indicate a concern about the quality of government data.

Revelio vs. BLS employment data

       

Revelio Labs generates monthly U.S. employment estimates by counting social networking profiles on sites like LinkedIn. Total nonfarm employment changes from Revelio generally track BLS-based estimates.

Seasonally adjusted and nonseasonally adjusted estimates from Revelio are released monthly by industry, occupation, and geographic region. National data are available as of 2021. The data are normally published the day before the BLS employment report is released. Historically, Revelio’s monthly change usually falls below the BLS estimate, but over relatively short periods of time, the data series do tend to rise and fall together.

Challenger, Gray & Christmas vs. BLS unemployment rate

       

The Challenger job cut report data are somewhat noisy on a month-to-month basis, but spike noticeably during recessions.

Challenger, Gray & Christmas collects its job cut data by tracking public announcements made by U.S. companies in both the private and public sectors. The job cuts can include cuts in multinational plants (i.e., outside the United States). The report has been published monthly since 1994 and is typically released at the end of every month or the first week of the following month. The figure shows that large increases in announced job cuts occur in recessions (and when the unemployment rate spikes).

Of note, the spike in layoffs in spring 2025 was partially driven by announced layoffs in the federal government.

State-level UI claims vs. BLS unemployment rate

       

Monthly changes in unemployment insurance claims track the unemployment rate and provide a useful indication of recessionary periods.

While the U.S. Department of Labor (DOL) aggregates unemployment insurance (UI) claims data and is the source of the national data, it relies on administrative data processed at the state level that are generally posted on state government websites, potentially allowing a real-time accuracy check if concerns are raised about the accuracy of data released through the DOL portal. During the shutdown, the number of initial and continued claims has been updated weekly on the 539 report, a DOL-compiled dataset based on information submitted by state unemployment insurance offices. Right now, the data are only available on a nonseasonally adjusted basis because of the shutdown. The figure displays continued (or insured) UI claims and the unemployment rate, both as 12-month moving averages to remove some volatility and seasonality.

Continued UI claims closely track the unemployment rate: They spike during recessions and fall during economic recoveries. The close relationship between UI claims and the unemployment rate allows us to track any notable and sustained divergence, which could indicate a concern about the quality of government data. More information on the UI claims data, including initial claims, federal claims, and state-specific data, is updated weekly on EPI’s UI claims page.

Google Trends vs. BLS unemployment measures

       

Google Trends provides real-time data on the relative frequency of search terms for a given keyword, category of keywords, or a topic. With the right search terms, we can construct an index that tracks the overall unemployment rate

Google Trends is a publicly available database maintained by Google that provides Search Volume Indices. Search Volume Indices are a measure from 0–100 of the relative search intensity (number of searches for a given keyword divided by total searches) by geographic location and period. The data are updated in real time and are available from 2004 onward.

Google Trends data can be benchmarked to several different economic indicators. In fact, academics have used Google Trends data to “nowcast” GDP, private consumption, unemployment, recessions, and health outcomes. The figure shows the monthly average of select labor market-related search terms and the unemployment rate. The Google Trends relative search frequency for these terms tracks the unemployment rate and spikes during recessions. The close relationship between these measures allows us to track any notable and sustained divergence. Any prolonged increase in the search frequency of these select search terms could indicate a recessionary period.

Economic policy uncertainty vs. recession indicators

       

The economic policy uncertainty (EPU) index clearly responds to business cycle downturns—spiking sharply during the recessions of the early 1990s, early 2000s, late 2000s, and the COVID-19 pandemic.

The EPU index is calculated by Baker, Bloom, and Davis, and their full methodology can be found here. The index aggregates information from three basic components: search results from 10 large national newspapers measuring the volume of news articles discussing economic policy uncertainty; the number of federal tax code provisions set to expire over the next year; and the degree of disagreement among economic forecasters about future levels of key economic variables.

While the EPU is clearly cyclical, it does rise during some non-recessionary periods, most notably in the early 2000s and from 2011–-2013. The early 2000s increase is almost surely driven by the Iraq War. From 2011–2013, a rolling series of economic crises in the Eurozone, as well as short-term extensions of expiring provisions from the tax cuts passed in the first George W. Bush administration explain the spikes. The very large spike in early 2025 was related to the “Liberation Day” tariff announcements.

University of Michigan consumer sentiment data vs. recession indicators

       

Sharp falls in consumer sentiment are often followed shortly by recessions.

The University of Michigan consumer sentiment index has been calculated since 1960 (and has been calculated monthly since 1978). The index is derived from answers to five survey questions that have categorical answers (that is, their questions provide survey respondents with two to three possible responses to choose from, for example, “Would you say that your (and your family) are better off, or worse off financially than you were a year ago?”).

For each question, the University of Michigan researchers calculate a relative score for answers that subtracts the unfavorable responses from favorable responses. The sum of responses is then compared with a base value from 1966.

The index reached lows in 2022 that were comparable with most recessionary periods, despite a very strong economy. The clear explanation for that was the sharp inflation spike in late 2021 through mid-2022. Early 2025 saw similarly low measures, likely driven by concerns over the potential effects of tariffs.

Because the 1966 base year was a long time ago, it seems fair to ask if there are structural changes that might reliably change the level of consumer sentiment over time. For example, if rising inequality (or anything else) made U.S. households consistently less happy about their relative economic situation over time, they might generally have a lower “baseline” level of favorable sentiment.

To assess this, and to evaluate whether the University of Michigan consumer sentiment index might be useful for assessing the broader health of the economy, the figure below shows changes in the level of consumer sentiment and changes in inflation-adjusted personal consumption expenditures (the broadest level of household spending). The theory is that consumers’ sentiment about the economy should correlate positively with their actual spending patterns. The pattern is clear: Changes in consumer sentiment do indeed coincide tightly with changes in consumer spending.

Data Accountability Dashboard

Chicago FED's flow consistent unemployment rate (FCR) vs. BLS unemployment

       

The Chicago Federal Reserve flow-consistent unemployment rate (FCR) combines several sources of labor market data to predict monthly unemployment rates. The Chicago Fed FCR closely predicts the official unemployment rate published by the Bureau of Labor Statistics (BLS).

Twice a month, the Chicago Federal Reserve publishes the Real-Time Unemployment Rate Forecast, which predicts the BLS unemployment rate in advance of its official release. The key ingredient in this prediction is the Chicago Fed’s flow-consistent unemployment rate, which is in turn a prediction of Current Population Survey job finding and separation rates using real-time data like UI claims; Google Trends index for unemployment; Bloomberg consensus unemployment rate forecasts; Indeed and Lightcast job openings; ADP employment levels; weekly Morning Consult unemployment and job search activity; Conference Board labor market differentials; and JOLTS layoffs, discharges, and hiring rates. As a result, the Chicago Fed labor market indicators are partially based on U.S. government data, but they can be extended even in the absence of government data releases.

Notable weakness in today’s #jobs report for this morning. Payroll employment grew by 29,000 while downward revisions to both July and August totaled 60,000. July is now recording losses. The job growth in September (29,000) is slower than the prior 12 months (45,000).
#EconSky

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— Elise Gould (@elisegould.bsky.social) 8:39 AM · Oct 2, 2026

Payroll employment growth has averaged 50,000 over the last three months, but it continues to be relatively volatile. Four months of jobs losses over the last year, while some notable gains recorded as well. Economic uncertainty may be at play in the latest spell of weakness and downward revisions.

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— Elise Gould (@elisegould.bsky.social) 8:51 AM · Oct 2, 2026

Health care employment continues to chug along, adding 17,000 in September. Modest job growth in construction, leisure and hospitality, and manufacturing. After a bounce back in August, state and local government recorded losses for September.

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— Elise Gould (@elisegould.bsky.social) 9:00 AM · Oct 2, 2026

Manufacturing employment grew modestly in September, a gain of 9,000 jobs. But the fact remains that manufacturing employment is still down 21,000 jobs since January 2025. It’s hardly a manufacturing boon.

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— Elise Gould (@elisegould.bsky.social) 9:00 AM · Oct 2, 2026

Federal employment lost 1,000 jobs in September, but the deficit in the federal workforce remains stark: there are 328,000 fewer federal jobs since January 2025. The vital services federal employees provide cannot be done without these essential workers.
#EconSky

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— Elise Gould (@elisegould.bsky.social) 9:02 AM · Oct 2, 2026

Nominal wage growth continued to decelerate in Sept, rising just 3.0% over the year. Slowing nominal wage growth suggests workers don’t have the leverage to bid up their wages. Even with low unemployment, the depressed hires rate means workers aren’t finding new jobs to raise their wages.
#EconSky

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— Elise Gould (@elisegould.bsky.social) 9:05 AM · Oct 2, 2026

Slower wage growth means that with higher prices, workers and their families continue to find it difficult to make ends meet. Year-over-year real wages fell for five months in a row. When the latest price data is out on October 14, we’ll learn if real wages have fallen for six months straight.

