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How many people will lose Medicaid coverage due to the new bill's work requirements?
Executive summary
The available analyses converge on a clear split: tens of millions are “at risk” of coverage loss under broad work-requirement proposals, while independent modeling and federal scoring put the expected actual coverage losses in the single-digit millions. Estimates range from a February 2025 projection that 36 million Medicaid enrollees could be at risk of losing coverage to multiple studies and budget scoring that forecast roughly 4.6–5.3 million people would lose Medicaid because of reporting burdens and administrative churn [1] [2] [3] [4] [5]. These differences reflect varying definitions — “at risk” versus modeled “losses” — and diverging assumptions about state implementation, automatic data verification, and exemptions.
1. Why the numbers diverge so dramatically — risk versus modeled losses that matter now
Analysts use two different yardsticks: one tallies everyone who could possibly face difficulty under a sweeping set of proposals, and the other models who would actually lose coverage given specific program rules and state practices. The high-end “at risk” figure of 36 million treats a wide universe of enrollees as vulnerable to administrative barriers and complex reporting, counting anyone who might be affected in any state scenario [1]. By contrast, the more conservative, model-based loss projections — roughly 4.6 to 5.3 million — are grounded in empirical experience from states that tried work requirements, CBO scoring, and simulation studies that factor in potential exemptions, automatic verification, and partial state compliance [2] [3] [4] [5] [6]. The methodologies differ in scope: “at risk” is a maximum-exposure metric, while the modeled losses reflect operational realities and historical error rates.
2. What the federal scorers and empirical studies say — the single-digit millions
Budget and policy modeling consistently lands in the 4.6–5.3 million range for people who would actually lose Medicaid coverage, with timing concentrated in the first years after implementation. The CBO and related analyses project millions becoming uninsured and large federal spending reductions tied to coverage churn rather than eligibility changes, and they flag the administrative burden on states to recode systems and verify compliance [2] [5]. Several peer studies using state-level experience from Arkansas and New Hampshire replicate similar magnitudes of loss when reporting is manual or states lack strong data-matching systems; these studies stress that most of those losing coverage are often working, exempt, or eligible for automatic verification but fail to report [3] [4]. The modeled losses thus reflect implementation frictions, not a mass increase in ineligible people.
3. The cautionary evidence from states that tried work rules — how reporting breaks coverage
State-level experience demonstrates that work requirements primarily harm people through paperwork and system failures rather than by removing coverage from people who clearly fail to meet legal standards. Analyses citing Arkansas and New Hampshire show substantial removal from rolls when reporting was manual or systems were ill-prepared, with many who lost coverage subsequently shown to be working or eligible for exemptions [1]. Those case studies underpin projections that administrative complexity and red tape are the dominant drivers of coverage loss, not changes in underlying health or employment status. Researchers and budget scorers therefore treat administrative capacity — automated verification, outreach, systems upgrades — as the key variable that can shrink modeled losses or let them balloon toward the “at risk” universe.
4. Economic and public-health ripple effects behind the headline numbers
Beyond counts of people losing coverage, analyses model large fiscal and economic consequences tied to coverage losses: multiyear federal spending reductions in the hundreds of billions, state GDP and job losses, and revenue shocks to community health centers that serve many Medicaid patients [6] [7]. Studies project revenue hits for safety-net providers and estimate that manual-reporting populations could see coverage loss rates far higher than average, amplifying local health-system strain [7] [6]. The primary mechanism is churn and service interruption: even temporary gaps in Medicaid can reduce access to care, worsen health outcomes, and increase uncompensated care costs, which then feed back into broader economic measures.
5. What to watch next — implementation choices that determine whether millions lose access
The ultimate scale of coverage loss depends on concrete policy and operational choices: whether work requirements are limited to expansion populations, whether states use data-matching to verify work or exemptions automatically, the frequency and format of reporting, and the resources devoted to outreach and system upgrades [3] [4] [5]. If states implement strong automatic verification and generous exemption recognition, modeled losses could fall toward the lower end of estimates; if states rely on manual reporting and compressed implementation timetables, losses could approach the higher modeled figures or produce widespread churn among the 36 million categorized as “at risk” [1]. The empirical lesson is unambiguous: implementation — not the statutory text alone — will determine whether work requirements translate into millions losing Medicaid.