South Dakota expanded Medicaid, so the federal work requirement that takes effect January 1, 2027 applies to its expansion adults. About 27,916 of them (5.5% of an expansion pool of 29,843) will have to prove 80 hours a month of work, school, or service, or an exemption, to keep coverage.
This model's bottom-up estimate is that 13,611 people in South Dakota lose coverage by 2034: 12,773 of them still eligible but lost to paperwork and reporting failures, and 837 not meeting the requirement. Nationally the same model gives 5,461,400, against CBO's 5,200,000.
Whether those eligible people keep coverage depends on the state's ability to renew them automatically (ex parte) from data it already holds. South Dakota scores 50 of 100 on this model's ex parte capability index, a mid band; its eligibility system is MMIS-SD.
Key figures
| Measure | South Dakota |
|---|---|
| Expansion state | Yes |
| Subject to the requirement via waiver | No |
| Expansion pool (adults 19 to 64) | 29,843 |
| Adults subject, strict definition | 27,916 |
| Adults subject, permissive definition | 27,358 |
| Share of expansion pool subject | 5.5% |
| Projected coverage loss by 2034 | 13,611 |
| Hardship exception status | No qualifying counties |
| Early implementer | No |
| Ex parte capability score (0 to 100) | 50 (mid) |
Who is subject in South Dakota
Of the 27,916 subject adults, 31% already work 80 or more hours a month and 58% are not working. 37% have a child under 14 at home. Among those not working, 59% report a disability and 19% are caregivers.
| Characteristic | Share of subject adults |
|---|---|
| Age 19-24 | 17% |
| Age 25-34 | 16% |
| Age 35-44 | 24% |
| Age 45-54 | 15% |
| Age 55-64 | 28% |
| Not working | 58% |
| 1 to 19 hours a month | 2% |
| 20 to 79 hours a month | 9% |
| 80 or more hours a month (meets the bar) | 31% |
| Not working: Disabled | 59% |
| Not working: Caregiver | 19% |
| Not working: Postpartum | 1% |
| Not working: Student | 2% |
| Not working: Other | 19% |
Why eligible people lose coverage
Of the 13,611 projected to lose coverage in South Dakota, 12,773 meet the requirement or qualify for an exemption but fail to document it. 2,356 of those are working people who cannot prove their hours through data the state can see, and 10,417 qualify for an exemption the state does not detect automatically.
| Group | Projected to lose coverage |
|---|---|
| Multiple part-time jobs (aggregation failure) | 873 |
| Variable shifts / on-call work | 529 |
| Self-employed (other) | 283 |
| Seasonal: agriculture, hospitality, food service | 210 |
| Full-time students not auto-verified | 171 |
| Volunteering / job training | 170 |
| Gig / courier (DoorDash, Uber, Lyft, Instacart) | 80 |
| Cash-paid construction & trades | 40 |
| Medically frail without auto-match | 4,122 |
| American Indian / Alaska Native (tribal exemption) | 2,566 |
| Parent caregivers of children ≤13 without auto-match | 1,172 |
| Caregivers of a disabled adult | 1,044 |
| Kinship caregivers (non-parent) of children ≤13 | 566 |
| SUD treatment not flagged in claims data | 487 |
| Recent incarceration (data-match gap) | 302 |
| Other categorical exemptions (foster, AYA cancer, SNAP/TANF) | 144 |
| Pregnancy / postpartum data lag | 15 |
Can the state renew people automatically?
The ex parte capability index blends the state's observed automatic-renewal rate (47% in CMS eligibility-processing data), the wage, disability, and other data sources it can query, and its history of churn. South Dakota's composite score is 50 (mid band). A low score means more eligible people will be dropped for paperwork unless the state changes its process before January 1, 2027.
| Component | Score |
|---|---|
| Composite | 50 |
| Observed ex parte | 47 |
| Core capability | 62 |
| Data sources | 55 |
| Historical churn | 50 |
How subjects overlap across exemption categories
| Combination | Adults | Share |
|---|---|---|
| None of the four | 9,229 | 33.1% |
| Medically frail | 6,885 | 24.7% |
| Working ≥ qualifying hours / income | 3,737 | 13.4% |
| Working ≥ qualifying hours / income + Parent caregiver of child ≤13 | 3,362 | 12.0% |
| Parent caregiver of child ≤13 | 2,022 | 7.2% |
| Full-time student | 741 | 2.7% |
| Working ≥ qualifying hours / income + Full-time student | 695 | 2.5% |
| Working ≥ qualifying hours / income + Medically frail | 568 | 2.0% |
Counties in South Dakota
Subject adults concentrate: Minnehaha County (5,651), Pennington County (3,550), and Lincoln County (1,952) account for 40% of the state's subject population.