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— Elise Gould (@elisegould.bsky.social) 9:10 AM · Oct 2, 2026

Key takeaways

• Years of government funding cuts are undermining the U.S.’s position as a global leader in providing the reliable statistical information that businesses and policymakers need for sound decision-making.
• The Trump administration has accelerated the funding cuts and worked to degrade the effectiveness and independence of data-collecting agencies.
• The Bureau of Labor Statistics (BLS) is a prime example of an agency whose data collection in areas like employment and wages is integral to our understanding of the economy’s health and whether it is heading into a recession.
• A decline in response rates to one of the BLS’s key surveys was already underway but, absent funding increases and survey modifications, it will be harder for economists and policymakers to make timely sense of changes in the labor market.

Historically, the U.S. has been a leader in providing reliable and timely statistical information to support business strategy and policymaking. The value of information provided publicly and free of charge to businesses, households, and governments is immense. Yet underinvestment over the past 15 years is a key reason why the U.S. lost its position on the cutting-edge of public statistical services worldwide.

Since the beginning of the second Trump administration, this underinvestment has accelerated, and the administration has made intentional efforts to degrade the effectiveness and independence of the federal statistical agencies (FSAs). This accumulation of threats to the effectiveness of the FSAs will rapidly degrade the value of the key public good they provide, unless policy changes course sharply.

This blog post provides just one example of how cumulative underinvestment has blocked the ability of a key FSA to respond to developments, making its data less reliable over time. The Bureau of Labor Statistics collects a range of necessary data tracking the performance of the U.S. labor market. This BLS data are a key input into high-stakes decisions across the U.S. economy—including for both public and private actors. For example, the Federal Reserve relies on BLS data about unemployment rates, payroll job growth, wage growth, and price indexes to set monetary policy. The more volatile the BLS data are from month to month, the worse the information that guides Federal Reserve decisions.

Private industry also relies heavily on these statistics. A 2018 survey conducted by the National Association for Business Economists found that 95% of businesses responded “yes” to the question: “Are government data important for analyses and forecasting that drive business decisions?” Employment and unemployment data produced by the BLS were rated as the most important data source for informing business decisions.

Yet over the past 15 years, the BLS has gradually lost personnel and funding, which has been undermining their mandate of producing timely, accurate statistics on wages, prices, and the labor market. More recently, the Trump administration’s choices to freeze BLS hiring has further strained Census field staff charged with collecting household survey data. Worst of all, the Trump administration took the unprecedented step of firing the commissioner of the BLS simply because the agency accurately reported data that the administration happened to find politically inconvenient.

Even without further blatant political pressure on the BLS’s independence, the agency will encounter growing difficulty in doing its job effectively in coming years. One of their most important efforts is the fielding of the Current Population Survey (CPS), a survey of thousands of households across the U.S. taken every month, which provides detailed employment and wage information. The CPS is the source data for the monthly estimate of the nation’s unemployment rate, for example. This is in turn a key criterion for assessing whether the economy is heading into recession. In recent years—after the COVID-19 pandemic—the response rates for the CPS have sharply declined. These declines, if not countered with greater investment in response rates, may make it harder for economists and policymakers to make timely sense of changes in labor market, particularly for populations that already have small sample sizes, such as rural areas or detailed demographic groups.

The rest of this blog post highlights the problem of falling response rates, demonstrates that they have made some labor market measures more volatile month to month, and shows that these falling response rates have occurred over the same period as the retrenchment in resources for the BLS.

Nonresponse reduces sample size in the Current Population Survey

The Current Population Survey asks questions about employment and other labor market characteristics to 60,0000 households or about 110,000 individuals every month. Between 2005–2016, the Current Population Survey household survey was able to steadily receive responses from around 107,000 people, ages 16 and older. However, as noted by others and shown in Figure A, the number of households responding to the survey has declined since the mid-2010s and then fell precipitously after the COVID-19 pandemic. In the first few months of 2026, just over 75,000 individuals, ages 16 and older, had responded to the monthly CPS.

Figure A

The decline in response rate has likely occurred for a few reasons. The Bureau of Labor Statistics notes that the rate of refusals had been increasing as early as the 1990s, likely as the world became more connected with computers and the internet, leading to less reliance on in-person interactions to conduct business. Social trust has also gone down over the past few decades, and the share of adults who agree that “most people can be trusted” has decreased by more than 15% since 1984.

More recently, the COVID-19 pandemic, coupled with concerns for privacy and distrust in the government, may be the reason that the rate of decline grew in recent years. The COVID-19 pandemic forced many workers to transition to remote work, and concerns about contagion limited overall social interactions, making response collection increasingly difficult. Additionally, concerns about privacy or retribution from the state felt by groups like immigrants may make some people more reluctant to answer questions for fear of deportation. 

Finally, distrust in the federal government, fueled by recent overtly political activity, could be behind some of the reduction in response rates. For example, when the Bureau of Labor Statistics published two consecutive months of large negative revisions to the number of payroll jobs in mid-2025, the Trump administration leveled charges—which were baseless and never backed up by any evidence—that the BLS had manipulated the data for political purposes and fired then Commissioner Erika McEntarfer. People are less likely to trust government if they think publicized information and facts are politically motivated. 

Smaller sample sizes are linked to less precision in key labor-market estimates

If the size of sampled households is large enough, declining participation does not have to significantly affect the reliability of statistics produced from the survey. However, if declines in participation reduce usable sample sizes too much, this can lead to estimates with less precision, which can reduce researchers’ ability to parse a signal from statistical noise in a timely manner, especially for economically vulnerable groups.

For example, because the unemployment rate for Black workers is volatile, it can be difficult to accurately diagnose labor market softness for this group. If the sample size is too small to generate statistical precision in each month, researchers will require increasingly more months of data to be able to diagnose labor market softness, which could jeopardize the timeliness of proper policy responses to support the labor market.

Every month, the Bureau of Labor Statistics publishes statistical significance summary tables, identifying whether changes in labor force indicators are statistically significant at the 90% level. BLS publishes these statistical significance tests for dozens of indicators across several demographic groups, including for Black workers. We collected these tables over time and documented the margin of error needed in order to claim a 1-month change in unemployment was statistically significant, shown in Figure B.

While the margin of error that is needed to claim a change is statistically significant varies with the level of unemployment rate, the reduction in precision from lower response rates is evident when we hold the unemployment rate constant. The two red lines in Figure B identify the effect size needed to claim statistical significance for a change from a starting unemployment rate of 7.3%. In November 2017, when the sample size of the labor force was 63,346, a 0.66 percentage point change in unemployment would have been considered a statistically significant change. In April 2026, when sample size of the labor force decreased to 45,416 respondents, a 0.84 percentage point change in unemployment is required to claim statistical significance.

If the declines in survey participation are not random across the U.S. population, estimates may also be biased, which runs the risk of conveying inaccurate information about the state of the economy. For example, if nonresponse is more likely to occur among unemployed respondents compared with employed respondents, the statistics derived from these samples may suggest labor market softness when there is none. These concerns are already materializing: The Census reported that nonresponse had biased income statistics from the CPS Annual Social and Economic Supplement upward by 2%–3% since 2020.

Researchers and field staff at Census and the BLS are aware of potential concerns of bias in their estimates and do their best to weight estimates using population counts from administrative data and other sources so that these issues don’t happen. However, if sample size declines continue on this trajectory, the BLS will need to create new methodologies and sampling strategies, all of which will require funding.

Steady throttling of BLS funding makes all decision-makers—public and private—less well informed

The declining precision of estimates in the Black unemployment rate is just one of the key indicators affected by a BLS that lacks resources to respond effectively to growing data collection challenges. Achieving a larger sample size for key surveys requires a well-functioning and well-funded BLS with personnel who can take on the challenges of administering surveys in the 21st century. Yet this is the exact opposite of what is happening. Figure C shows that from 2005 to the present, the staffing at the BLS went from roughly 2,500 employees to just over 2,150, a drop of about 15%.

Figure C

Funding has followed a similar trajectory. Since its high-water mark in 2010, the BLS budget has declined from $810 million to $636 million in inflation-adjusted terms, a decrease of 20%. These cuts don’t hurt just the estimates generated by the Current Population Survey. In the past couple of years, the BLS has been forced to reduce data collection for the Consumer Price Index and to discontinue certain Producer Price Indexes in an effort to cut costs. At a time when affordability and price changes are top of mind for U.S households and businesses, depriving public and private decision-makers of accurate and timely information about prices makes little sense.

