Old, Frail, and Uninsured: Accounting for Puzzles in the U.S. Long-Term Care Insurance Market
Bibliographic record
Abstract
Half of U.S. 50-year-olds will experience a nursing home (NH) stay before they die, and a sizeable fraction will incur out-of-pocket expenses in excess of $200,000. Given the extent of NH risk, it is surprising that only about 10 percent of individuals over age 62 have private long-term care insurance (LTCI). This market also has a number of other puzzling features. Many applicants are denied coverage by insurers. Coverage of those who have insurance is incomplete. Insurance premia are high relative to an actuarily fair benchmark. Using a model that features agents with private information about their NH entry risk and an insurer who optimally chooses menus of LTCI contracts subject to participation and incentive compatibility constraints, this paper shows that these puzzles can be attributed to adverse selection, overhead costs on the insurer, and Medicaid. The model also accounts for the lack of correlation between NH entry and LTCI ownership. This final property is novel because our setup has only one dimension of private information.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".