Equitable Longevity Risk Sharing or, the raison d'être for a First Nations Pension Plan
Bibliographic record
Abstract
We investigate the extent to which groups with elevated mortality rates ex ante might opt out of guaranteed national pensions in favour of demographically aligned plans, which we label equitable longevity risk sharing (ELRiS) pools, even if this involves accepting some idiosyncratic risk. Technically, this paper develops a stochastic model of retirement income within an ELRiS structure that is calibrated to equate the discounted expected utility of a guaranteed national pension. The practical motivation for developing this alternative is that working members of First Nations peoples of Canada: (1) experience much higher mortality rates than average over their entire life cycle, and (2) some are actually allowed by current legislation to opt out of the Canada Pension Plan (CPP). We then demonstrate that under reasonable economic preferences and parameters, a sub-group with a 10-year life expectancy gap relative to the population could attain equivalent lifetime utility by contributing a mere two-thirds to the plan, even if they were pooled with only 30 members. For a longevity gap of 20 years, such as between an Indigenous male versus a non-Indigenous female, the contribution rate falls to less than a third. The difference between the statutory and mandatory contribution rates to a guaranteed national pension and those needed within these self-sustaining pools is an implicit subsidy from Indigenous to non-Indigenous. From a policy perspective, this paper aspires to jump-start a conversation that sparks a change in a status quo, which is obviously unfair and inequitable.
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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 | 0.004 | 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".