The Pension Factor of 9: Actuarial Logic Skewed by Political Myopia
Bibliographic record
Abstract
This paper provides a quick review of the Actuarial Factor of “9”. This factor was devised as an attempt to create a level playing field between Defined Benefit and Defined Contribution Pension Plans (and with Registered Retirement Savings Plans). The paper argues that this attempt would have been quite successful had it not been for the “freezing ” of the CCRA contribution limits many, many times. The paper argues that these limits have been “frozen ” because the Ministry of Finance sees RPPs/RRSPs as representing a large “tax expenditure”. This is because RPP/RRSP contributions are tax deductible and investment income on Registered funds accrues tax free until taken as income. The paper argues that instead of being viewed as “tax expenditures”, RPP/RRSPs represent the “perfect ” deferred tax asset. This is because the Canadian government will reap increased tax revenues from the baby-boom generation when they cash in their RPP/RRSPs, which will be exactly when the government will need extra funds to pay for increased health care services for the same baby-boom generation. The paper goes on to argue that the CCRA contribution limits (which are “frozen”) also create a bias in favour of DC plans versus DB plans. While this may be intended, the author doubts that it is understood by Ottawa parliamentarians. In summary, the paper argues that it is time to index the pension contributions to average wages, as was originally intended, to restore the actuarial logic of the factor of “9”. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".