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Chapter 3

Building a Resilient Modern Workforce

By Brent Orrell and Mason M. Bishop

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The post-COVID-19 hangover of contradictory and perplexing economic data—lingering inflation, historically low unemployment, layoffs, labor shortages, housing costs, banking instability, and empty office towers—has produced empirical and subjective disorientation in today’s economy and job market. The surge of interest and investment in artificial intelligence has added to this uncertainty because, while most analysts agree the effects will be important, they disagree on how AI will affect specific jobs. Some believe AI will make human beings more productive than ever; others argue the technology is likely to swamp workers with bigger workloads. And yet others fear AI will make people redundant. Given the radical economic uncertainty in which we are living, how can we frame an approach to the future of work that provides workers with meaningful guidance for coping with rapid change?

This chapter provides a framework for thinking about—and mapping a strategy for—an uncertain future. The first part deals with the three main factors that will shape the future of the workforce: (1) the slowing growth in the number of available workers, (2) the rise of artificial intelligence, and (3) the growing importance of noncognitive or soft skills in enabling workers to adapt to technological change.

In the second part of the chapter, we propose a number of policy prescriptions that will help reinvigorate the federal workforce development system to better serve the interests of workers, businesses, and state and local governments. The objective of these policy proposals is to provide a general guide for building the resilient and adaptable workers that our economy will require in the future.

People, Technology, and Skills

Three major factors determine the future of the workforce: people, technology, and skills. Each of these factors interacts with the others, driving economic change and labor market demand. A shrinking workforce (relative to the size of the economy) will keep labor markets tight. Burgeoning new technologies may counteract tight labor markets via automation, but productivity increases will likely increase aggregate consumption and raise demand for workers. Rapid technological change will accelerate skill obsolescence in ways we have never seen before, forcing workers to retrain more frequently. We call these interacting issues—labor force decline, productivity-driven labor demand shifts, and the resulting increased need for re-skilling and noncognitive skills—the workforce trilemma.

Decreased Labor Supply

Falling population growth is the most important factor shaping the future of our workforce. While the global population is now above eight billion, up from three billion in 1960, the rate of population growth is slowing.1 Projections show the global population will peak at around 10 billion in the mid-2080s before stabilizing and eventually declining.2 In the meantime, the median global age has risen from 20 years in 1970 to over 30 today.3

US demographics mirror these global trends. Our population rose from 180 million in 1960 to 343 million in 2025.4 Between 1960 and 1970, the US population increased by 13.3 percent; between 2010 and 2020, it grew by just 7.4 percent.5 In 1960, the number of expected births per woman stood at 3.65; by 2022, it had dropped to 1.67.6 This decline in fertility rates has led to an aging population, with the median age rising from 30 years in 1960 to 39 years today.7

As population growth slows and the median age rises, fewer people are available to sustain economic growth. Between 2000 and 2005, the US working-age population grew by nearly 12 million; however, between January 2017 and January 2022, it grew by just 1.7 million.8 This deceleration in prime-working-age population growth means we will have less of the “raw material” to supply the talent the American economy requires to thrive.

Aggravating this demographic squeeze is the fact that Americans no longer work at the same rate as in the past. As recently as June 2003, 66.5 percent of adult Americans were in the labor force.9 As of September 2024, that rate had fallen to 62.7 percent. The decline in labor force participation has been especially sharp among working-age men (age 25–54), whose participation rate has fallen from 97.1 percent in 1960 to 89.5 percent today.10 This means that 6.6 million men are not working or looking for work, and many of them are dependent on federal disability programs or are otherwise on the margins of the economy.11

Technology and Skills Obsolescence

The shrinking number of available workers increases our dependence on technology to handle tasks traditionally done by people. At the same time, the acceleration of technologies that enhance automation introduces uncertainty about their impact on labor demand. In other words, technology is both a solution and a problem for employers (who will likely have to retool their businesses and workforces more often) and workers (who will bear the burden of learning the new skills required as technology evolves).

While technology and productivity improvements are critical to raising living standards and improving well-being, they create new short- and medium-term challenges. New technology often erodes the value of existing skills, leaving workers with a difficult choice: Accept lower-paying jobs that may not suit them or invest in retraining for new, less familiar jobs. The downside to the latter is that learning requires individual effort, financial cost, and income loss from dropping out of the labor market. Moreover, as the population ages, re-skilling becomes more challenging; cognitive flexibility, which includes our capacity to learn, declines with age, making retraining more difficult.

