Two state Medicaid directors receive identical letters from CMS. Both states have expansion populations exceeding 400,000 adults. Both face the December 2026 deadline to implement community engagement requirements under the One Big Beautiful Bill Act. Both must design systems to verify that hundreds of thousands of adults are meeting 80-hour monthly work requirements. Director Chen calls her operations team asking how to confirm that people who are already working get recognized for it. Her team begins pulling unemployment insurance wage data, cross-referencing SNAP employment records, and mapping employer concentrations. They discover 68 percent of expansion adults already show wages in state databases and another 12 percent are receiving disability benefits. They start building systems to match what they already know against what they need to verify.
Director Hargrove calls his compliance team asking how to catch people who are not meeting the requirements. His team begins designing a reporting portal, establishing monthly submission deadlines, and drafting termination notices for members who fail to report. They build the system around the assumption that members must prove their compliance or face consequences. Eighteen months later, Director Chen’s state reports a 6 percent coverage loss rate. Director Hargrove’s state reports 23 percent. Both states have the same federal requirements. Both states have comparable expansion populations. The difference is not policy but paradigm. This is not hypothetical but the difference between what happened in Arkansas in 2018 and what states like Ohio are designing for 2026.
The compliance paradigm begins with a default assumption that beneficiaries are non-compliant until they prove otherwise. This assumption shapes every downstream design decision from system architecture to staffing models to success metrics. Under this paradigm, the burden of proof falls entirely on the individual. A person receiving Medicaid must affirmatively demonstrate, through documentation submitted to the state, that they are meeting work requirements. The system does not look for evidence that the person is working. It waits for the person to provide that evidence. If the person fails to provide it, the system treats them as non-compliant regardless of whether they are actually working.
Documentation becomes the gatekeeper rather than the confirmation. The critical transaction is not whether you are working but whether you can prove you are working. These are profoundly different questions. The first is about behavior. The second is about administrative capacity. A person bagging groceries for 35 hours a week at two different stores answers the first question affirmatively every week. Whether they can answer the second depends on whether their employers provide documentation, whether they can aggregate hours across jobs, whether they have internet access to submit reports, and whether they understand the reporting requirements in the first place.
Systems designed around the compliance paradigm optimize for enforcement, verification, and termination. Administrative convenience takes priority over beneficiary capacity. The system is built for the administrator’s workflow, not the worker’s reality. Monthly reporting portals, narrow submission windows, and automated termination processes reflect what is easiest for the state to administer, not what is most likely to produce accurate results. Success under this paradigm is measured by terminations. When people lose coverage, the system is working. Non-compliance has been identified and consequences have been applied. The question of whether terminated individuals were actually non-compliant or simply unable to navigate the verification process does not factor into the success metric.
Arkansas in 2018 represents the paradigm case. The state implemented online-only reporting through a web portal, required monthly submissions within narrow windows, provided minimal outreach about the new requirements, and automatically terminated coverage for anyone who failed to report. The system was designed to catch non-compliance efficiently. It did not consider whether the non-compliance it was catching was real. Research by Benjamin Sommers and colleagues at Harvard, published in the New England Journal of Medicine, found that 95 percent of those who lost coverage were either working or qualified for exemptions. The system was extraordinarily efficient at terminating people. It was extraordinarily poor at determining whether those terminations were justified.
The recognition paradigm begins with a different default assumption: most beneficiaries are already working or legitimately exempt, and the system’s job is to verify that reality rather than to assume it does not exist. This assumption is not naive optimism but grounded in evidence. Research consistently shows that the vast majority of Medicaid expansion adults who are able to work are already working. Kaiser Family Foundation data indicates that among non-disabled, non-elderly Medicaid expansion adults, roughly 60 percent are employed and another 30 percent have legitimate reasons for not working, including caregiving, disability not yet formally documented, school enrollment, or illness. The genuinely non-compliant population represents a small fraction of the total.
Under the recognition paradigm, the burden of proof falls on the system rather than the individual. The system must demonstrate that it has exhausted available data sources before concluding that someone is non-compliant. Unemployment insurance wage records, state new hire databases, SNAP employment and training records, TANF work participation data, educational enrollment systems, and disability databases all contain information about what expansion adults are doing. A recognition system queries these sources first and only asks individuals to self-report when administrative data cannot confirm compliance. Documentation becomes confirmation rather than barrier. The system starts with what it already knows and asks individuals to fill gaps, not to rebuild the entire picture from scratch.
Systems designed around the recognition paradigm optimize for data matching, multiple verification channels, and retention. Beneficiary capacity takes priority over administrative convenience. The system is built around the worker’s reality, then adapted for administrative needs. Success under this paradigm is measured by accurate classification. The system succeeds when working people are correctly identified as compliant, exempt people are correctly identified as exempt, and genuinely non-compliant people are correctly identified as non-compliant. Coverage loss among compliant individuals represents system failure, not system success.
Ohio’s proposed approach demonstrates recognition architecture in practice. The state plans to use unemployment insurance wage data to automatically verify employment for an estimated 60 to 70 percent of expansion adults before those individuals submit a single document. Social Security data identifies disability exemptions. SNAP and TANF compliance records confirm participation in other work programs. Only the remaining 30 to 40 percent whose circumstances cannot be confirmed through automated channels enter active verification workflows requiring individual action.
The same 80-hour monthly work requirement can produce coverage loss rates ranging from 5 percent to 25 percent depending on which paradigm shapes system design. The policy is identical. The outcomes are radically different. The Arkansas evidence makes the case starkly. Sommers found that 97 percent of those subject to requirements were already compliant through work or exemption eligibility. Yet 25 percent lost coverage. The gap between actual non-compliance, roughly 3 percent, and coverage loss of 25 percent represents system-generated harm. These were not people who refused to work. They were people who could not prove they were working through the specific channels the system demanded.
There is a deep asymmetry in visibility between the two types of errors these systems produce. False negatives, compliant people incorrectly terminated, are invisible in the political landscape. A worker who loses Medicaid because they could not navigate the verification portal does not appear on anyone’s political radar. They become uninsured quietly. False positives, people receiving benefits they should not receive, are politically explosive. A single case of someone gaming the system generates more political attention than ten thousand cases of working people losing coverage for administrative reasons. This asymmetry creates political incentives that favor compliance theater over recognition accuracy.
The policy question has been answered by Congress. The paradigm question remains open. Every state must decide whether to build compliance systems or recognition systems. States choosing recognition will retain coverage for working people, produce accurate classification of their expansion populations, minimize downstream costs from wrongful terminations, and achieve the policy’s stated goals of promoting work while maintaining coverage. States choosing compliance will replicate Arkansas, terminating working people alongside non-working people, generating false negatives at scale, and producing coverage losses that far exceed actual non-compliance rates.