Increased funding would allow the BLS to maintain all their current functions and implement new procedures to address declining sample sizes. In 2023, BLS began to modernize the collection process of the CPS to improve response rates by allowing online self-completion of the survey and other collection process improvements for certain data products. This BLS initiative is happening in parallel to similar initiatives in several other countries undertaking modernization efforts. The United Kingdom, the Netherlands, Australia, and Canada have all received funding to launch similar modernization efforts for their own household surveys to address declining response rates. However, the BLS requests for increased funding for the modernization efforts have not been fully granted.

The decision to steadily defund the BLS is especially striking when weighed against the large economic benefits provided by the agency and other federal statistical agencies. The BLS provides up-to-date precise estimates of economic indicators that policymakers and business leaders alike rely on. Previous research finds that increased economic uncertainty can have negative effects on the economy, proving the important role that the BLS plays. Moreover, some economists have estimated in 2025 that the BLS generates economics benefits of about $25 for every $1 spent on the agency’s budgets. The 2025 FY BLS budget was approximately $636 million, meaning the BLS currently generates about $15.9 billion in economic benefit. Across all agencies, in FY 2022, the combined budget request for statistical agencies was $7.1 billion or 0.3% GDP, yet the benefits have been measured to be around $770 billion.

Conclusion

At a time when more information on the economic and social well-being of people and communities is needed, not less, funding the BLS should be a top priority. Addressing nonresponse will require substantial effort and creativity to counteract declining levels of social trust and anti-government sentiment. It will, for example, require public campaigns to convey that information provided to the BLS is confidential and safe, and changes in methodology to render the correct statistical adjustments, such that the statistics generated are unbiased. 

Rather than tackle these challenges head on however, the Trump administration put forward a proposal that would reduce the number of statistics about rural and less populous substate areas that could be published without running the risk of disclosing personally identifiable information. These proposals are a lazy solution to the real but solvable problem of making public data widely available and fully confidential. They would provide less information on the economic and social well-being of citizens, likely leading to delays in accurately diagnosing economic and social problems.

When agencies like the BLS are underfunded and understaffed, they aren’t able to conduct the critical functions of their agency or serve the public to the degree their mission entails. Funding for these organizations shouldn’t be up for debate, given how strong of an economic benefit they deliver.

The cost of this wage suppression is real. Because wages make up a large share of income for most U.S. working families, lost wage growth meant sluggish income growth. However, there was one group where wage growth, and corresponding income growth, was substantial: the top 1%, whose wages skyrocketed by 182% since 1979. This uneven growth implies that today’s labor market, and the income derived from it, is deeply unbalanced. Rather than workers reaping the rewards of their rising productivity, a growing share of their labor has gone to the top 1%, leading to rising wage and income inequality.  

Decades of sluggish wage growth for typical workers—paired with the message that it was workers’ own fault for not obtaining the necessary skills for today’s economy—have left many pessimistic about whether policy changes can help them earn more. But if we want to achieve broad-based economic security in the U.S. economy, there is no alternative to raising pay for workers up and down the wage distribution. The U.S. economy generates enough income in aggregate to provide decent rates of pay growth for all workers—the challenge is a political one.

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The cost of inequality to middle-class households

The stakes of rising inequality for middle-income households in the United States are enormous. By 2022, steadily rising inequality was costing these households roughly $30,000 per year.

Figure 1 compares actual income growth of the middle fifth of nonelderly households since 1979 with what this income growth could have been had inequality not increased. The graph highlights market-based incomes because rising inequality was driven entirely by this income category—earnings from the labor market and business income like dividends and interest payments. We focus on incomes of nonelderly households because inequality largely stemmed from unbalanced labor market power, and nonelderly income is dominated by labor earnings.

In 2022, the average market income of the middle fifth of households was $96,335. But their income would have been $127,011—roughly 32% (over $30,000) higher—had inequality not increased after 1979. Put another way, if the middle fifth of households had seen market income grow at the overall average rate—an overall average pulled up by stratospheric growth at the very top—their income could have been roughly $30,000 higher by 2022.

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Income inequality is driven by unbalanced power in labor markets

The inequality in market-based incomes highlighted in Figure 1 was overwhelmingly driven by unbalanced power in labor markets that prevented typical workers from seeing their paychecks keep up with overall economic growth. Figure 2 shows hourly pay (including benefits) for the roughly 80% of the private-sector workforce who are not managers or supervisors and a measure of economy-wide productivity—defined as the income generated in an average hour of work in the U.S. economy. The rising gap between these lines is how we define wage suppression—keeping typical workers’ wage growth slower than growth in incomes in the rest of the economy.

In the three decades following World War II, hourly compensation for the vast majority of workers rose 2.1% on an annualized basis, roughly in line with productivity growth of 2.5%. But for most of the last five decades (except for a brief period in the late 1990s), pay for the vast majority lagged further behind overall productivity. Over the entire 1979 to 2025 period, hourly pay for a typical worker rose just 0.6% while productivity increased 1.4% on an annualized basis. This means that workers have been producing far more than they receive in their paychecks and benefit packages from their employers, and this wedge has grown substantially over time. Some of those gains in productivity went to greater profits for corporations but an even larger portion went to highly paid managers and professionals, resulting in a vast increase in wage inequality over the last five decades.

Had we pursued policies to support broad-based wage growth—instead of wage suppression—typical workers would have much higher pay today. In fact, if annual pay for production and nonsupervisory workers had instead tracked productivity growth since 1979, their pay would be 45% higher, or $30,000 more than it is today.

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Wage suppression boosted profits

The declining labor share of corporate-sector income—the share of corporate income received by workers in the form of wages and benefits—explains a portion of the wedge between productivity and pay. The labor share of corporate-sector income has not recovered to its pre-pandemic level, and remains well below its 1979 to 2008 average. Figure 3 illustrates the labor share of corporate-sector income since 1979. Typically, the labor share rises during recessions because profits fall much faster than wages during downturns. Then, in the early stages of recovery, the labor share falls significantly as profits increase much more rapidly than wages. Usually, the labor share then rises again late in an expansion as labor markets tighten and workers regain the bargaining power necessary to secure wage increases. Since 2000, no business cycle expansion has gone on long enough with unemployment low enough to put serious upward pressure on the labor share of income. Small upticks were seen sporadically over the last 15 years, but this progress, but this progress has stalled out.

Two things about the shift from labor compensation to profits in recent decades deserve some context. First, even as measured in official data, this shift explains well under half of the overall wedge between productivity and pay—the biggest piece of this gap remains inequality within labor compensation (we return to this point further below). Second, some of the changes in the official data might be more the outcome of accounting decisions than of fundamental changes in the economy.

Since the early 2000s, capital income (dividends, capital gains, and business income) has been taxed at lower rates than labor income. Highly privileged economic actors who have the ability to engage in tax evasion by reclassifying their income have likely done so in this time—relabeling income once classified as wages and salaries as capital income instead to pay lower tax rates. Recent research has estimated that roughly a third of the decline in the labor share of income might be driven by this kind of tax evasion rather than by a genuine change in which factors of production are seeing income gains.

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Highly paid corporate managers and professionals benefited the most from wage suppression

The largest portion of the growing wedge between productivity and pay can be explained by the astronomical wage growth for those at the highest end of the wage distribution. The ability of those at the very top to claim an ever-larger share of overall wages is evident in Figure 4. Annual earnings for the top 1% have grown 182% since 1979, while wages of the bottom 90% have grown just 44%. If the wages of the bottom 90% had grown at the average pace over this period—meaning that wages grew equally across the board—then wages for the bottom 90% would have grown by 65%, far higher and roughly 50% faster than the actual growth experienced. Earnings are so concentrated at the top that average growth is well above the 90th percentile of the earnings distribution. That means workers need to be among the highest 10% of wage earners to even experience average earnings growth. And everyone else—more than 90% of the workforce—saw less than average growth over the last four and a half decades.

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Since 1979, middle-wage workers only saw short-lived real wage growth during tightening labor markets

Between 1979 and 2025, the hourly wages of middle-wage workers (workers earning the median wage) cumulatively grew just 30%—about 0.6% per year. Yet even this disappointing cumulative wage growth occurred only because wages grew in the late 1990s and the late 2010s–2020s. Apart from these two periods, the wages of middle-wage workers were totally flat or in decline since 1979. Figure 5 shows real median wages since 1979. The red portions of the line highlight periods when median wage growth was genuinely stagnant, while the green portions highlight when growth occurred.

In the absence of strong institutions and labor standards like robust unions and high minimum wages, strong wage growth occurs only when labor markets are tight. In tight labor markets, the relative scarcity of workers gives them more power in the workplace to effectively demand decent hourly wage growth across the board.

