Adaptive Human Capital: A Research Study on the Evolution of Workforce Measurement, Credential Validation, and Labor Resilience in an AI-Accelerated Economy - RESEARCH AND PODCAST SERIES 2026

Executive Summary

The transition into an artificial intelligence-driven economy necessitates a paradigm shift in how the United States measures, validates, and funds human capital development. Current workforce systems, largely governed by the Workforce Innovation and Opportunity Act (WIOA), are optimized for a mid-twentieth-century industrial model that prioritizes W-2 employment and enrollment-based throughput.¹ However, emerging empirical evidence indicates that these metrics fail to capture approximately 17.3% of the workforce engaged in self-employment, a sector where income is frequently underreported by as much as 30%.³ Furthermore, while AI accelerates the displacement of routine cognitive and administrative labor, human-touch industries—particularly specialized trades like nail technology and esthetics—show remarkable resilience and accelerating demand.⁵

This research study proposes a “Full-Stack Workforce Development Model” that moves beyond static certificates toward a dynamic “Proof of Work” framework.⁷ By leveraging digital portfolios, verifiable credentials, and AI-assisted audit systems, regional workforce boards such as KentuckianaWorks can broaden their definition of success to include micro-enterprise and independent service labor.⁸ This evolution is not merely a technical requirement but a sociological imperative to restore the dignity of vocational labor and ensure that human-centered work remains the bedrock of economic stability in an age of automation.¹⁰

1. Structural Breakdown of the Enrollment-Based Incentive Model

The architecture of the American workforce development system is fundamentally shaped by the fiscal and regulatory incentives established under the Workforce Innovation and Opportunity Act (WIOA). Since its inception, the system has operated on a performance accountability framework that centers on program enrollment and short-term placement results.¹ While intended to ensure transparency, this model has inadvertently created structural biases that favor volume over the long-term quality of human capital development.¹²

1.1 Enrollment-Driven Funding and Reporting Incentives

The primary mechanism for funding local workforce development areas (LWDAs) is tied to the successful attainment of six core performance indicators.¹ These metrics focus on the percentage of participants in unsubsidized employment during the second and fourth quarters after exiting a program, their median earnings, and their attainment of recognized postsecondary credentials.¹ Because these indicators are tracked through state Unemployment Insurance (UI) wage records, the system is inherently biased toward W-2 employment structures.²

WIOA Core Performance Indicator	Primary Tracking Mechanism	Institutional Incentive
Employment Rate (Q2/Q4)	UI Wage Record Match	Prioritize W-2 job placement over self-employment
Median Earnings (Q2)	UI Wage Record Match	Focus on high-starting-wage corporate roles
Credential Attainment	Program Completion Records	Maximize certificate volume, regardless of market demand
Measurable Skill Gains	Academic/Technical Records	Support short-term, “stackable” certificates
Effectiveness in Serving Employers	Retention with Same Employer	Discourage career mobility or transition to independent work

This structural orientation creates a “throughput bias,” where the success of a regional board is often judged by the number of enrollments rather than the longitudinal economic mobility of its participants.¹⁷ This is exacerbated by the “Work First” service delivery model, which emphasizes rapid entry into any job to satisfy immediate reporting requirements, often at the expense of deeper skill attainment that could provide long-term immunity to automation.¹²

1.2 Divergence Between Enrollment Metrics and Economic Outcomes

The divergence between institutional metrics and actual economic outcomes is most visible in the “completion-employment gap.” A program may report a 90% completion rate for a coding or administrative assistant course, satisfying enrollment-based targets. However, if those graduates enter a market where AI tools have automated 40% of the entry-level tasks, their actual “economic outcome” may be negative—characterized by underemployment or debt exposure.¹⁹

Institutional reporting often fails to distinguish between “placement” and “persistence.” Because WIOA tracking typically ends after the fourth quarter following program exit, the system does not account for the long-term sustainability of the work.¹⁴ This creates a “revolving door” effect where individuals are trained for roles with high turnover rates, allowing boards to report multiple “successful placements” for the same individual over several years, masking a failure in actual human capital accumulation.²

1.3 Distortion Effects on Program Design and Misalignment

The pressure to meet enrollment and W-2 placement targets leads to several documented distortions in workforce policy and program design.