Over the past 50 years, workers without a postsecondary education, especially men in manufacturing, have borne the brunt of the labor force disruptions caused by technological change. Computers and robotics have taken over a large share of productive activity in goods-producing firms. What is often characterized as the “China shock” that cut manufacturing employment is better characterized as a longer-running “automation shock.” Regardless of the cause, the rapid decline in manufacturing employment has left a trail of profound and lingering economic and social disruption.

AI, however, may flip the script of automation by affecting more highly educated and skilled workers in the knowledge and information sectors.12 A study by the University of Pennsylvania and OpenAI found that around 80 percent of the US workforce could see the introduction of large language models (LLMs) affect at least 10 percent of their work tasks, and nearly 20 percent of workers may have at least half their tasks affected.13 This AI-driven automation using LLMs is likely to affect sectors like law, finance, accounting, insurance, and data processing, which are now more exposed to automation than are jobs requiring manual skills—a significant shift from prior chapters of automation.

Figure 1. Skills Tiers

Source: CareerOneStop, “Energy Generation, Transmission and Distribution Competency Model,” https://www.careeronestop.org/competencymodel/competency-models/energy.aspx.

Noncognitive Skills: Building Adaptability and Resiliency

As AI and other emerging technologies reshape the economy, some worker populations that have never faced skill obsolescence may need to reskill. Even those who retain their jobs face a future in which technological change will require regular skill updating. However, how and when these skill changes will affect workers is uncertain, making planning particularly challenging for businesses, individuals, educators, and the government.14

Considering this uncertainty, adaptability is likely to become the most important skill for the future rather than any particular “narrow” skill (e.g., coding or welding). In this context, adaptability should be understood as the ability to learn in a fast-changing technological environment. This makes it imperative that we return to the basics by strengthening the noncognitive skills that form the foundation of learning.

As research and employer feedback have confirmed, these types of skills are rising in value and are the most critical, yet missing, skills in the workforce.15 A recent survey by IBM confirmed this reality.16 In 2016, employers ranked proficiency in STEM as the most important skill for the future. By 2024, the same survey found that a cluster of noncognitive skills—time management, teamwork, communication, and others—were the most important, while STEM skills ranked toward the bottom.

So, what exactly are these noncognitive skills? Figure 1 presents a “competency model” produced by the Center for Energy Workforce Development, a nonprofit trade association focused on preparing workers for careers in the electrical utilities sector. It is representative of the types of models many industries use to guide professional development. At the base of the model is Tier 1, labeled “Personal Effectiveness”—a term that captures the broad, general abilities applicable to almost any job.

A common problem in workforce development is that we devote most of our resources, energy, and time toward training in Tier 6 (occupation-specific competencies) while largely ignoring Tier 1 (personal effectiveness). The problem with this approach is that weak Tier 1 skills make Tier 6 skills training much less likely to be effective. Further, a lack of Tier 1 skills is often associated with stunted career advancement. In the vernacular of workforce development, a strong Tier 6 gets you hired, while a weak Tier 1 gets you fired.

Reforming Workforce Programs

The future of work, therefore, is a complex and shifting landscape shaped by the dynamic interaction between technology and skills, which heightens the risk of obsolescence in education and training, and a long-term worker shortage. The resulting uncertainty means that workers need to develop broad, flexible skill sets that will help them adapt to change in real time. Likewise, the workforce system must be equally flexible and dynamic, providing ongoing support for workers seeking to regularly gain and update skills.

Setting the Stage: The Workforce Futures Initiative

In recent years, the Workforce Futures Initiative (WFI)—made up of scholars from the American Enterprise Institute (AEI), the Brookings Institution, and the Malcolm Wiener Center for Social Change at the Harvard Kennedy School—has conducted a review of rigorous workforce program evaluations to identify programs that are successful in helping workers gain skills and advance in employment.17 The key finding is that evidence of what works in workforce development and skills training is remarkably limited. As demonstrated by examinations of various service interventions funded under the federal Workforce Innovation and Opportunity Act (WIOA), outcomes have proved to be modestly positive for low-income adult workers, while no significant positive effects have been found for dislocated workers or youth. Whether this absence of positive evidence is due to program design issues, administrative problems, inadequate funding, or some combination of these factors is still unknown.18

This is not the same as saying nothing works in workforce development. The counterfactual situation—a world in which WIOA doesn’t exist—isn’t available, and there may be positive effects that haven’t been captured through evaluations. One of the most pronounced challenges in measuring workforce development impacts is the inability to track people longitudinally to see what employment and earnings gains may be realized over a longer period.