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Wage suppression was not driven by increased benefits

The suppressed “pay” portion of the pay–productivity chart includes wages and benefits, suggesting that benefits have not offset slow wage growth. In fact, by many measures benefit generosity and availability have deteriorated alongside wage suppression. We explore trends in two key employment benefits in Figure 6: employer-sponsored health insurance and retirement coverage. We define employer-provided health insurance as insurance for which the employer pays some or all of the premium. We define retiree coverage as coverage a worker receives at their firm that offers it. We isolate workers who are strongly attached to their job, working at least 20 hours per week and 26 weeks per year.

For workers, sluggish pay has been compounded by the fact that health insurance and retirement coverage have declined significantly over time. Health insurance fell from 69.0% to 52.2% between 1979 and 2024. At the same time, retiree coverage nearly fell by half from 50.6% down to 29.0%. Furthermore, plan generosity has suffered with the trend away from defined benefit to defined contribution retirement benefits, as well as the shift to high deductible health plans.

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CEO pay grabs a larger share of wages

In a nation of increasing inequality, the most extreme disparities are between the heads of large American corporations and typical workers. Figure 7 tracks the ratio of pay of CEOs at the 350 largest public U.S. firms to the pay of typical workers. In 1965, these CEOs made 21 times what typical workers made. As of 2025, they make 325 times typical workers’ pay. This higher pay for CEOs does not reflect any increased contribution to corporate output or growth in the skills or productivity of CEOs. CEO pay gains help explain the growing divergence between pay and productivity. Further, the pay of CEOs and other highly placed corporate managers sets norms and market benchmarks across a range of elite occupations. The pay of specialty physicians, lawyers, consultants, and even sectors like university and nonprofit administration is likely pulled upward by rising pay for corporate executives.

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Tolerating excess unemployment has been deeply damaging for wage growth

As a rough rule of thumb, real wages for middle-wage workers outright fall when unemployment is greater than 5%.

Prioritizing high-pressure labor markets—those identified by extended periods of low unemployment—is key to providing lower- and middle-wage workers the leverage to bid up their wages. Figure 8 shows that the threshold for achieving any real wage growth at all is around 5% unemployment. In general, real wages grow when unemployment is lower than 5%, but they fall when unemployment is higher. Extended periods of low unemployment can also reduce wage inequality and racial disparities.

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The federal minimum wage no longer lifts wages

Figure 9 shows the decline in the real (inflation-adjusted) value of the federal minimum wage since its high in 1968. The last increase to the minimum wage was enacted in 2009, 17 years ago, which is the longest period without a minimum wage increase since its inception in 1938.

The minimum wage is essential for establishing the wage levels for the bottom fifth of wage earners. Today’s low-wage workers are far more educated, older, and experienced than low-wage workers in 1968. Yet, despite being more skilled and productive, their wages are 43% lower than wages earned in 1968, and 32% lower than the most recent increase in the minimum wage 17 years ago. The failure to raise the federal minimum wage has especially adverse effects on women and Black and Hispanic workers.

Luckily, many states have refused to follow the federal government’s inaction and have passed higher minimum wages at an increasing rate over the past two decades. Seventeen states and the District of Columbia now have minimum wages of at least $15 per hour, and more workers live in states with minimum wages over $15 than in states only bound by the too low federal minimum wage.

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The assault on collective bargaining hurts all workers

One of the main causes of wage suppression and rising wage inequality is the decline of collective bargaining, which lowers the wages of both union and nonunion workers. Collective bargaining not only raises wages for organized workers but also leads other employers to raise the wages and benefits of nonunion workers to come closer to union wage standards.

The erosion of collective bargaining can explain from one-fifth to one-third of the growth of wage inequality between 1973 and 2007; it had a greater impact on men than women. This erosion of unionization occurred despite large numbers of workers indicating they would prefer collective bargaining if they had a choice. But the political power of those with the most income, wealth, and power has prevented the adoption of laws to modernize our labor–management system and enable workers to pursue collective bargaining.

Figure 10 shows how wages could have grown if unionization hadn’t declined since 1973. If union density in 2025 had been the same level as union density levels of 1973 (14 percentage points higher than it is today), median wages would rise by 10.1%.

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Introduction

For Black Americans, public-sector employment has historically provided a pathway to better, more equitable and secure job opportunities compared with available private-sector jobs. The federal government has played an especially vital role in establishing a robust Black middle class in the Washington, D.C. metro area. According to the 2023 American Community Survey, roughly 2 out of 5 Black adults in the D.C. metro area were college graduates, Black median household income was nearly $90,000 and the Black homeownership rate was 52.8%. Postal service jobs have been particularly valuable to Black workers without college degrees because of the uniform wage and benefit structure (all postal employees who have the same job title and job tenure are paid the same nationwide) and higher pay relative to comparable private-sector employment. With a minimum education requirement of a high school diploma, the median hourly wage of a postal worker is 43% higher than the typical high school graduate. While federal employment has opened the door to social and economic mobility for generations of Black Americans, it has often been the battleground and served as a compass in setting higher labor standards and equal employment policies in the United States.

Opportunity. Backlash. Resistance. Change: A brief history of Black federal workers

The history of Black workers employed in the federal government dates to the Civil War when the federal government hired its first Black employee in the Treasury Department in 1863. In time, the federal government quickly became the largest employer of formerly enslaved people, with large concentrations in the military and the U.S. Postal Service (USPS). By 1912, the federal government was the largest employer of Black Americans in the nation, including highly skilled Black workers who were hired in high-ranking white-collar positions.

One of the earliest actions aimed at weakening the position of Black federal workers came shortly after the inauguration of President Woodrow Wilson. In 1913, Wilson racially segregated the USPS and Treasury department—the first federal agencies to employ, and in the case of USPS, promote Black workers to management positions. The administrative practice of segregating the federal workforce extended to the demotion of Black civil servants from white-collar positions, at-will firings, and refusal to fill open jobs with qualified Black candidates. Later that year, a group of Black workers formed the National Alliance of Postal Employees, the first industrial union in the federal service, to resist the administration’s racist tactics.

In the 1940s and 1950s, Presidents Franklin D. Roosevelt, Harry S. Truman, and Dwight D. Eisenhower each issued executive orders that took measured steps to undo the overtly racist and discriminatory federal employment practices put in place by Wilson. Those orders were largely directed at national defense industries, armed forces, and government contractors in response to the demands imposed by World War II. But, throughout the 1950s and 1960s, civil rights activists pushed the federal government to do more to expand its hiring of Black workers. In response, President Eisenhower’s Executive Order 10590 established the President’s Committee on Government Employment Policy (PCGEP) in 1955. The PCGEP involved federal agencies more fully in the government’s anti-discrimination agenda and called for departments to develop regulations in accordance with its mission to stop all discrimination in all federal employment. However, the group lacked the enforcement power necessary to accomplish that mission.

Over the following decades, job prospects for Black federal workers were most improved by a series of executive actions and legislation introduced in the 1960s and 1970s. On March 6, 1961, President John F. Kennedy’s Executive Order 10925 required the federal government and federal government contractors to practice non-discrimination in their hiring practices. Additionally, E.O. 10925 established the President’s Committee on Equal Employment Opportunity (PCEEO) to monitor non-discrimination on government contracts. In a move that distinguished the PCEEO from prior ineffective, enforcement-lacking efforts like Eisenhower’s PCGEP, Kennedy granted policy-making authority to the group led by Vice President Lyndon Johnson and Secretary of Labor Arthur Goldberg.

On January 17, 1962, Kennedy signed Executive Order 10988 which allowed limited collective bargaining for federal employees for the first time and opened the door to federal employee union membership under three different classifications: informal, formal, and exclusive recognition. Public-sector collective bargaining would play a central role in maintaining the quality and accessibility of federal jobs through labor contracts that fostered transparency with clearly defined policies and pay structures. Labor contracts also served to limit discriminatory outcomes while providing critical protections and recourse against other forms of exploitation or mistreatment.

The power of Kennedy’s executive orders was reinforced when Title VII of the historic Civil Rights Act of 1964, signed by President Lyndon Johnson, formally prohibited employment discrimination in the United States and established the Equal Employment Opportunity Commission (EEOC) to enforce the law. The Equal Employment Opportunity Act of 1972 extended Title VII protections to cover more employers and strengthened the enforcement power of EEOC by allowing them to litigate against employers, including federal agencies, who violated Title VII.

Within the span of the 1960s and 1970s, the federal government had established a clear definition of what it meant to be an equal opportunity employer, leveraged its purchasing power to compel private contractors to meet similar standards, extended limited collective bargaining rights to federal workers, and assigned the EEOC a central role in enforcing anti-discrimination law. Black federal employees also continued to support and advocate for one another, establishing the non-profit organization, Blacks in Government (BIG), in 1975. The progress made during 1960s and 1970s would be gradually chipped away in the decades that followed. 

