Strengthening Fairness and Funding in the Canada Pension Plan: Is Raising the Retirement Age an Option?
Bibliographic record
Abstract
This paper seeks to contribute to a forward-looking debate on possible reform options for the Canada Pension Plan (CPP) and the Quebec Pension Plan (QPP). Even though it focuses on the CPP, most of its analysis applies to the QPP as well since the two programs are largely identical. This paper does not provide a broad survey of all possible reform options, but rather analyzes one vital option that has received insufficient attention in previous debates: raising the normal retirement age from 65 to 67 years. A discussion of this option is warranted not only because it could prevent future financing problems in Canada’s public pension insurance programs, but also because it could improve fairness across generations. The significant increase in life expectancy raises the question of whether the current retirement ages of 60 years, for earliest CPP and QPP benefits, and 65 years, for full benefits, are too low. Should future generations pay for the longevity increases of the current generation of workers, or should current workers share the costs by retiring at a later age? We conclude that raising the normal age from 65 to 67 years—and the earliest age from 60 to 62 years—is a financially effective, intergenerationally fair, and politically acceptable option for improving the CPP and for addressing the QPP’s problems. We suggest that the option of raising the retirement age needs to be discussed well before longevity increases or funding problems occur and that a broad consultation with stakeholders and citizens would be an essential part of a debate on raising the retirement age in Canada.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".