Which populations would see the largest premium increases if premium tax credits revert to pre-ARPA rules?

1. Who the studies say would be hit hardest — a split between low-income pain and the “subsidy cliff” shock

Analysts are split between two compelling narratives. One stream finds people with incomes below 250% of the federal poverty level would see the steepest relative increases, with average annual premiums quadrupling from enhanced-credit levels (e.g., $169 to $919) and sharp rises for 250–400% FPL groups ^{[1] [4]}. The other stream focuses on the returning “subsidy cliff,” projecting very large absolute premium increases for households above 400% FPL who gained eligibility under ARPA — including examples of older couples facing five-figure annual premiums — which would reverse ARPA’s expansion and produce dramatic dollar shocks ^{[5] [6]}. Both findings are factually supported but emphasize different metrics: percentage change versus dollar amounts and coverage loss versus premium burden.

2. Age, race, and geography: who loses coverage versus who pays more

Reports highlight distinct demographic and geographic patterns. One analysis projects higher uninsurance increases among non-Hispanic Black, non-Hispanic White people, and young adults, with up to a 30 percent rise for some groups if enhanced credits expire ^[1]. Rural residents and states that did not expand Medicaid — including Texas and Georgia — are repeatedly flagged as being disproportionately affected, with estimates of sharp increases in the uninsured and marketplace instability in certain Southern and rural states ^{[2] [4]}. The studies therefore present a dual risk: higher premiums for many enrollees and concentrated coverage losses tied to race, age, and state policy choices, which would amplify existing disparities.

3. Concrete examples that drive the headlines — older adults and families at specific incomes

Several reports use illustrative scenarios to show scale. For example, a 60-year-old couple at 402% FPL could face annual premiums near $22,600 in 2026 absent enhanced credits — roughly a quarter of their income versus 8.5% with ARPA-era rules ^[5]. A family of four at roughly 140% FPL that paid $0 in 2025 could see premiums of $1,607 in 2026 under pre-ARPA rules ^[5]. These case studies underscore how age and income thresholds interact with plan rate structures to produce either catastrophic dollar burdens for older middle-income households or recurring affordability declines for lower-income families.

4. Macro estimates and budget implications: coverage drops, premiums, and fiscal notes

Broader modeling ties the subsidy change to large coverage shifts and fiscal effects. One September 2025 estimate projects 4.8 million people losing coverage in 2026 if enhanced credits expire, with substantial premium increases across income bands ^[1]. The Congressional Budget Office report similarly identifies that returning to pre-ARPA rules would remove eligibility for those above 400% FPL and notes budgetary impacts tied to marketplace rules and actuarial-value changes ^[3]. Analysts also estimate that the expiration could raise pre-subsidy premiums modestly (about 5% in some models) while producing significant variation across states, meaning marketwide shocks coexist with localized, severe affordability crises ^{[7] [3]}.

5. Points of disagreement, methodological drivers, and policy implications

Discrepancies across analyses trace to different focal metrics (percentage vs. dollar increases), varying year and scenario assumptions, and whether models emphasize enrollment or premium level effects. Some reports center on who loses coverage and relative affordability impacts for lower-income groups ^{[1] [4]}, while others center on absolute dollar pain for those newly eligible under ARPA and the so-called “subsidy cliff” ^{[5] [6]}. Policymakers weighing options face a clear trade-off: extending enhanced credits prevents coverage losses and reduces relative burdens for low- and middle-income people, while not extending them creates sharp out-of-pocket shocks especially for those just above previous eligibility thresholds. The evidence therefore supports the conclusion that both low-income households and certain middle-to-higher-income older households would be materially harmed, depending on the metric used ^{[1] [5] [3]}.