Federal job losses since the 1980s

During the 1980s, the Reagan administration took a swipe at federal employees, unions, and anti-discrimination enforcement, but that record pales in comparison to more recent developments. While Reagan announced plans to make federal job cuts, and infamously fired 11,000 striking air traffic controllers in the early 1980s, federal payrolls actually rose by more than 200,000 during his presidency before dropping by 427,000 during the 1990s and taking another hit of 244,000 between 2010 and 2014. Since the 1980s, the postal service, a major employer of Black workers, has been under sustained assault, including attempts to undercut employee compensation and the agency’s solvency.

In 2025, the Trump administration took steps to implement massive cuts to the federal sector and reverse course in the government’s pursuit of equity by rescinding at least a dozen prior executive orders related to racial and/or gender equality and terminating workers in DEI departments within federal agencies. In a series of legally challenged actions, Trump fired decisionmakers at the EEOC and National Labor Relations Board (NLRB)—rendering two independent agencies responsible for enforcing workers’ rights non-operational for several months—while his newly created Department of Government Efficiency (DOGE) made severe staff reductions and eliminated entire federal agencies. Trump’s attacks on the federal workforce have also included attempts to limit the approval of collective bargaining agreements with federal workers. The actions of Trump and DOGE contributed to the loss of 288,000 federal jobs between January and December of 2025, based on data from the Bureau of Labor Statistics. Ironically, while federal jobs once provided Black workers relatively more job security, early evidence suggests the burden of federal job cuts has fallen disproportionately on Black women. The potential consequences of these actions go beyond job losses and include major implications for Black family incomes and racial and gender pay equity.

An accounting of the significance of federal sector employment for Black workers and families

As detailed in the history presented above, between 1941 and 1981, Black workers gradually improved their employment status in the federal government through collective and individual activism of groups like the National Alliance and Blacks in Government, within a context of official support for their rights through executive orders and landmark civil rights legislation. This improved employment status expanded the ranks of Black federal workers who were able to secure higher incomes. By 1970, the median household income for Black families was just $6,279 compared with a range of $7,178–$10,987 for those earning GS 5–8 salaries in the federal government. In fact, Black federal employees compensated between grades GS 5–8 were either close to or slightly above the national median of $9,867. This remains a factor today as the high concentration of federal employment and related professional job opportunities in the Washington, D.C. metro area helps to make metro D.C.’s Black median household income ($89,912 in 2023) one of the highest in the nation and well above the overall national median of $77,719.

Analysis of 2024 state-level data from the Office of Personnel Management (OPM) reveals that over 300,000 federal workers (excluding USPS) reside in the D.C. metro area, accounting for 60% of all federal workers in the District of Columbia and surrounding states of Virginia, Maryland, and West Virginia. Black workers are just over one-fourth of the federal workforce in the District of Columbia (28.8%), Maryland (27.9%), and Virginia (26%). While the D.C. metro area is home to the largest concentration of federal workers, over 90% of the federal workforce live and work outside the nation’s capital. Black workers account for at least one-fifth of the state’s federal workforce in 12 states beyond the D.C. metro area.

Implications of massive federal job losses and the unfinished business of equity

To understand the stakes of federal workforce contraction, it is necessary to compare the demographic and wage structure of federal employment with that of the broader labor market. As shown in Table 1, in 2023 and 2024, Black workers were 12.5% of the private-sector workforce, compared with more than a fifth (22.6%) of all workers in the federal sector—a share that also exceeds their representation in the entire public sector (16.4%) which includes state and local governments. Black women’s share of the federal workforce (12.8%) was double their share in the private sector (6.4%).

A national comparison of hourly wages at the median and for low-wage (10th percentile) workers demonstrates the clear monetary benefit of federal over private-sector employment. Figure A shows this is true across race and gender both at the middle and lower end of the wage distribution. The hourly wage of a typical (i.e., median) Black federal worker is more than 40% higher than that of the median Black worker in the private sector. Black federal workers—median and 10th percentile—also have higher wages than same gender white workers in the private sector. It is worth noting that these wage comparisons don’t account for the more generous benefits typically offered to federal and other public-sector workers, which further raises the value of their total compensation. The higher wages earned by federal workers largely reflect the higher share of college and advanced degree holders and higher rates of union coverage relative to private-sector employees. Less than 7% of private-sector workers are in a union or covered by a union contract compared with 35.9% of all public-sector workers and 29.5% of federal workers (see Table 1). While greater union coverage helps to boost wages and benefits for all workers, it is an even more important factor in raising wages of those for whom racial and gender discrimination further restrict individual bargaining power.

Another factor contributing to better pay outcomes in the federal government is the use of the Schedule (GS) pay scale which applies to over 70% of white-collar federal jobs. This helps to mitigate pay discrimination in the federal government by standardizing the qualifications and compensation associated with a specific position and consistent with experience, job performance, and local cost of living. On average, Black federal workers appear to experience only marginally improved pay equity over Black workers in the private sector, while the Black-white wage gap is much smaller in the public sector, overall.

In the federal sector, Black workers earn 12.6% less than white workers with the same levels of education, experience, union coverage status, gender, and state of residence, compared with 14.9% less in the private sector and just 3.8% less in the overall public sector (see Table 2). Although there is a sizable wage gap between Black women and white men across sectors, the federal sector gap (26.1%) is nearly 8 percentage points lower than the gap that exists in the private sector (33.9%). Given enforcement of the GS pay scale, remaining racial and gender pay gaps among federal workers likely reflect disparities in job positions and associated GS levels, a long-documented concern of Black federal worker advocates and activists. These disparities may stem from the underrepresentation of Black workers in higher-level, higher-paying positions, which can reflect differences across agencies in workforce demographic composition, occupational structures, and promotion rates. Notwithstanding the relatively higher economic position of many Black federal workers, these results epitomize the unfinished business of eliminating pay inequity and occupational segregation across all sectors of the labor market.

Conclusion

This brief summarizes the important role federal-sector employment has played in providing better job opportunities for Black Americans than have traditionally been available in the private sector. However, those outcomes have never been a given. A solid history of advocacy and activism by and on behalf of Black federal workers alongside others were critical in securing important wins through executive actions and policy change. Moreover, pushback against some of the most egregious violations of federal worker’s civil and worker rights have at times resulted in stronger, more broadly enforced labor and equal employment standards, improving outcomes to the benefit of all workers.

Today’s #jobs data can be considered a solid bounce back to relative weakness in June and July. Job growth has averaged 71k over the last 3 months. Weaker numbers for leisure and hospitality and unusual July losses in local government education employment seems to have resolved in August.
#econsky

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— Elise Gould (@elisegould.bsky.social) 7:43 AM · Sep 4, 2026

Leisure and hospitality and state/local government led the job growth for August, following by construction. Information and financial activities reported losses. Federal employment continues to trend down.
#EconSky

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— Elise Gould (@elisegould.bsky.social) 7:51 AM · Sep 4, 2026

With August losses, federal employment is now down 336,000 jobs since January 2025. The vital services federal employees provide cannot be done without these essential workers.
#EconSky

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— Elise Gould (@elisegould.bsky.social) 7:52 AM · Sep 4, 2026

The preliminary benchmark revisions were out last week, regular BLS communication needed for timely and accurate data. That release suggests there were 79,000 fewer jobs added than originally reported since Trump took office, including 178,000 fewer private sector jobs.

www.bls.gov/news.release…

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— Elise Gould (@elisegould.bsky.social) 7:59 AM · Sep 4, 2026

Nominal wage growth decelerated in August, rising just 3.1% over the year. Slowing nominal wage growth suggests workers don’t have the leverage to bid up their wages. Even with low unemployment, the depressed hires rate means workers aren’t finding new jobs to raise their wages.
#EconSky

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— Elise Gould (@elisegould.bsky.social) 8:15 AM · Sep 4, 2026

Even though unemployment held steady, I have continuing concerns about the depressed hires rate. Those who are lucky enough to have a job are sitting tight while new entrants or long-term employed can’t find work. Long-term unemployment has been steadily rising led by those unemployed over a year.

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— Elise Gould (@elisegould.bsky.social) 8:37 AM · Sep 4, 2026

Key takeaways:

• So-called right-to-work (RTW) laws undermine workers’ ability to form unions. Unionization rates are clearly lower in RTW states—and since unions raise wages, that suppression lowers pay.   

• States with RTW laws are also more likely to have other anti-worker laws and generally weaker labor standards. This is likely because both RTW and other anti-worker policies stem from the same anti-worker political roots, and because RTW’s suppression of unions deprives these states of strong potential political champions (unions) for other pro-worker policies.  

