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.