Opting out and topping up reconsidered: Informal care under uncertain altruism
Bibliographic record
Abstract
Abstract We study the design of public long‐term care (LTC) insurance when the altruism of informal caregivers is uncertain. We consider non‐linear policies where the LTC transfer depends on the level of informal care, which is assumed to be observable, while children's altruism is not. Our policy encompasses two policies traditionally considered in the literature: topping up policies consisting of a transfer independent of informal care, and opting out policies entailing a positive transfer only if children fail to provide care. We show that both total and informal care should increase with the children's level of altruism. This is obtained under full and asymmetric information. Public LTC transfers, on the other hand, may be non‐monotonic. Under asymmetric information, public LTC transfers are lower than their full information level for the parents whose children are the least altruistic, while it is distorted upward for the highest level of altruism. This is explained by the need to provide incentives to highly altruistic children. In contrast to both topping up and opting out policies, the implementing contract is always such that social care increases with informal care.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".