• Non-RTW states are more likely to have pro-worker policies like higher minimum wages and prevailing wage laws, restrictions on noncompete agreements, pay transparency policies, and more protective unemployment insurance benefits. 

• As a result, workers in RTW states are paid 6.7% less on average than workers in non-RTW states—more than double the 3.2% wage penalty we found in 2015. That means the RTW wage penalty has increased in recent years and now translates to over $4,000 less per year for a median full-time worker in a RTW state.

• The RTW wage penalty is steeper for women (7.3%) than for men (6.3%), and for Black (9.7%) and Hispanic workers (10.0%) than for white workers (5.4%).   

• Lawmakers already have the policy tools they need to boost wages and improve other outcomes for workers: 

• • At the state level, rolling back existing RTW laws is the most powerful lever.  

• • At the federal level, the Protecting the Right to Organize Act (which includes a ban on RTW) would be transformational for efforts to organize workers.  

• • Policymakers could also make progress on numerous other fronts, such as making UI systems more protective and raising minimum wages. 

In the United States, so-called right-to-work (RTW) laws are anti-union state policies promoted by big business interests to weaken unions and limit workers’ bargaining power. Despite the name, RTW laws do not provide any sort of job protection or right to a job. Rather, the deceptively named policy starves unions of resources, making it harder for them to negotiate collectively for better wages, benefits, and working conditions.

RTW laws emerged in the 1940s as part of anti-union campaigns to suppress worker organizing and maintain Jim Crow labor relations in Southern states; these campaigns worsened economic inequality and racial disparities (Childers 2024). Today, 26 states have active “right-to-work” statutes.1

As intended, RTW laws reduce union density (the share of workers who are either members of a union or covered by a union contract). These policies prohibit unions and employers from negotiating over union security—the contract terms under which workers covered by a union contract either join the union or pay an agency fee covering their share of representation costs. This deprives unions of critical resources and suppresses union membership (Sherer et al. 2026). RTW states—specifically those with RTW laws for at least 25 years—have an average union density rate of 6.2%, far less than half the union density rate in non-RTW states (16.0%) (see Appendix Table 1).

RTW laws not only reduce the number of workers with collective bargaining agreements but also limit unions’ political power to advocate for stronger labor standards for workers more broadly. This is, in part, why states without anti-union restrictions are more likely to pass protections such as prevailing wage mandates, restrictions on noncompete agreements, higher minimum wages, and pay transparency laws.

To assess the effects of RTW laws on outcomes for workers, we analyze whether wages are lower in RTW states after controlling for key demographic-, job-, and state-level characteristics (see Appendix Table 1 for full demographic controls as well as median and average wages by RTW status). We find that wages in “right-to-work” states are, on average, 6.7% lower than in non-RTW states. This penalty is worse for Black and Hispanic workers, as well as young workers and those without college degrees. Notably, the penalty is also larger for nonunion workers. This is because strong unions benefit workers across the state, not just those in unionized workplaces (Shierholz et al. 2026). In RTW states with weaker unions, nonunionized workers do not experience these positive “spillover” effects.

Pro-worker policies are more common in states without RTW laws

The strong correlation between wages and states’ RTW status is not surprising. Not only do RTW states have lower unionization rates, leaving workers with less leverage to bid up wages, but they often maintain other anti-worker policies. Combined, these anti-worker policies tilt bargaining power toward employers, weakening workers’ ability to garner higher wages. States without RTW laws are more likely to have a wide array of stronger labor standards in place that raise pay, protect workers’ ability to switch jobs, and support those who lose one. These same states—where unionization rates have not been suppressed by RTW laws—have a better safety net and stronger labor standards in part because stronger unions give workers more political power and the ability to better lobby for pro-worker policies writ large (Shierholz et al. 2026).

Figure A displays the incidence of these labor standards for states with and without anti-union RTW laws.2 This is far from a complete list of relevant policies that may correlate with RTW status and wage levels, but they are illustrative.

Figure A

Prevailing wage

Prevailing wage laws apply to certain contracts for public works and require that contractors and subcontractors pay the employees performing such work a prevailing wage rate. The prevailing wage rate is defined as the average wage paid to similarly employed workers in a specific occupation in the area of intended employment. Federal law requires this of federal contracts while state and local laws may apply to state and local contracts.

Prevailing wage laws are more common in non-RTW states. Figure A shows that nearly all (96.0%) of non-RTW states have prevailing wage laws, while only 23.1% of RTW states have these protections for workers on state contracts.3 Without prevailing wage requirements, contractors may reduce their workers’ wages to win bids on government contracts—putting contractors who pay their workers fair wages at a disadvantage.

Restrictions on noncompetes

Noncompete agreements are employment provisions that ban workers at a company from joining a competing business or starting their own for a set period after leaving a job. Noncompetes undermine economic dynamism and innovation; they depress business formation and labor mobility, hurt productivity and growth, raise prices, shrink workers’ wages, and restrict workers’ freedom (Shierholz 2024).

One of the most effective ways workers achieve higher wages is by securing another offer, and then either renegotiating their current salary or leaving for the new higher-paying job. It is not surprising then that workers in states that enforce noncompetes are paid less than similar workers in states that do not enforce noncompetes (Starr 2019). States without anti-worker RTW laws are nearly four times as likely to restrict noncompetes as RTW states (44.0% vs. 11.5%).4 In this analysis, we include only full bans on noncompetes and states with income-based restrictions. States with only small carve-outs for certain occupations provide a much weaker standard and are, by definition, less broad, so those aren’t included.

Minimum wage

Raising the minimum wage is particularly important for lower-wage workers. While the federal minimum wage has sat at $7.25 an hour since 2009, losing over 30% of its purchasing power, workers in 31 states enjoy a higher wage floor (Zipperer 2026). Non-RTW states are three times as likely to have minimum wages higher than the federal minimum compared with RTW states (92.0% vs. 30.8%). This is not a small difference. As of 2026, the average value of the minimum wage in non-RTW states is 60% higher—$5.36 more per hour—than in RTW states.5

Pay transparency

Pay transparency laws prevent employers from lowballing wage offers by requiring them to include wage information in job postings. While they vary, all laws include some requirement that employers provide salary information in job postings or if directly requested by applicants. Lack of knowledge about wages and benefits keeps potential workers in the dark and limits their ability to learn about and apply for better paid opportunities. A study on Colorado’s newly passed wage transparency law found that wages increased 4.2% faster than those in neighboring states without such laws (Shedge 2025). Further, wage transparency has the potential to reduce gender- and race-based discrimination by arming jobseekers with more information and limiting employers’ ability to pay different amounts to similarly qualified candidates. Only two RTW states have a pay transparency law, compared with 64% of non-RTW states.6

Unemployment insurance

Access to unemployment insurance (UI) also shapes workers’ ability to seek better wages. When a worker is laid off and their household income falls, unemployment insurance supports them and their family until they find another job. UI is a joint federal-state program that relies on state UI systems to effectively deliver benefits to unemployed workers. States differ in their rules and approaches under the federal UI framework, leading to wide variation in the share of unemployed workers receiving UI benefits (the UI recipiency rate).

Better UI not only cushions workers and their families but also provides workers with additional leverage in the labor market. When UI access and generosity fall, job seekers are forced to settle for lower wages and employers leverage this by offering less (Dahl and Knepper 2026). On the flip side, research shows a positive relationship between more UI generosity and wages (Rinz and Wasser 2026). The UI recipiency rate is significantly higher in non-RTW states compared with RTW states (32.8% vs. 18.7%).7 This is not surprising because non-RTW states have stronger unions, which fight to increase UI eligibility, ease of access, benefit levels, and benefit duration—to the benefit of all workers, union and nonunion (Hertel-Fernandez and Gould-Werth 2020). As with other pro-worker policies, better UI access can contribute to higher wages in non-RTW states.

Other policy levers

Not only do workers in non-RTW states enjoy higher wages, but they also benefit from increased economic security thanks to higher health insurance rates, higher public education spending, paid family and medical leave access, and no preemptions against higher labor standards. Every non-RTW state expanded Medicaid compared with fewer than two-thirds of RTW states (61.5%).8 Unsurprisingly, the uninsured rate is higher in RTW states than in non-RTW states (8.9% vs. 6.2%).9

Investments in public education deliver a more productive workforce and more informed and engaged society. Increased funding for schools not only improves educational attainment, but also increases wages and family incomes (Jackson, Johnson, and Persico 2016). Non-RTW states spend 45% more on public education than RTW states ($24,084 vs. $16,620).10 Paid family and medical leave provides essential benefits that help workers maintain their livelihoods while taking care of themselves and their families. These laws are also shown to increase labor force participation, job retention, and earnings (Glynn 2020). Only one RTW state has comprehensive paid family and medical leave provisions for private-sector workers, compared with 56% of non-RTW states.11

Finally, some cities, counties, and other local governments enact policies that raise standards for working people, but some state legislatures force those standards back down with preemption—the use of state law to void local ordinances. Preemptions apply to policies such as minimum wage, prevailing wages, paid leave, and fair scheduling (EPI 2025). Preemption laws interfere with local governments’ ability to set job quality standards. They suppress wages, exacerbate racial inequities, and reduce worker power (Sherer, Cohn, and Ahdoot 2025). These preemptions are more common in RTW states.

