Retirement Income Security and Well-Being in
Bibliographic record
Abstract
Expenditures on income security programs for seniors in Canada are projected to increase substantially over the next decades. For example, expenditures on the primary transfer programs for seniors, which totaled over $23 billion in 1999/2000, are projected to rise from $25 billion in 2001 to $109 billion by 2030, or from 2.3 percent of GDP to 3.2 percent (Office of the Superintendent of Financial Institutions 2002a). Benefit expenditures in the largest of the two public pension plans, which totaled over $20 billion in 2000/01 are projected to rise to $74 billion in 2025 (Office of the Superintendent of Financial Institutions 2002b). While these expenditures are growing, Canada is in a better position than other developed countries. The main earnings related pensions have moved in the direction of pre-funding and current contribution rates are projected to be actuarially stable in the future.1 The pure pay-as-you-go component of the system funded out of general tax revenue is relatively small. Finally, population growth, while diminished relative to earlier decades, is still projected to be larger than in Europe or Japan.2 Still, as the country’s population ages, stresses on the public finances may extend to seniors ’ pension benefits. There are a variety of solutions possible to this long-term problem. Some of them involve reducing, in one way or another, the benefits available to retirees in Canada. For example, the government could cut the OAS amount, change the translation of past earnings into CPP/QPP benefits, or raise program entitlement ages. As discussed in Baker, Gruber and Milligan (2003, 2004), these changes could have significant impacts on both retirement behavior and program finances. For example, we find that raising the age of eligibility for 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".