Why Some Nonelderly Adult Medicaid Enrollees Appear Ineligible Based on Their Annual Income
Bibliographic record
Abstract
CONTEXT: Recent studies have highlighted Medicaid enrollment among middle- and higher-income populations and questioned whether the program is reaching those for whom it is intended. METHODS: The authors use administrative tax data to measure Medicaid enrollment and income in 2017, they use survey data to measure monthly income, and they use administrative data to identify Medicaid enrollment pathways. FINDINGS: Among 38.8 million nonelderly adults in Medicaid at any point in 2017, 24.4 million had annual income below their state's typical eligibility threshold, and 14.4 million (37%) had income above the threshold. Among those above the threshold, 3.5 million enrolled through a pathway allowing higher income (pregnant women, the "medically needy," and others). The authors also estimate that more than 12 million had at least one month with income below the threshold, and roughly 4 million had at least five months with income below the eligibility threshold. CONCLUSIONS: Pathways allowing higher income account for one quarter of enrollees with annual incomes above typical thresholds. Among low-income adults, month-to-month variation in income is common and can account for most or all of the remaining enrollees with annual incomes above typical thresholds. A complete accounting of eligibility status would require merged data on income, Medicaid enrollment, and family structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".