First, there is a distinct bias toward lower-cost career services over higher-cost, intensive training.¹³ Nationwide data on WIOA Adult and Dislocated Worker funds shows that approximately 60% of expenditures support career services (such as resume workshops and job search assistance), while only 40% support actual technical training.¹³ In some states, as much as 80% of funds are diverted away from skill acquisition to satisfy the high volume of participants required by enrollment metrics.¹³

Metric Category	Distortion Effect	Real-World Consequence
Program Design	Focus on short, low-cost “career services”	Under-skilled workforce vulnerable to AI displacement
Completion Rates	Artificial inflation via “easy-win” credentials	Credential inflation and reduced market signal value
Learner Debt	Pursuit of degrees with low ROI but high visibility	Increasing defaults and financial precarity for participants
Workforce Alignment	Training for past-era, W-2 heavy industries	Missed opportunities in micro-enterprise and gig economies

This misalignment is particularly acute in regional economies like Kentuckiana, where the aging workforce and the growth of service-based micro-enterprises require a more nuanced approach to alignment than traditional W-2 metrics provide.⁹ When programs are designed solely to satisfy federal auditors, they often ignore high-growth, non-W-2 sectors such as the specialized beauty industry, which offers strong recurring revenue but poor W-2 visibility.⁵

2. The W-2 Visibility Problem

The reliance on W-2 wage records as the primary data source for measuring labor market health has created a systemic “visibility gap”.³ As the economy evolves toward independent work, platform-based labor, and micro-entrepreneurship, the traditional tools used by workforce boards have become increasingly blind to significant portions of economic activity.³

2.1 Empirical Documentation of Missing Labor Data

Research conducted by the National Bureau of Economic Research (NBER) and the U.S. Census Bureau highlights the scale of this problem. In 2019, approximately 33 million people—representing 17.3% of the U.S. workforce—engaged in some form of self-employment.³ This population is entirely absent from the statistics used to monitor business dynamism and labor market health because they operate as “nonemployers” (businesses without employees).³

Workforce Status (2019)	Percentage of Workers	Longitudinal Stability (Year-over-Year)
Wage and Salary Only	82.7%	90.0%
Self-Employment Only	7.8%	73.5%
Dual-Employed (Wage + Self)	9.5%	High Volatility

The data indicates that self-employment is less stable than wage work, yet it is often a critical transition state for workers moving between industries or those displaced by technology.³ Furthermore, self-employment income is systematically underreported in both tax filings and household surveys. Economists utilizing Engel curve analysis have found that self-employed individuals underreport their income by approximately 30%.⁴ This means that the “median earnings” metrics used by agencies like KentuckianaWorks may be understating the actual economic power of program graduates by nearly one-third.⁴

2.2 Case Study: Beauty Industry Sub-Sectors

The beauty industry provides a compelling case study for the divergence between W-2 data and economic value. Traditional workforce reporting often lumps all personal appearance workers into a single, aggregated “Cosmetology” category.⁵ This categorization masks the distinct economics of specialized trades like Nail Technology and Esthetics, which exhibit different growth curves and service frequencies.²³

2.2.1 Service Frequency as an Economic Signal

In specialized beauty services, the predictability of income is driven by service frequency rather than employer structure. Data from the Louisville Beauty Academy indicates that Nail Technology has the highest repeat rate in the industry, with regular clients returning every two weeks or less for gel, dip, or acrylic maintenance.²³

Beauty Specialty	Average Frequency	Booking Predictability	Economic Profile
Nail Technology	14 Days	Very High	Strong recurring revenue; high self-employment
Esthetics / Skin Care	21–30 Days	High	Membership-based; clinical focus
Eyelash Extensions	14–21 Days	High	Precision trade; high hourly rate
Cosmetology (Hair)	42–56 Days	Moderate	Seasonal/lower frequency; W-2 dominant

Because a nail technician can build a full, recurring client base in less time than a general cosmetologist, the “absence” of W-2 data in this sector does not signify an absence of economic value.²³ Instead, it represents a successful transition into micro-enterprise ownership, which WIOA metrics currently penalize as “unemployment” if a wage match is not found.²

2.2.2 Specialized Trades vs. Aggregated Categories

The Bureau of Labor Statistics (BLS) projects that employment for manicurists and pedicurists will grow by 7% from 2024 to 2034, significantly faster than the average for all occupations.⁵ Approximately 28% of these workers are officially self-employed, though the actual number is likely higher when accounting for “dual-employed” individuals who supplement W-2 work with independent services.³