| County | Adults subject | Subject rate | Projected loss | Poverty rate | Working-age population |
|---|---|---|---|---|---|
| Minnehaha County | 5,651 | 4.7% | 2,755 | 9.0% | 121,266 |
| Pennington County | 3,550 | 5.5% | 1,731 | 12.0% | 64,327 |
| Lincoln County | 1,952 | 4.8% | 952 | 6.0% | 40,816 |
| Brookings County | 1,357 | 6.3% | 662 | 12.0% | 21,708 |
| Brown County | 1,177 | 5.4% | 574 | 11.0% | 21,708 |
| Lawrence County | 957 | 6.2% | 467 | 11.0% | 15,476 |
| Codington County | 926 | 5.7% | 451 | 11.0% | 16,134 |
| Meade County | 859 | 4.6% | 419 | 7.0% | 18,588 |
| Yankton County | 742 | 5.6% | 362 | 8.0% | 13,158 |
| Davison County | 619 | 5.7% | 302 | 15.0% | 10,827 |
| Clay County | 610 | 6.3% | 298 | 21.0% | 9,694 |
| Beadle County | 603 | 5.9% | 294 | 10.0% | 10,146 |
| Union County | 528 | 5.6% | 258 | 8.0% | 9,483 |
| Hughes County | 510 | 5.1% | 249 | 10.0% | 10,086 |
| Lake County | 418 | 7.1% | 204 | 10.0% | 5,920 |
| Roberts County | 344 | 6.8% | 168 | 21.0% | 5,080 |
| Butte County | 342 | 5.9% | 167 | 8.0% | 5,782 |
| Oglala Lakota County | 328 | 4.4% | 160 | 56.0% | 7,448 |
| Custer County | 325 | 7.0% | 158 | 8.0% | 4,660 |
| Charles Mix County | 297 | 6.5% | 145 | 23.0% | 4,540 |
| Turner County | 277 | 5.8% | 135 | 9.0% | 4,739 |
| Grant County | 268 | 6.7% | 131 | 10.0% | 4,030 |
| Todd County | 247 | 5.2% | 121 | 52.0% | 4,754 |
| Fall River County | 246 | 6.7% | 120 | 19.0% | 3,661 |
| Hutchinson County | 226 | 6.0% | 110 | 10.0% | 3,776 |
| Spink County | 214 | 6.5% | 105 | 11.0% | 3,321 |
| Moody County | 207 | 6.2% | 101 | 10.0% | 3,348 |
| Bon Homme County | 193 | 4.6% | 94 | 12.0% | 4,178 |
| Hamlin County | 191 | 5.9% | 93 | 7.0% | 3,261 |
| Kingsbury County | 179 | 6.7% | 87 | 9.0% | 2,670 |
| Day County | 179 | 6.6% | 87 | 15.0% | 2,696 |
| McCook County | 176 | 5.9% | 86 | 8.0% | 2,974 |
| Brule County | 169 | 6.0% | 83 | 14.0% | 2,826 |
| Tripp County | 166 | 5.8% | 81 | 22.0% | 2,867 |
| Marshall County | 152 | 6.9% | 74 | 8.0% | 2,198 |
| Dewey County | 145 | 5.5% | 71 | 31.0% | 2,613 |
| Walworth County | 145 | 5.2% | 71 | 13.0% | 2,795 |
| Deuel County | 143 | 6.3% | 70 | 6.0% | 2,254 |
| Gregory County | 130 | 6.8% | 63 | 13.0% | 1,919 |
| Clark County | 126 | 7.0% | 62 | 9.0% | 1,814 |
| Edmunds County | 121 | 5.7% | 59 | 7.0% | 2,143 |
| Lyman County | 120 | 6.1% | 59 | 25.0% | 1,966 |
| Hanson County | 117 | 6.3% | 57 | 6.0% | 1,856 |
| Corson County | 111 | 5.9% | 54 | 43.0% | 1,890 |
| Bennett County | 110 | 6.8% | 53 | 30.0% | 1,615 |
| Hand County | 107 | 6.8% | 52 | 8.0% | 1,573 |
| Stanley County | 105 | 6.5% | 51 | 3.0% | 1,622 |
| Perkins County | 97 | 6.5% | 48 | 12.0% | 1,498 |
| Douglas County | 94 | 6.8% | 46 | 15.0% | 1,390 |
| Aurora County | 82 | 5.8% | 40 | 5.0% | 1,416 |
| McPherson County | 82 | 8.1% | 40 | 13.0% | 1,012 |
| Sanborn County | 80 | 6.3% | 39 | 9.0% | 1,258 |
| Miner County | 79 | 6.7% | 38 | 11.0% | 1,178 |
| Potter County | 75 | 6.7% | 37 | 7.0% | 1,134 |
| Jackson County | 73 | 4.8% | 36 | 38.0% | 1,519 |
| Ziebach County | 72 | 4.9% | 35 | 41.0% | 1,470 |
| Faulk County | 68 | 6.4% | 33 | 24.0% | 1,062 |
| Buffalo County | 64 | 7.1% | 31 | 37.0% | 894 |
| Jerauld County | 59 | 7.2% | 29 | 9.0% | 818 |
| Haakon County | 58 | 6.7% | 28 | 11.0% | 856 |
| Sully County | 58 | 7.0% | 28 | 8.0% | 824 |
| Campbell County | 50 | 6.0% | 24 | 6.0% | 832 |
| Mellette County | 49 | 4.6% | 24 | 49.0% | 1,052 |
| Hyde County | 42 | 6.7% | 21 | 7.0% | 632 |
| Harding County | 41 | 6.5% | 20 | 6.0% | 632 |
| Jones County | 28 | 6.6% | 14 | 15.0% | 424 |
State brief and methodology
Estimates, not counts. The model rakes to CBO's national subject total and allocates by ACS microdata; state agencies hold the administrative data that would replace these figures.