Lifting the Lid on Pension Funding: Why Income-Tax-Act Limits on Contributions Should Rise
Bibliographic record
Abstract
Single-employer, defined-benefit (DB) pension plans in Canada are in decline. Among the reasons: laws and regulations that foster under-funding of these plans by their sponsors (Laidler and Robson 2007). A case in point is the prohibition by the federal Income Tax Act (ITA) of sponsor contributions to such plans when their assets exceed recorded liabilities by 10 percent.1 Recent volatility in asset prices and interest rates, and resulting volatility in DB plan balance sheets, highlights the desirability of raising — or even removing — this restriction. The 10 percent limit exists to prevent companies making pension contributions, which are tax deductible, to reduce taxable profits. The benefit of the limit is marginal at best, however, since (i) businesses will typically prefer to reinvest their earnings or pay them out as dividends, (ii) pension funds attract tax when distributed or withdrawn, and (iii) regulations prevent deliberate over-funding of designated plans. Easier to demonstrate are the problems the limit creates. First, and fundamentally, limiting contributions in good times stops plan
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".