An Economic Analysis of Proposals to Improve Coverage of Longevity Risk
Bibliographic record
Abstract
We use simulation methods to analyze the impacts of certain proposed reforms to improve the coverage of longevity risk. This risk, which may in principle be adequately covered by classic defined-benefit pension plans, has been of particular interest in Quebec for some years now, notably due to the decline in the participation to such plans. Recent proposals which aim to increase the coverage of longevity risk mostly deal with expansion of the “2nd pillar" of the retirement income system, currently comprised of the Quebec Pension Plan. We therefore consider a key proposal of the D’Amours committee (the longevity pension), in addition to two other proposals: that of Mintz and Wilson, which aims to increase the generosity of the current regime, and that of Wolfson, which introduces a concept of contribution and benefit rates differentiated by income. Using data from Statistics Canada surveys, we analyze the internal rate of return (IRR) of these proposals for various types of individuals taking into consideration inequality in life expectancy, temporal variability of income, and interactions with taxation and the different retirement income support programs. We contrast the results with those obtained when opting instead for additional contributions into existing voluntary savings vehicles combined with a basic annuity purchased at retirement.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".