Pensions, annuities, and long-term care insurance: On the impact of risk screening
Bibliographic record
Abstract
We examine the interaction between the choice of a retirement vehicle and the purchase of long-term care insurance in a world where agents learn about their longevity and long-term care risk over time. In our setting, and absent any long-term care issues, acquiring a retirement product before learning one’s risk type would be preferred by risk averse agents. When we introduce the possibility of needing longterm care, some agents will prefer to wait until they know their health status (i.e., their risk type) before purchasing a retirement product (a situation akin to having a defined contribution pension plan), whereas others will opt to purchase their retirement product before learning their health status (a situation akin to having a defined benefit pension plan). The preference of one retirement vehicle over the other depends, inter alia, on the level of information asymmetry on the market, on an agent’s risk aversion, and on the probability of needing long-term care and its potential cost. When agents purchase their retirement vehicle after (resp. before) knowing their their health status, then agents will choose a contract that provides them with (resp. less than) full long-term care insurance coverage.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".