The stigma associated with “vocational” work often leads policymakers to overlook these specialized trades in favor of “white-collar” training.¹⁰ However, the resilient demand for human-touch grooming and wellness services—driven by rising disposable income and a shift toward holistic beauty—makes these roles more stable than many administrative roles vulnerable to AI.⁶

3. AI Labor Displacement vs. Human-Touch Labor Resilience

The current technological revolution, powered by generative AI and autonomous agents, is unique because it targets “routine cognitive” labor—the very tasks that defined middle-class administrative and professional work for decades.¹⁹

3.1 Vulnerability of Cognitive and Administrative Labor

Analysis from the Brookings Institution and McKinsey indicates that approximately 70% of workers in the top quartile of AI exposure are in high-income, white-collar occupations requiring postsecondary education.²⁷ However, “exposure” is not the only factor; “adaptive capacity” determines the actual risk of displacement.²⁷

Risk Category	Characteristics	Typical Roles
High Exposure / High Adaptive	High pay, liquid savings, transferable skills	Software developers, lawyers, financial managers
High Exposure / Low Adaptive	Low savings, older age, geographic isolation	Clerical staff, administrative assistants, junior auditors
Low Exposure / High Resilience	Physical presence, tactile precision, trust	Beauty techs, healthcare workers, specialized trades

The group most at risk consists of 6.1 million U.S. workers who face high AI exposure but possess low adaptive capacity.²⁷ Interestingly, 86% of this vulnerable group are women, primarily concentrated in administrative and customer service roles.²⁷ This demographic reality creates an urgent need for workforce boards to pivot toward resilient industries that value the “human-touch”.⁶

3.2 The Resilience of Human-Touch Industries

In contrast to white-collar roles, service trades that require physical presence and high-trust human contact are remarkably resistant to AI replacement.²⁰ McKinsey’s Skill Change Index shows that skills related to “assisting and caring” are among the least likely to be automated in the next five years.²⁰

Beauty services, particularly nail technology and esthetics, behave differently than office-based roles because they are:

Physically Bound: The work requires manual dexterity in a three-dimensional, variable environment that current robotics cannot replicate at scale.²⁰
Relational: The value of the service is derived from the “human connection” and the trust between the technician and the client.¹¹
Non-Routine Physical: Every client’s skin, nails, and preferences are different, requiring a level of “real-time situational judgment” that AI lacks.²³

3.3 The Concept of AI-Complementary Human Labor

Rather than viewing AI as a competitor, resilient workforce models must embrace “AI-complementary human labor”.²⁰ This concept suggests that AI will handle the administrative and cognitive overhead of a business, allowing the human worker to focus entirely on the “high-touch” aspect of the service.²⁹ For a self-employed nail technician, AI tools can manage booking, inventory, and marketing, effectively lowering the “skill barrier” to entrepreneurship.⁶

The future economy will be defined by a “partnership” between people and AI agents.²⁰ In this partnership, the human worker provides the “sensory and emotional intelligence,” while the AI provides the “information-processing efficiency”.²⁸ Workforce programs must therefore shift from training workers to be processors to training them to manage AI while providing the services only humans can deliver.

4. Proof-of-Work as the New Credential

As traditional degrees and certificates lose their “signal value” due to the rapid evolution of technology and the ease of credential inflation, a new model of validation is emerging: “Proof of Work”.⁷ In this context, proof of work is not a reference to cryptocurrency but to a verifiable, time-stamped record of demonstrated competence and output.⁷

4.1 Defining Proof of Work in Workforce Terms

Proof of work moves the focus from “what you learned once” to “what you can repeatedly do”.⁷ It consists of four primary components:

Demonstrated Skills: Verification that a specific task was performed to an industry standard.
Time-Stamped Outputs: Records showing when and how often the skill was applied.
Public Artifacts: Verifiable evidence, such as portfolios, photos of work, or code repositories.
Repeatable Performance: Data showing consistent quality over time, rather than a one-time test result.