Workers in RTW states are paid less

Our analysis of the relationship between wages and RTW status puts states into three categories: long-term RTW states, always non-RTW states, and “switcher” states that have changed their RTW status since 2011.12 Appendix Table 1 provides descriptive statistics for these three groups of states, and Appendix Figure A provides a map of states by RTW status. Our estimates focus on comparisons between long-term RTW states and always non-RTW states.

A simple comparison of hourly wages, the primary variable of interest, reveals that average wages are 22.9% higher in non-RTW states ($40.28 in non-RTW states vs. $32.78 in RTW states). Median wages are 19.2% higher in non-RTW states ($28.79 vs. $24.16). Because there are differences between worker-, job-, and state-level characteristics in RTW and non-RTW states, and since some of these characteristics will directly impact workers’ wages, it is important to control for these factors in a multivariate regression model. This allows us to more accurately identify the relationship between RTW status and wages.

We estimate log wage equations using Bureau of Labor Statistics Current Population Survey Outgoing Rotation Group (CPS-ORG) data for 2023–2025. We pool three years of data to minimize any spurious year-specific economic relationships, thereby helping us achieve more precise estimates. The total sample consists of 234,155 workers, ages 16+, who earn wages and salaries. About 42% of the sample lives in states with RTW laws (see Appendix Table 1 for the sample’s full demographic breakdown).13

In Table 1, we construct a regression model, starting with an unadjusted framework and building up to a model that controls for the full range of explanatory variables. The dependent variable is the natural log of hourly wages.

The variable of interest is an indicator variable when the worker lives in a long-term RTW state. We also include an indicator in the regression for a switcher state, though that is not our focus of interest (see Appendix Table 2 for a complete set of regression results, including the coefficient for switcher states). There are currently 26 states with RTW laws on the books and 25 without them including Washington, D.C. (see Appendix Figure A). There are five switcher states that have adopted RTW in recent years: Indiana (2012), Michigan (2013), Wisconsin (2015), West Virginia (2016), and Kentucky (2017). In 2023, Michigan became the first state to repeal its RTW statute. We separate the five switcher states from the analysis to better isolate the relationship between wages and the long-run effects of RTW.

The naïve model, without any controls, suggests that workers in RTW states are paid 17.8% less than workers in non-RTW states (Model I in Table 1).14 But many factors contribute to wage differences: demographic characteristics such as age, race/ethnicity, gender, and level of education are relevant as well as job-related factors such as work hours, occupation, and industry. Since these vary across states, they reduce the measured relationship between RTW status and wages. Model II shows that when the analysis compares more similar workers with each other, the RTW penalty drops to 14.0%.15

There are other factors that are different in RTW states, aside from these individual and job-related characteristics. Most importantly, the cost of living. The cost of living in non-RTW states is higher than in RTW states, as shown in Appendix Table 1. In prior research, Gould and Kimball (2015) showed that choice of price indicator makes no material difference on the measured relationship between RTW and wages. Here we include the Bureau of Economic Analysis’ Regional Price Parity index: The regional price parity index is higher in non-RTW states.16 Not surprisingly, this correlates with wage levels and therefore is important to include in the model. As with earlier studies, we also include state unemployment rates to control for differences in economic conditions.

After including the full set of demographic, individual-, and state-level labor market and cost of living controls (Model III), our analysis finds that workers in RTW states are paid 6.7% less than similar workers in non-RTW states. For the average worker, this is a difference of $2.70 per hour, or more than $5,600 for full-time workers at 2,080 hours over the year. However, because the average is skewed upward due to wage inequality, an assessment at the middle of the wage distribution provides a more conservative but perhaps more robust estimate of the RTW wage penalty. Acknowledging that this is a likely understatement of the full relationship, the RTW wage penalty for the median worker at full-time hours is over $4,000 for the year.

Table 1

This does not mean that RTW laws are a causal explanation for this 6.7% wage penalty. As demonstrated earlier in this report, RTW status correlates strongly with a number of other anti-worker policies. Other research has attempted to calculate the causal effects of RTW laws on wages. Using the switcher states in the most recent period, Fortin, Lemieux, and Lloyd (2022) found smaller wage penalties, which is to be expected given the shorter time frame for measurement post-adoption. Our wage penalty for switcher states—shown in the final column of Appendix Table 2—is much smaller (closer to zero) than the coefficient for long-term RTW states, though not statistically significant at conventional levels. Dasgupta and Merchant (2023) also examine states recently adopting RTW laws and find a statistically significant decline in annual wages by almost $1,900. Again, these states are more recent RTW adopters, so the full effects we find may take time to materialize.

We find that this relationship between long-term RTW status and wages remains economically and statistically significant under alternative specifications of our econometric model. For instance, removing major industries and occupations changes the coefficient to -0.071. Restricting the model to RTW status as of 2026—removing the separate indicator for states that switched since 2011—yields a coefficient estimate of -0.060. Doing the same but dropping Michigan (since its status changed in the measurement period) results in a coefficient of -0.064. In our final model, we do not include an indicator for unionization, unlike Gould and Kimball (2015). If we reintroduce that variable, the coefficient is -0.063.

In each of our regressions discussed above, we include a three-year average as it improves data reliability and allows us to run smaller groups of the data (see next section). When we run our analysis using just 2025, our results do not change. In fact, when we run our three-year final model for the periods 2021–2023, 2022–2024, and 2023–2025, our coefficients remain in the -0.070 to -0.068 range.

Wage penalties are steeper for women, Black, Hispanic, and nonunion workers in RTW states

The RTW penalty is not uniform across demographic groups and other labor market characteristics. Table 2 below shows the results of a series of final-model regressions (comparable to Model III in Table 1) for a set of demographic groups.

We find that women’s wages in RTW states are penalized at a higher rate (7.3%) than men’s (6.3%). The wage penalty also persists across all racial and ethnic groups, but it is almost twice as large for Black (9.7%) and Hispanic (10.0%) workers as for similar white workers (5.4%) and more than twice as large as for Asian American and Pacific Islander (AAPI) workers (4.6%). Young workers between the ages of 16 and 24 face a greater penalty (8.4%) than older workers. And workers without a college degree experience a much higher penalty (7.8%) than workers with a college degree (4.7%).

Table 2

It is particularly important to note that RTW laws have a statistically significant negative effect on the wages of nonunion workers, as shown in the last row of Table 2. Our analysis indicates that nonunion workers in RTW states have wages that are 6.3% lower, on average, than their counterparts in non-RTW states. Union members likely experience a smaller penalty (3.6%) because their wages tend to be higher than those in nonunionized workplaces, regardless of the state’s RTW status (McNicholas et al. 2025). However, the wage impact of unions extends beyond those directly covered by a union contract. Unions lift wage standards across entire industries, meaning that nonunion workers in states with higher union density are more likely to receive better pay.

As the gap in wages and labor standards between RTW and non-RTW states has grown, so has the wage penalty

Using our final model (Model III in Table 1) with pooled 2023–2025 data, we find a 6.7% RTW wage penalty—a larger penalty than we found in earlier examinations. For example, we found a 3.2% wage penalty using data for 2009 (Gould and Shierholz 2011) and pooled 2010–2012 data (Gould and Kimball 2015). Some of the increase in the current estimate of the RTW wage penalty reflects small changes (likely improvements) in our methods. But part of the higher estimate persists even when we apply our current methods to earlier data: Applying these methods to the 2010–2012 data yields an RTW wage penalty of 4.7%.

One method change concerns “switchers”—states that changed from non-RTW to RTW status (or back) since 2019. In the 2010–2012 period, there were no switchers to affect the data.17 In our analysis, we isolate switchers from the long-term RTW group, but the RTW penalty remains negative and significant no matter how one deals with the issue of switcher states. For example, recategorizing the five switchers to match their 2012 status (all non-RTW) lowers the measured RTW wage penalty in the 2010–2012 period to 4.1%, but it remains significant.