Sector-Based Training Programs

Among WFI’s most hopeful findings is that well-designed, rigorous sector-based training programs—which combine noncognitive and technical skill training with wraparound support services—have delivered significant improvements in employment and wage progression among lower-skilled and low-wage workers. Replicating and expanding these programs is a key strategy for improving workforce development outcomes.

Rigorous sector-based training programs are characterized by the following:

  • Strong employer partnerships in high-demand, high-pay sectors and an organization that provides a foundational “convening” role to develop comprehensive organizational partnerships while connecting employers and participants
  • A dual focus on job-specific technical training and helping workers attain and express durable “soft” skills in the workplace
  • Prioritization of jobs that stretch participants’ skills and professional capabilities
  • Wraparound support services, such as transportation and stipends for work, to boost persistence and completion

While some questions remain about which worker populations benefit most from sector-based programs, outcomes to date justify public and private investment to help replicate and scale these programs.19 Refocusing spending and prioritizing federal investments in sector-based training should include requirements that grantees adhere to the evidence-based practices associated with proven models.

The WFI report on sector-based training outlines several ways Congress and the US Department of Labor could replicate and scale high-quality sector-based initiatives. These include integrating them with community colleges, providing discretionary grants that require strict adherence to successful practices, and leveraging national evidence- and sector-based training programs like Per Scholas and Year Up to increase training capacity.20

Expanded Individual Training Accounts

In line with reforms that help maximize the flexibility and responsiveness of federal investments in training and education, we also recommend expanding the availability of WIOA’s Individual Training Accounts (ITAs). ITAs are worker-directed resources for upskilling and retraining for in-demand occupations. ITAs have been shown to generate annual earnings gains of $1,500 to $2,000 on average—comparable to the outcomes of WIOA adult programs noted above.21 Because so many public workforce development resources are spent on inefficient and redundant workforce program administration, ITAs are not widely available to workers, imposing limits on their scale and effectiveness. Increasing funding to expand ITA availability, improving the efficiency of the WIOA system generally, and increasing the account values to cover more expensive training (e.g., sector-based programs) might expand ITA access and improve effectiveness.

ITA effectiveness has been measured using three approaches: structured choice, guided choice, and maximum choice. Structured choice is the most stringent model, requiring vocational counseling and counselor approval of the worker’s job training decisions. Guided choice, as the name suggests, involves a less intensive, but still mandatory, counseling approach. Finally, maximum choice relies on voluntary counseling and leaves the selection of training programs in the worker’s hands, with the counselor functioning more as a sounding board than as a caseworker.

A Mathematica study has demonstrated the superior performance of the more flexible maximum-choice model.22 This suggests that many workers can make training choices without extensive case management. For efficiency’s sake, state and local WIOA administrators should be encouraged to triage potential ITA users mainly between structured choice (for individuals with significant barriers to employment) and maximum choice (for most other workers). This would accelerate training, reduce opportunity costs for workers, and minimize the administrative burden for the WIOA system itself. Further reform of ITAs should include transitioning to a Career Advancement Account model, which further expands worker choice by allowing the use of account funds to address key employment barriers (e.g., housing, transportation, and childcare) or relocating to regions with better job opportunities.

System-Level Reforms

As previously mentioned, evidence of positive returns on public investment in workforce development is sparse. Because we lack this evidence, it is crucial to support innovation strategies to develop better data-informed practices and encourage other approaches that broaden access to opportunity.

State Service Delivery

When the nation faced high levels of intergenerational welfare dependency in the 1990s, it turned to the states as “laboratories of democracy” to develop the models for moving welfare recipients to work. We advocate a similar strategy of encouraging experimentation and innovation in the publicly funded workforce system as a precursor to national workforce policy reform.23

Oregon’s workforce and social services system illustrates the challenges many states face when attempting to coordinate federally funded education, training, and human services programs.24 Like most states, Oregon administers WIOA, adult education, vocational rehabilitation, employment services, and Temporary Assistance for Needy Families through multiple agencies and subagencies, each with separate funding streams, reporting requirements, facilities, and case management structures. This “multi-door” arrangement places a substantial burden on agency staff and employers, as well as workers and job seekers, who must navigate a complex array of offices and eligibility rules to access needed assistance. It also increases administrative overhead, requirements of duplicative staff, and infrastructure across siloed programs.

