Target- Benefit Plans in Canada – An Innovation Worth Expanding
Bibliographic record
Abstract
The limitations inherent in the traditional pension models – defined contribution (DC) and defined benefit (DB) – are facing increased scrutiny and new models are developing in response to these pressures. Due to extremely low interest rates and the volatility of equity markets, over the past several years many DB pension plans have suffered significant solvency deficits. As a result, plan sponsors have sought relief from higher contributions required under pension laws. Temporary relief measures granted in certain jurisdictions have not addressed the underlying issues but have provided merely short-term solutions. At the other end of the spectrum, DC plans incorporate both predictable contributions and an alignment of risk and reward for plan members, but they leave complicated investment decision-making to plan members who frequently have no investment expertise. Furthermore, DC plans fail to capture substantial value available from pooling of costs, investment risk and longevity risk among plan members. We need to move beyond the DB versus DC debate towards a middle-ground option that incorporates some of the positive attributes of both designs. Target-benefit plans (TBPs) can deliver the cost predictability of DC plans combined with a defined-benefit-type pension to retirees, with predictable contribution levels, and enable pooling of longevity and investment risks. This Commentary reviews the recent New Brunswick shared risk pension legislation and draws lessons that can be applied to the design of similar TBP legislation elsewhere. In most Canadian jurisdictions, pension laws do not currently accommodate single-employer TBPs – although several provinces have taken initial steps. Existing legislation generally prohibits reduction of accrued benefits outside of the multi-employer unionized environment, and a key element of TBPs is their ability to let benefits vary as a function of the funding status of the plan. Tax rules must be changed to accommodate single-employer TBPs. As well, clear and logical accounting guidance for TBPs must be established to facilitate the emergence of such plans. Also, a jurisdictions wishes to permit conversion of accrued benefits to target benefits, legislative change is required. In New Brunswick, accrued benefits may be converted, which can promote intergenerational equity. Pension standards laws across the country will have to be changed in order to facilitate the emergence of new design options such as single-employer TBPs.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".