Credential Type	Validation Mechanism	Signal Duration
Certificate/Diploma	Institutional authority	Permanent (but static)
Licensure	State-regulated exam	Periodic renewal
Digital Portfolio	Public/Social artifacts	Real-time / Dynamic
Digital Badges (OB3)	Cryptographic metadata	Portable / Stackable

4.2 The Shift to Digital Portfolios and Badging

Traditional resumes and certificates are “opaque” to employers; they do not reveal the actual quality of the work.³² Digital badges, such as those offered by Parchment, provide a “skills-based view of achievement” backed by metadata and real-time labor market context.⁷ These badges are OB3-compliant, meaning they are secure, portable, and interoperable across different roles and industries.⁷

For a worker in the beauty or service trades, a digital portfolio—integrated with social media evidence and client reviews—serves as a more powerful credential than a paper license.³³ Future systems will utilize “Learning and Employment Records” (LERs) that aggregate these diverse proofs into a single, verifiable identity.³¹

4.3 Why Performance Validation Must Replace Completion Verification

In an AI-accelerated economy, the half-life of technical skills is shrinking. A person who completed a coding certificate in 2020 may find their skills obsolete by 2026 if they haven’t maintained a proof-of-work trail.¹⁹ Workforce systems must therefore validate “professional mileage”—the continuous application of skill—rather than just the “entry point” of graduation.⁷

This is especially critical for self-employed and micro-enterprise workers who do not have an HR department to “vouch” for them.²⁵ Their “proof of work” exists in their digital footprint—the search queries, purchases, and social interactions that form a “detailed profile” of their professional life.³⁴ AI algorithms are already being developed to “read” these footprints and predict job success more accurately than traditional resumes.³⁴

5. AI-Assisted Audit and Verification Systems

The transition to proof-of-work credentialing requires a commensurate evolution in audit and oversight. Government agencies cannot manually verify millions of digital artifacts; they must deploy AI to “audit the outcomes” at scale.³⁶

5.1 Large-Scale Evaluation and Automated Auditing

Federal agencies are already exploring AI for program verification. The Department of Labor (DOL) has deployed “Occupation Autocoders” to match job titles to SOC codes and “Grant Document Review” AI to identify anomalies and compliance issues in high-volume records.³⁷ These tools demonstrate how AI can be used to “cross-check” claims and verify outcomes without human intervention.³⁶

AI Audit Application	Current Use Case (DOL/GSA)	Workforce Board Potential
Outcome Verification	Automatic transcription of voicemails for claims ³⁷	Automated verification of self-employment revenue
Fraud Detection	Real-time monitoring of financial data ³⁶	Detection of fabricated resumes and deepfake interviews
Compliance Auditing	Grant document anomaly detection ³⁷	Real-time monitoring of licensure and service logs
Skill Assessment	AI-enabled augmented reality for inspector training ³⁷	Remote validation of technical service output

5.2 Social Media and Public Records as Auditable Trails

The “digital footprint” of a worker is becoming a legitimate source of auditable proof. AI evaluation suites, such as the GSA’s USAi platform, provide the infrastructure for agencies to analyze vast datasets and identify strengths and limitations across different workforce systems.³⁸

For regional boards, this means that “proof of work” can be verified by:

Licensing Databases: Real-time cross-referencing with state board records.
Public Work Logs: Analyzing activity on professional booking platforms (e.g., Vagaro, Square) to verify cash flow and client retention.³⁴
Social Media Proof: Using AI to analyze the “professional consistency” of work showcased on platforms like LinkedIn or Instagram.³⁴
Portfolio Platforms: Using identity-centric fraud prevention tools (e.g., Socure) to ensure that the artifacts presented belong to the actual applicant.³⁹

5.3 Ethics, Bias, and Safeguards in AI Verification

While AI offers efficiency, it introduces risks of systemic bias. Algorithms that rely on “digital footprints” may exclude candidates with minimal online presence or those from marginalized communities with limited digital access.³⁴ Furthermore, AI systems can be prone to “hallucinations” or incorrect classifications in niche trades.⁴⁰

To ensure an “interdisciplinary” and ethical approach, verification systems must include:

Transparency and Explainability: It must be clear to the worker how AI is making decisions about their credentials.¹¹
Data Reciprocity: Workers should have control over their personal data and understand how it informs AI systems.¹¹
Human-in-the-Loop: Automated audits should flag anomalies for human review rather than making final, unchallengeable determinations.¹¹

6. Full-Stack Workforce Development Model

To meet the challenges of the AI era, this study proposes a “Full-Stack Workforce Development Model.” This framework moves beyond enrollment-based metrics to focus on the complete lifecycle of human capital development, from entry to sustained economic contribution.