Figure 2. Oregon’s Multi-Door Approach to Workforce Development

Source: Information was derived from federal and state agency websites from July to August 2023, along with WIOA state plans. Brent Parton, “Training and Employment Guidance Letter No. 15-22,” US Department of Labor, Employment and Training Administration, April 21, 2023, https://www.dol.gov/sites/dolgov/files/ETA/advisories/TEGL/2022/15-22/TEGL%2015-22.pdf; US Department of Education, “Funds for State Formula-Allocated and Selected Student Aid Programs,” October 2, 2023, https://www2.ed.gov/about/overview/budget/statetables/24stbystate.pdf; and US Department of Health and Human Services, Administration for Children and Families, FY 2021 Federal TANF & State MOE Financial Data, December 1, 2022, https://www.acf.hhs.gov/sites/default/files/documents/ofa/fy2021_tanf_financial_data_table_20221201.pdf.
Note: “TANF” stands for Temporary Assistance for Needy Families. Dollar amounts reflect WIOA and Wagner-Peyser Employment Service amounts for program year 2023. Adult education and vocational rehabilitation numbers are actual numbers for fiscal year 2022. The TANF number is for fiscal year 2021 and represents the total state allotment. Work, education, and training activities are funded at $1,493,868, and work supports are funded at $3,576,946.

By contrast, Utah’s streamlined and integrated approach demonstrates the potential benefits of consolidation. Using federal waivers in the early 1990s, Utah merged workforce, education, and human services programs into a single state agency—the Department of Workforce Services—creating a “one-door” model that provides unified intake, case management, and service delivery. The integrated system enables individuals and families to receive employment, education, and social supports through one office and one case manager, reducing confusion for users and lowering the cost of administration. Utah’s unique federal fiscal agreement—which allows cost allocation across programs through a single reporting relationship with the US Department of Health and Human Services—further improves efficiency by enabling the state to braid funding streams and align services.

Although Utah’s model was grandfathered after passage of the Workforce Investment Act, no other state has been permitted to implement a similar consolidation. Late in 2024, Congress moved to try to expand state-led innovation only to see that effort fail at the last moment.25 As Congress considers a new effort to reauthorize the federal workforce development system, including this initiative in revised legislation is essential. Evidence-based lessons generated by state-led reform could then inform a broader redesign of the federally funded workforce system.

Public-Private Collaboration to Integrate and Leverage Data

AEI, the Coleridge Initiative at New York University, the Stanford Digital Economy Lab, and the University of Michigan are collaborating to explore and develop new models for understanding how AI and other emerging technologies are likely to affect jobs and skill requirements. The National Science Foundation has funded a three-year, $10 million pilot project to integrate datasets and develop innovative labor market analysis and forecasting tools tailored to local and regional economies as the first step toward a new “headlight” approach to forecasting labor market demand. This enhanced forecasting could guide government, employers, and workers in anticipating skill needs and designing appropriate training and retraining programs for the jobs of the future.

Occupational Licensing Reform

Occupational licenses have increasingly become a requirement of modern American work. In the 2010s, about 25–30 percent of US jobs required an occupational license, up from about 5 percent in the 1950s.26 While these licenses may be useful in some areas, they often come with high costs in terms of fees, regulations, and barriers to competition and market entry. Professions in nursing, medicine, counseling, trades, law, and real estate face these licensing challenges, which are exacerbated by legal and regulatory differences across states and localities.

Occupational licensing reform benefits local economies by broadening labor supply and easing requirements that exclude skilled workers.27 Some clear beneficiaries include those with criminal records, veterans, and military families, who frequently move between states.28

According to the Institute for Justice, 20 states have universal license recognition.29 These licensure approaches offer instant approval through pre-negotiated agreements with state licensing boards once residency verification and background checks are completed.

