Moving Beyond the ‘DB vs. DC’ Debate: The Appeal of Hybrid Pension Plans
Bibliographic record
Abstract
This article analyzes the tradeoffs between uncertainties in contributions and benefits embedded in different pension arrangements. The two key criteria for evaluating the risk-sharing characteristics of a private pension plan from the perspective of the plan member are the funding ratio (ratio of assets to liabilities) and the replacement rate (ratio of benefits to salaries). The stochastic simulations performed (considering financial risks only) show that hybrid plans (those in between traditional defined benefit and individual defined contribution) can offer efficient and sustainable forms of risk-sharing. The appeal of different hybrid plans depends very much on the regulatory, social, and economic environment. In situations where funding excesses can be efficiently and fairly apportioned, conditional indexation plans appear to have the greatest potential as sustainable forms of risk-sharing. However, the appropriate design of hybrid plans requires careful consideration of the relative pension plan risks that can be borne by working and retired individuals.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".