A Different perspective on retirement income sustainability: Introducing the ruin-contingent life annuity
Bibliographic record
Abstract
The raison d’être of the variable annuity (VA) industry – now with over $1.4 trillion in assets and over $160 billion in annual sales2-- has shifted from tax deferral and death benefits to income riders. The large majority of VA sales – a.k.a Segregated funds in Canada-- now include guaranteed living benefit riders, which anecdotally have become central to the sales pitch, allure and due diligence process. To many, they are viewed as an ideal private sector replacement for defined benefit (DB) pensions, in an increasingly defined contribution (DC) world. Motivated by the popularity of these instruments – and the fundamental fear they appear to help address-- in this article we pose the question; why must retirees buy this type of insurance with their investments? Would it not be possible to purchase the same insurance for their investments and keep the insurance coverage distinct from the money management processes? This might sound confusing at first – and academic at worst-- but it truly gets to the heart of the retirement income dilemma. Currently, the VA “story ” or bundle contains two distinct parts. The first component is the promise of a series of cash flows-- which is essentially a reverse dollar cost averaging strategy--
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".