Such processes already exist for professions like medicine and law, whether through bar wave-ins or compacts. In an economy that is increasingly integrated across jurisdictions, encouraging states to work together to streamline licensing procedures could alleviate shortages around the country using the market forces of supply and demand to improve mobility. Licensing reforms can also facilitate the rising tide of remote work to improve opportunities for every American regardless of where they live.

Figure 3. Utah’s One-Door Approach to Workforce Developmen

Source: Brent Parton, “Training and Employment Guidance Letter No. 15-22,” US Department of Labor, Employment and Training Administration, April 21, 2023, https://www.dol.gov/sites/dolgov/files/ETA/advisories/TEGL/2022/15-22/TEGL%2015-22.pdf; US Department of Education, Office of Finance and Operations, “Funds for State Formula-Allocated and Selected Student Aid Programs,” October 2, 2023, https://www.ed.gov/sites/ed/files/about/overview/budget/statetables/24stbystate.pdf; and US Department of Health and Human Services, Administration for Children and Families, FY 2021 TANF & State MOE Financial Data, December 1, 2021, https://www.acf.hhs.gov/sites/default/files/documents/ofa/fy2021_tanf_financial_data_table_20221201.pdf.
Note: “TANF” stands for Temporary Assistance for Needy Families. Dollar amounts reflect WIOA and Wagner-Peyser Employment Service amounts for program year 2023. Adult education and vocational rehabilitation numbers are actual numbers for fiscal year 2022. The TANF number is for fiscal year 2021 and represents the total state allotment. Work, education, and training activities are funded at $1,493,868, and supports are funded at $3,576,946.

Equal Tax Treatment of Business Investment in Human Capital

After the 1986 tax reform, business investments in human and physical capital received equal tax treatment in terms of credits and depreciation. Over the ensuing decades, however, tax rates on tangible, nonhuman capital business assets, such as industrial plants, physical equipment, and computer software, have fallen to spur productivity-improving investment, while the tax treatment for human capital (e.g., on-the-job education, training, and upskilling) has largely been static.

Congress is considering a variety of approaches to increase tax incentives for human capital development and reskilling. These proposals include creating a refundable tax credit for small- and medium-sized enterprises that provide training programs, using tax incentives to foster partnerships between employers and community colleges, and expanding the deductibility of spending on education and training to cover course materials, technology, and certifications. Such tax code changes would help level the investment playing field, incentivizing businesses to seek the optimal productive balance between people and machines.

The purpose of such changes is not to discourage automation but to improve the neutrality of federal tax policy relating to business decisions between automating and augmenting human labor. This would also support the use of technology to increase human productivity, which has long been the pathway to raising incomes and living standards over time.

Workforce Pell Grants and Education Savings Accounts

Recent federal reforms advance this vision of expanded opportunity by supporting flexible, skills-based education pathways. The 2025 Workforce Pell Grant program will expand Pell Grant eligibility to short-term nondegree workforce programs beginning in 2026. Likewise, changes to Section 529 education savings accounts will allow families to use those funds for recognized postsecondary credentialing programs. Together, these provisions underscore a growing bipartisan commitment to aligning federal education policy with the realities of a rapidly changing labor market.30

Conclusion

The future of the workforce is about much more than marginal adjustments to systems that, at best, have only modest impacts on employment outcomes. Rather, we must reorient the entire system—from top to bottom—toward innovation, flexibility, and adaptation. To thrive in a rapidly changing economy, we must prioritize robust sector-based training, reform occupational licensing, and implement flexible, worker-driven approaches to skill development.

Such a system should emphasize agency, choice, and adaptation at every level—from individual workers to federal and state administrations. Better labor market data and forecasting can help local and regional economies anticipate and adjust to change. Tax reform and other incentives can be used to encourage business investment in human capital, making internal business retraining and re-skilling more practical and economically feasible. Flexibility for states to realign and reconfigure the overlapping array of federally funded education, training, and self-sufficiency programming can help generate the evidence we need, but do not yet have, to replicate successful policies and strategies.

Above all, adaptability must become the primary focus of policymakers and workers. In an era when emerging technologies and labor demands are constantly evolving, supporting workers and program leaders through flexible education and training systems is needed to meet the demands of evolving opportunity. These themes are consistent with the long-standing ethos of the American experiment, in which free people acting on their own vision for their future help drive the unguided miracle of American dynamism, opportunity, and economic growth.