6.1 Phase 1: Entry and Skill Acquisition

The model prioritizes “adult entry” and “re-entry” pathways, recognizing that the AI economy requires constant upskilling.⁹

Targeting Vulnerable Populations: Focus on the 6.1 million high-exposure, low-adaptive workers, particularly those in declining administrative roles.²⁷
Flexible Skill Acquisition: Utilizing “modular” and “stackable” credentials that allow learners to gain immediate, marketable skills (e.g., Nail Technology) while pursuing longer-term education.⁷

6.2 Phase 2: Real-World Work Output and Licensure

Unlike traditional models that treat “graduation” as the end point, the full-stack model integrates real-world output during the training phase.

Licensure as a Baseline: Using state-approved programs as a foundation, but supplementing them with “proof of work” documentation.⁵
Portfolio Building: Every student must generate a digital portfolio of artifacts that can be cryptographically verified.⁷

6.3 Phase 3: Income Generation and Self-Employment

The model explicitly recognizes self-employment as a successful outcome.

Micro-Enterprise Support: Providing training in “business literacy,” including tax management and AI-assisted marketing.²⁶
Supplemental Wage Tracking: Utilizing TEGL 26-16 procedures to capture income data that UI wage matches miss.¹⁶

6.4 Phase 4: Ongoing Proof-of-Work and Accountability

Success is measured by the worker’s ability to maintain a persistent professional identity over time.

Continuous Verification: Moving from a one-time “placement” report to a quarterly “activity” report based on public work logs and portfolios.³⁴
Post-Training Scaffolding: Providing ongoing mentorship and networking opportunities to ensure workers can navigate the “income volatility” of independent work.²⁶

Model Component	Enrollment-Based Metric (Current)	Proof-of-Work Metric (Proposed)
Success Indicator	Program Exit / Placement	Persistent Economic Contribution
Verification	UI Wage Record Match	Digital Portfolio + Supplemental Wage Form
Incentive	Rapid Placement (any job)	Skill Mastery and Micro-Enterprise Growth
Auditor View	Static Snapshot (Q2/Q4)	Continuous, Longitudinal Skill Progression

7. Implications for Regional Workforce Boards

Regional boards, such as KentuckianaWorks, function as the primary executors of federal workforce policy. To survive and thrive in an AI era, these agencies must evolve their reporting and audit strategies to defend non-W-2 outcomes.⁹

7.1 Historical Audit Constraints and Future Resilience

Historical audits have frequently “questioned” the costs associated with local boards when participant data was found to be “inaccurate” or “unsubstantiated”.¹⁸ In one instance, KentuckianaWorks was found to have over-reported participant wage gains, leading to recommendations for cost recovery.¹⁸ To prevent such findings in the future, boards must adopt “empirically defensible” methods for documenting non-W-2 income.¹⁸

7.2 Defending Non-W-2 Outcomes During Audits

Agencies will increasingly need to rely on “supplemental wage information” as defined in TEGL 26-16.¹⁶ This guidance allows boards to use non-UI sources to verify employment, provided they meet specific documentation standards.¹⁵

Self-Employment Verification Forms: Participants sign a statement certifying their earnings, which is then corroborated by business licenses or tax records.²¹
Alternate Contacts: Using social media, text messaging, and secondary contacts to track participants who “disappear” from W-2 databases but remain economically active.²¹
Calculation of Earnings: For self-employed individuals, net profit (gross receipts minus expenses) is the accepted metric for “median earnings”.²¹

7.3 Policy-Safe Language for Recognizing Micro-Enterprise

Workforce boards should adopt standardized, “auditor-ready” language to describe non-traditional outcomes:

“The participant has achieved sustainable economic contribution through a licensed micro-enterprise (NAICS Code 812113). Outcome verified via Supplemental Wage Self-Employment Verification (TEGL 26-16) and corroborated by an active state professional license and digital proof-of-work artifacts.”

