Measuring and reporting the actuarial obligations of the Canada Pension Plan
Bibliographic record
Abstract
Abstract The processes used to assess the financial sustainability of the Canada Pension Plan (CPP) and the corresponding reporting are recognized internationally as “best practices”. In the context of the international and multi‐disciplinary debate about the most appropriate methodology for the measuring and reporting of social security assets and obligations, the experience and practices of Canada offer a number of important policy lessons. The article analyses the assets and obligations of the CPP using different actuarial balance sheet methodologies, i.e. open and closed group. It concludes that the balance sheets under the closed‐group with and without future benefit accruals methodologies do not reflect the nature of the partial funding approach of the CPP, whereby future contributions represent a major source of financing for future expenditures. As such, it is inappropriate to reach a conclusion regarding the Plan's financial sustainability considering only the asset shortfalls determined under the closed group with and without future accruals balance sheets. The article asserts that measuring the Plan's assets and obligations using the open‐group approach provides information that properly reflects how changing demographic and economic environments affect the long‐term sustainability of the CPP. In contrast, using the closed group without future accruals approach may provide incomplete or even misleading information. Finally, the article discusses approaches used to report the financial state of the CPP, including both actuarial and financial reporting. It highlights the comprehensive disclosures approach adopted for the purpose of CPP annual reports and the Public Accounts of 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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".