Notes

  1. US Census Bureau, International Database, World, 2024, https://www.census.gov/data-tools/demo/idb/#/dashboard?COUNTRY_YEAR=2024&COUNTRY_YR_ANIM=2024&menu=countryViz&CCODE_SINGLE=**&CCODE=**&popPages=PYRAMID&ageGroup=1Y&POP_YEARS=2024.
  2. United Nations, Department of Economic and Social Affairs, Population Division, World Population Prospects 2022: Summary of Results , 2022, https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf.
  3. Our World in Data, Median Age, https://ourworldindata.org/grapher/median-age?time=1950..2100&country=~OWID_WRL.
  4. US Census Bureau, “U.S. and World Population Clock,” December 23, 2025, https://www.census.gov/popclock/world.
  5. US Census Bureau, “Historical Population Change Data (1910–2020),” April 26, 2021, https://www.census.gov/data/tables/time-series/dec/popchange-data-text.html.
  6. Federal Reserve Bank of St. Louis, Fertility Rate, Total for the United States (SPDYNTFRTINUSA), April 16, 2025, https://fred.stlouisfed.org/series/SPDYNTFRTINUSA.
  7. US Census Bureau, Age of the Population of the United States, by States: 1960, September 8, 1961, https://www.census.gov/library/publications/1961/dec/pc-s1-11.html; and US Census Bureau, “America Is Getting Older,” press release, June 22, 2023, https://www.census.gov/newsroom/press-releases/2023/population-estimates-characteristics.html.
  8. Federal Reserve Bank of St. Louis, Infra-Annual Labor Statistics: Working-Age Population Total; From 15 to 64 Years for United States (LFWA64TTUSM647S), December 15, 2025, https://fred.stlouisfed.org/series/LFWA64TTUSM647S.
  9. Federal Reserve Bank of St. Louis, Labor Force Participation Rate (CIVPART), December 16, 2025, https://fred.stlouisfed.org/series/CIVPART.
  10. Federal Reserve Bank of St. Louis, Infra-Annual Labor Statistics: Labor Force Participation Rate Male; From 25 to 54 Years for United States (LRAC25MAUSM156S), December 15, 2025, https://fred.stlouisfed.org/series/LRAC25MAUSM156S.
  11. Federal Reserve Bank of St. Louis, Infra-Annual Labor Statistics: Working-Age Population Male; From 25 to 54 Years for United States (LFWA25MAUSQ647S), December 15, 2025, https://fred.stlouisfed.org/series/LFWA25MAUSQ647S; and Federal Reserve Bank of St. Louis, Infra-Annual Labor Statistics: Labor Force Participation Rate Male.
  12. Michael Chui et al., The Economic Potential of Generative AI: The Next Productivity Frontier , McKinsey & Company, June 14, 2023, https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-nextproductivityfrontier; and Edward W. Felten et al., “How Will Language Modelers like ChatGPT Affect Occupations and Industries?” (working paper, Social Science Research Network, April 17, 2023), 2023, https://ssrn.com/abstract=4375268.
  13. Tyna Eloundou et al., “GPTs Are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (working paper, Arvix, August 22, 2023), https://arxiv.org/pdf/2303.10130.
  14. Brent Orrell and David Veldran, The Age of Uncertainty—and Opportunity: Work in the Age of AI , American Enterprise Institute, February 29, 2024, https://www.aei.org/research-products/report/the-age-of-uncertainty-and-opportunity-work-in-the-age-of-ai/.
  15. David J. Deming, “The Growing Importance of Social Skills in the Labor Market,” The Quarterly Journal of Economics 132, no. 4 (2017): 1593–640, https://academic.oup.com/qje/article-abstract/132/4/1593/3861633.
  16. Jill Goldstein et al., Augmented Work for an Automated, AI-Driven World: Boost Performance with Human-Machine Partnerships, IBM Institute for Business Values, August 10, 2023, https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/augmented-workforce.
  17. American Enterprise Institute, “The Workforce Futures Initiative,” https://www.aei.org/workforce-futures-initiative/.
  18. Brent Orrell, ed., What’s Working? Perspectives on Key Issues in Workforce Development Programs and Practices, American Enterprise Institute, October 2024, https://www.aei.org/wp-content/uploads/2024/10/Whats-Working-October-2024.pdf.
