Swimming with Wealthy Sharks: Longevity, Volatility and the Value of\n Risk Pooling
Bibliographic record
Abstract
Who {\\em values} life annuities more? Is it the healthy retiree who expects\nto live long and might become a centenarian, or is the unhealthy retiree with a\nshort life expectancy more likely to appreciate the pooling of longevity risk?\nWhat if the unhealthy retiree is pooled with someone who is much healthier and\nthus forced to pay an implicit loading? To answer these and related questions\nthis paper examines the empirical conditions under which retirees benefit (or\nmay not) from longevity risk pooling by linking the {\\em economics} of annuity\nequivalent wealth (AEW) to {\\em actuarially} models of aging. I focus attention\non the {\\em Compensation Law of Mortality} which implies that individuals with\nhigher relative mortality (e.g. lower income) age more slowly and experience\ngreater longevity uncertainty. Ergo, they place higher utility value on the\nannuity. The impetus for this research today is the increasing evidence on the\ngrowing disparity in longevity expectations between rich and poor.\n
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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 | 0.001 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".