Policy Forum: Better Late Than Never—Policy Options for Modernizing OAS and Retirement Tax Credits
Bibliographic record
Abstract
Canada's retirement income system requires modernization. In 2025, old age security (OAS) will deliver over $18,000 annually to retired couples with household incomes of $182,000, while still leaving almost 400,000 older Canadians below the poverty line. At a cost of $86 billion per year, OAS is the largest federal transfer and the biggest source of new spending growth, driving the operating deficit. Younger Canadians now contribute 20-40 percent more in income taxes toward seniors' healthy retirements than baby boomers once paid, while programs for housing, child care, and post-secondary education receive relatively little new money. Drawing on the reform blueprint advanced by Prime Minister Chrétien in the mid-1990s, a recent auditor general review, and new microsimulation modelling, this article evaluates six policy options to recalibrate OAS recovery thresholds and phase out the age and pension income tax credits. The analysis shows that redirecting benefits from financially secure retirees with six-figure incomes could free up $14 billion to $19 billion annually—enough to eliminate seniors' poverty, expand investments in younger generations, and reduce the deficit without raising taxes. These reforms to retirement income security would be a textbook case of "better late than never" to advance multiple priorities in Prime Minister Carney's mandate letter.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".