A Different Perspective on Retirement Income Sustainability: <i>The Blueprint for a Ruin Contingent Life Annuity (RCLA)</i>
Bibliographic record
Abstract
This article describes a new retirement income insurance product that would help individuals protect against longevity risk and retirement “ruin”in an economically efficient manner. The authors label this product a ruin-contingent life annuity (RCLA), for which they develop various numerical examples and pricing models. They then argue that RCLA products already exist, albeit not on a stand-alone basis. Namely, these RCLA products are embedded within modern variable annuity (VA) policies with guaranteed living income benefit (GLiB) riders. The authors’ analysis thus provides a deeper understanding of the role of VA + GLiB policies in hedging retirement risks. Indeed, the popularity of GLiB riders on VA policies point towards the potential commercial benefit of such stand-alone insurance products. At the very least, the existence of such a market would enable wealth managers to properly value the economic cost of a given retirement spending rate. TOPICS:Retirement, risk management
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".