Bibliographic record
Abstract
Canadians are not fond of hearing news about people losing their hard-earned pensions because their employer misused the money. The thought of some Working Joe or Jane being deprived of a pension, after a lifetime of working for a company, is naturally repugnant. That is why regulations around defined-benefit pension plans are designed to force employers to keep their pension funds sufficiently solvent. But there are many ways to achieve that end and, while the rules that Canadian companies must follow might serve to help preserve pension savings, they also be very expensive — to employers and regulators — compared to other policies that can do the same thing. In fact, more flexibility in the rules might even make it easier for companies to offer richer pension benefits to employees than they already do. Some of Canada’s outdated pension-funding rules have created the opposite problem: There are now pension plans that are actually overfunded if one assumes the company and the plan will continue. That means money the sponsoring companies could be using to hire more workers, to offer employees better pay and benefits, or to invest, is tied up in pension coffers. The problem lies in the divergence between a “going-concern” valuation — which assumes that the plan continues indefinitely — with the more prescriptive “solvency” valuation that is central to Canadian regulations. It examines funding adequaciy if one assumes the employer is going to go out of business (even if the vast majority of large employers are at any given time at no risk of that). In the days when fixed-income returns were lucrative, companies relied on pension fund investments to top up the funds, reducing sponsor contributions to unsafe levels. The solvency rules required plans that were reaping higher returns in the stock market to continue making some contributions to their plans. Back then, the gap between a going-concern valuation and a solvency valuation was small, and so the rules were not an unacceptable burden. Those rich returns are gone. Now, that gap between valuations has grown dramatically. In B.C., for example, a recent analysis found that when using a going-concern evaluation, 75 per cent of 143 defined-benefit plans registered in the province in 2015 had at least 100-per-cent funding, while the median funding ratio was 124 per cent. Using a solvency model, the median funding ratio was instead estimated to be a much lower 85 per cent. Closing that gap would require onerous pension contributions. More importantly, the contributions it triggers might never be needed to cover benefits. Quebec is the first province to recognize that pension-funding rules need to be revisited and made more responsive, with new rules coming in that will reduce the unnecessary burden on employers while also adapting to changes in the economic environment. Ontario is showing signs that it will take steps in the same direction. Regulators everywhere should be revisiting pension rules to: remove the solvency-valuation requirement for well-funded plans, while allowing the regulator to assume a worst-case scenario in the uncommon case where they believe it to be warranted; to develop a method to rate the credit risk of a plan; to be less stringent and more realistic about plan liabilities (by allowing some types of liabilities to use a longer amortization period); but still restricting plan changes for underfunded plans. The result would not only reduce the cost and work of over-regulating well-funded, well-run plans, while freeing up cash . By reducing pressure on the cash flow for sponsors, and adding more flexibility, the policymakers will ultimately make defined-benefit pension plans more sustainable. They might even see defined-benefit plans making a comeback among employers who found heavy contributions enough to drive them out of the DB world.
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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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 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".