  19. Recent research by Namrata Narain and Kadeem Noray found that black people and women saw significantly less benefit from these programs. Identifying the sources of these differences and adjusting training and placement strategies accordingly may be useful in reducing disparities. This may be explained by the job sector’s channeling of certain participants into industries that are commonly held by those with similar characteristics (e.g., channeling physically able men into construction and building trades, which often have a higher wage upgrade). See Namrata Narain and Kadeem Noray, “Whose Bridge to Opportunity and Why? Unpacking the Impacts of Sectoral Job Training,” (working paper, November 13, 2023), https://www.dropbox.com/scl/fi/dvcoxvioywj37j8ujsnjl/yu_het_updated.pdf.
  20. Richard Hendra et al., Expanding Economic Opportunities Through Evidence-Based Sector Training , American Enterprise Institute, August 3, 2023, https://www.aei.org/research-products/report/expanding-economic-opportunities-through-evidence-basedsector-training/.
  21. Harry Holzer, “Should the U.S. Spend More on Job Training?,” Forbes, May 15, 2023, https://www.forbes.com/sites/harryholzer/2023/05/14/should-the-us-spend-more-on-job-training/.
  22. Irma Perez-Johnson et al., Improving the Effectiveness of Individual Training Accounts: Long-Term Findings from an Experimental Evaluation of Three Service Delivery Models, Mathematica Policy Research, October 2011, https://www.dol.gov/sites/dolgov/files/ETA/publications/ETAOP_2012_06.pdf.
  23. Such a strategy nearly became federal law with the consideration of H.R. 6655, A Stronger Workforce for America Act (ASWA). ASWA passed the House of Representatives on a large bipartisan basis (378–26) on April 9, 2024, and was referred to the Senate. It nearly passed the Senate as part of the December 2024 appropriations measure to fund the federal government but was removed due to controversies over the size of the legislation and other spending measures. ASWA included a key provision authorizing up to nine states to waive most of WIOA’s Title I requirements and operate statewide demonstration projects, modeled on the waiver of authority used in 1990s welfare reform. Eligibility was limited to states with fewer than five million residents and labor force participation below 60 percent. In parallel with ASWA’s advancement in 2024, Louisiana Governor Jeff Landry launched a comprehensive review of the state’s workforce and social services system. Executive Order JML 24-44 (March 2024) established the Louisiana Workforce and Social Services Reform Task Force, which was expressly charged with identifying opportunities for the state to participate in federally authorized workforce demonstrations and specifying required WIOA waiver requests. The task force’s final report informed subsequent state legislation that reorganized Louisiana’s workforce programs and positioned the state to pursue demonstration authority. The report documented that Louisiana—like many states—administers workforce and social service programs through multiple agencies and siloed funding streams, creating fragmented service delivery, duplicative overhead, and significant access barriers for workers and job seekers. See Louisiana Workforce and Social Services Reform Task Force, Final Report Pursuant to Executive Order JML 24-44, Pelican Institute for Public Policy, https://pelicanpolicy.org/wp-content/uploads/2025/02/Report-LA-WASS-Task-Force.pdf.
  24. Mason M. Bishop, The Utah Model: Workforce Programs and Services Integration Tool Kit, American Enterprise Institute, July 31, 2023, https://www.aei.org/research-products/report/the-utah-model-workforce-programs-and-services-integration-tool-kit/.
  25. A Stronger Workforce for America Act, H.R. 6655, 118th Cong. (2023–24).
  26. National Conference of State Legislatures, “The National Occupational Licensing Database,” August 12, 2022, https://www.ncsl.org/labor-and-employment/the-national-occupational-licensing-database.
  27. Joseph B. Fuller et al., Hidden Workers: Untapped Talent, Harvard Business School and Accenture, September 7, 2021, https://www.aei.org/research-products/report/hidden-workers-untapped-talent/.
  28. Susan Stutzky and Marcus Coffin, Health and Occupational Licensing: Veterans’ Exemptions, Michigan House of Representatives, House Fiscal Agency, https://www.legislature.mi.gov/documents/2021-2022/billanalysis/House/pdf/2021-HLA-0157-E5CC9F85.pdf.
  29. Institute for Justice, “State Reforms for Universal License Recognition,” https://ij.org/legislative-advocacy/states-reformsfor-universal-recognition-of-occupational-licensing/.
  30. One Big Beautiful Bill Act, Pub. L. No. 119-21.