Cross‐Subsidization in Nursing Homes: Explaining Rate Differentials Among Payer Types
Why this work is in the frame
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Bibliographic record
Abstract
Are Medicaid patients being subsidized by other residents in nursing homes? This article employs cross‐sectional data on nursing homes and residents in a multiproduct empirical cost analysis to obtain the benchmark magnitudes of patient service costs needed to assess the issues. The estimated cost function provides evidence that Medicaid reimbursement rates are lower than the average incremental cost of care for Medicaid patients in approximately one quarter to one third of Florida nursing homes. One possible explanation for this apparent cross‐subsidization, considered here, is that patients pay a premium in self‐insured rates early in their residency to fairly cover the expected future losses if they later convert to Medicaid. Based on the empirical frequencies of patient transitions to different payer status over the length of the nursing home stay, it is shown that the apparent cross‐subsidization is explained, to a large extent, by an intertemporal conversion surcharge.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 it