Other changes relative to our earlier research include controlling for public-sector status; removing union as an independent variable from the regression; using the full working age range of 16 and up (as opposed to 18–64); and correcting an issue with nonrandom missing values to the metropolitan variable. We’ve further clustered standard errors at the state level as suggested by Douglas (2024), which does not affect the size of the coefficients, only the size of their standard errors and resulting statistical significance.18

Setting those minor model differences aside, a consistent comparison over time sees the RTW wage penalty rising from 4.7% in 2010–2012 to 6.7% in 2023–2025. One possible reason why this RTW wage penalty grew could be the increasingly divergent policy trajectories of RTW and non-RTW states over this period.

For example, the gap between minimum wages in RTW states and non-RTW states has grown since 2010, largely because policymakers in most RTW states have refused to increase their minimum wage despite the fact that the federal minimum wage has been stagnant in nominal terms since 2009. In that year, the average minimum wage in RTW states was $7.25 while the average minimum wage in non-RTW states was $7.54. That gap has grown enormously, from just $0.29 per hour in 2009 to $5.36 per hour by 2026 ($14.24 in non-RTW states vs. $8.89 in RTW states).

There were other potentially relevant policy changes between 2012 and 2025. The first state-level pay transparency law was passed in Maryland in 2020. Seventeen states followed suit in subsequent years, 15 of which were non-RTW states. On the flip side, six states have repealed their prevailing wage laws since 2012—all of which were RTW. Only one state, Michigan, reinstated its prevailing wage law. It did so in 2023, the same year it repealed RTW. While this is far from an exhaustive list, it’s not surprising that the measured RTW wage penalty has grown over time.

Repealing anti-union “right-to-work” laws and strengthening labor law is key to raising wages

The wage penalty for workers in RTW states has grown since the last time we conducted this research a decade ago. Long-standing and more recently enacted anti-union RTW laws in 26 states, in tandem with other anti-worker policies, have further eroded workers’ collective bargaining power, suppressed wages, and lowered labor standards. These outcomes are linked to clear policy choices, and lawmakers at every level of government have the tools they need to restore workers’ union rights, strengthen labor standards, and lift wages. Across the U.S., 56 million workers say they want a union in their workplace, yet only 10% of all workers have one (Shierholz et al. 2026). Current federal and state labor laws—including anti-union RTW laws—place too many obstacles in workers’ path to unionizing and give employers too much power to interfere with workers’ free choice.

First and foremost, states with RTW laws should follow Michigan’s recent lead and repeal them. Repealing state RTW laws will enable workers to unionize and raise wages across the country. Indeed, recent EPI research finds that equalizing collective bargaining rights across all states by repealing RTW laws that affect private employees and ensuring collective bargaining rights for public employees would increase national union density by almost 50% (from 9.9 to 14.4%) (Shierholz et al. 2026). States should also consider passing constitutional amendments that affirm collective bargaining rights and explicitly bar RTW-style restrictions, such as the 2022 Workers’ Rights Amendment in Illinois (Sherer 2026).

At the federal level, lawmakers should pass the Protecting the Right to Organize (PRO) Act. The PRO Act is designed to address major weaknesses of the National Labor Relations Act (NLRA), which has been severely eroded by amendments and court decisions. Among many other reforms, the PRO Act would eliminate the option for states to maintain anti-union RTW laws—restoring full bargaining rights to workers in all states as intended when the NLRA was originally passed in 1935 (McNicholas, Poydock, and Rhinehart 2021). Since its first introduction in 2019, the PRO Act has passed the House of Representatives twice with bipartisan support (Shierholz et al. 2026).

In addition to proposed PRO Act reforms that would enable newly unionized workers to pursue arbitration to achieve a first contract (if an employer fails to negotiate in good faith), federal labor law should set a minimum standard that such contracts include a cost-of-living adjustment (COLA). To further expand the benefits of collective bargaining, Congress should amend the NLRA to require default collective bargaining at any firm where CEO-to-worker pay ratios exceed 100 times what a typical worker makes in their industry (as this ratio would be one proxy for whether conditions at an employer are particularly unequal) (Shierholz et al. 2026). 

Legislators at both the state and federal level should also look to the list of anti-worker laws that are typically found in RTW states for more opportunities to rebalance unequal power in the labor market and improve wages and working conditions. These policies include:

• Raising the minimum wage;
• Banning noncompete agreements;
• Strengthening federal unemployment insurance guidelines and state UI systems;
• Removing state restrictions that preempt local governments from raising wages and strengthening labor standards.

These are among just a few of the policy levers states can use to raise wages and standards (EPI 2026b).

Our analysis suggests that repealing anti-union RTW laws and enabling workers to increase unionization levels are key to raising wages, both because RTW laws directly suppress unionization rates (and unions raise wages), and because weakening unions limits workers’ collective ability to shape other public policies.

Acknowledgements

The authors are grateful to Josh Bivens, Jennifer Sherer, Hilary Wething, and Ben Zipperer for their helpful comments and expertise.

Appendix

Appendix Figure A

Appendix Table 1

Appendix Table 2

Notes

1. See Appendix Figure A for RTW status. As of 2026, these states are Alabama, Arizona, Arkansas, Florida, Georgia, Iowa, Idaho, Indiana, Kansas, Kentucky, Louisiana, Mississippi, North Carolina, North Dakota, Nebraska, Nevada, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Wisconsin, West Virginia, and Wyoming.

2. States are categorized as “RTW” or “non-RTW” based on their status as of 2026.

3. Authors’ analysis of prevailing wage laws from DOL (2023) and state legislative websites.

4. Authors’ analysis of noncompete restriction policies from EIG (2026).

5. Authors’ analysis of minimum wage data from EPI (2026c).

6. Authors’ analysis of pay transparency laws from GovDocs (2026).

7. Authors’ analysis of UI recipiency rate data from DOL-ETA.

8. Authors’ analysis of Medicaid expansion policies from KFF (2026).

9. Authors’ analysis of uninsurance rates by state, 2023 through 2025, from Carter (2025).

10. Authors’ analysis of per-pupil education spending data from NCES-NPEFS (2024).

11. Authors’ analysis of paid family and medical leave policies from Wielk (2026).

12. Prior to 2012, the last state to change its status was Oklahoma in 2001.

13. This analysis builds on earlier work by Gould and Kimball (2015); see the report for a full description of the methodology and list of controls.

14. Interpreting the results from these semilogarithmic functions requires utilizing the exponential function on the coefficient. Specific to the binary variable coefficient (β1) for RTW, the percent change in workers’ wages resulting from a state being RTW can be calculated by the formula: 100*[exp(β1)-1]. Typically, the result of this equation will be very close to the coefficient itself but will differ more as the coefficient becomes larger. We show the coefficient in the first row of Table 1 and the exponentiated result as a percent in the third row.

15. Full regression results for covariates in all three models can be found in Appendix Table 2.

16. Because the 2025 BEA RPP data were not yet available at the time of publication, we assign an average of 2022–2024 data to all years in our sample (2023–2025).

17. Gould and Kimball (2015) classify Indiana as non-RTW as its RTW law had just taken effect.

18. We do not control for state-level fixed effects, as Douglas (2024) suggested, because state-level fixed effects perfectly correlate with RTW status and would deem our results uninterpretable.

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Weak #jobsreport all around
-Nonfarm payrolls fell by 23,000 (mostly driven by state and local losses)
-The unemployment rate ticked down for the “wrong” reasons as labor force participation and employment-to-population ratio softened
-Nominal wage growth decelerated to 3.2% over the year
#EconSky

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— Elise Gould (@elisegould.bsky.social) 7:47 AM · Aug 7, 2026

Large losses in the public sector in July (-53k), particularly in local education (-49.6k jobs). An initial look doesn’t suggest an issue with seasonal adjustment because losses were also registered in NSA data for June and July. Local education jobs have fallen by nearly 100k (-98.3k) since March.

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— Elise Gould (@elisegould.bsky.social) 8:01 AM · Aug 7, 2026

While public sector weakness in July was centered around state and local jobs, federal jobs ticked down by 3,000. Federal employment remains 327,000 below its January level. The vital services federal employees provide cannot be done without these essential workers (e.g. food inspectors).
#EconSky

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— Elise Gould (@elisegould.bsky.social) 8:12 AM · Aug 7, 2026

Although manufacturing employment ticked up slightly in July (+5k), manufacturing jobs are still down by 62k since January 2025.

Instead of adding these blue collar jobs, the manufacturing sector has lost 62,000 jobs since Trump took office.

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— Elise Gould (@elisegould.bsky.social) 8:16 AM · Aug 7, 2026

Nominal wage growth decelerates in July, rising just 3.2% over the year. Slowing nominal wage growth suggests workers don’t have the leverage to bid up their wages. And, along with rising prices, workers and their families continue to find it difficult to make ends meet.

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— Elise Gould (@elisegould.bsky.social) 8:26 AM · Aug 7, 2026