This language shifts the burden of proof from a “missing” UI match to a “present” set of verifiable artifacts.¹⁶ Agencies must also clarify what they cannot do: they cannot ignore federal reporting requirements, but they can use “negotiated performance levels” to account for the unique economic characteristics of their region.¹²

8. Measurement Frameworks for the Future

To move beyond speculative theory, this study proposes implementable, non-W-2 metrics that workforce boards can begin integrating into their local plans for 2025–2028.⁴⁵

8.1 Implementable Proof-of-Work Metrics

Agencies should track “Real-Time Public Records” rather than just “Certificates of Completion.”

Licensure + Documented Service Output: Cross-referencing active state professional licenses with a verified “service count” from digital booking platforms.
Verified Client Work Histories: Measuring the growth and retention of a client base over a 24-month period, providing a signal of “market-validated skill”.²³
Time-Based Skill Progression: Tracking the evolution of a worker’s portfolio (e.g., from “Basic Manicure” to “Advanced Eyelash Extensions”) as a proxy for “human capital accumulation”.⁶
Income Stability Indicators: Measuring the frequency and consistency of cash flow rather than just total annual earnings, to assess a worker’s resilience to economic shocks.²⁶
Public Proof-of-Work Continuity: A metric that awards points for the regular update of digital portfolios and active participation in industry-standard verification systems.³⁴

8.2 Using “WORKS” and Data Interchange Systems

Kentucky’s DWD utilizes the “WORKS” (Workforce Online Reporting for Kentucky System) for fiscal reporting.⁸ Future iterations of this system must be “interoperable” with digital badging platforms (like Parchment) and state licensing databases.⁷ By creating a “Single Persistent Identity Anchor” (SocureID), the system can connect every phone, address, and behavior into a single, auditable profile.³⁰

9. Ethics, Humanization, and Dignity of Work

The final component of this study addresses the “Subjective Dimension of Work”—the idea that work is not just an economic activity but a source of human dignity.¹⁰

9.1 Addressing Stigma in Service Labor

There is a long-standing academic and policy bias against vocational labor, often viewing it as a “last resort” for those who cannot succeed in white-collar environments.¹⁰ However, in an AI era, the “dignity of work” is most clearly preserved in roles that require a human to be the “indispensable subject” of the labor.¹⁰ Beauty services and specialized trades are not just “jobs”; they are “vocations” that allow for personal autonomy and creative expression.¹⁰

9.2 Human-Centered Work in an AI Era

As technology creates a “lonely and isolating experience” in many digital workplaces, human-touch industries provide the “joy of human connection”.¹¹ The ethics of future workforce development must prioritize:

Human Involvement: Ensuring that “absolute technological control” does not replace human agency in the workplace.¹¹
Dignified Transitions: Moving beyond simple “reskilling” to provide workers with the “essential scaffolding” (mentorship, networks, and portables benefits) they need to transition between roles with their dignity intact.²⁶
Worker Voice: Ensuring that the concerns of the self-employed and gig workers are at the heart of AI policy development.¹¹

9.3 Conclusion: Towards a Humanized Workforce Framework

The workforce development system is at a “crucial moment” in the technological revolution.¹¹ By evolving our measurement frameworks to recognize “proof of work” and the resilience of “human-touch” labor, we can build a more inclusive and empirically sound economy. This research study serves as a “strategic knowledge gift”—a framework for regional boards to defend the economic value of their constituents and a policy-safe foundation for the next generation of human capital investment. The dignity of work is not found in a W-2 form; it is found in the repeatable performance of skills that satisfy human needs and inspire hope.¹⁰

Appendix: Definitions and Frameworks

Digital Badge (OB3): A cryptographically secure, metadata-rich record of a specific skill or achievement that is portable and verifiable across different software platforms.⁷

Human-Touch Resilience: The economic phenomenon where jobs requiring physical presence, tactile precision, and interpersonal trust show higher resistance to AI automation than routine cognitive tasks.²⁰

Learning and Employment Record (LER): A holistic, digital record of an individual’s skills, credentials, and work history, verified through various institutional and public artifacts.³¹

Micro-Enterprise (Nonemployer): A business without employees where the owner is the primary worker. These are often missing from W-2 datasets but represent a significant portion of the specialized service economy.³

Supplemental Wage Information: Methods of verifying income (e.g., tax records, self-attestation, social media footprints) used when traditional UI wage match data is unavailable.²¹

WIOA Performance Indicators: The six core metrics used by the federal government to assess the effectiveness of state and local workforce programs.¹

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