The interaction of pillars in multi‐pillar pension systems: A comparison of Canada, Denmark, Netherlands and Sweden
Bibliographic record
Abstract
Abstract Canada, Denmark, the Netherlands and Sweden have advanced multi‐pillar pension systems. Using micro‐simulations, this article presents a close examination of the interaction of pillars in these countries. The relative importance and the role of the different pension pillars vary from country to country, and according to age, income, gender and socio‐economic dimensions as well as between generations. A further area of investigation is the mitigation capacity of the four pension systems. On the one hand, adverse labour careers lead to lower life‐time earnings and lower private pension accruals. On the other hand, these effects are mitigated through the design of pillars and their interaction. Mitigation is important to income security and stability in retirement and to post‐retirement income distribution. However, mitigation mechanisms come at the cost of incentives. Moreover, in many countries, the generosity of public benefits is set to decrease – increasing the importance of private pensions. This will shift risk and uncertainty from employers and pension institutions to individuals. Thus, risks and uncertainties related to private pensions will become more important, raising questions about the division of responsibilities between public and private pensions, and about the potential of mitigating such risk through pillar interaction. These concerns are further reinforced by labour market changes. Although a pension system free of distortions is inconceivable, this article seeks to contribute to addressing how mitigation should be designed, and how mitigation and risk sharing should be balanced against incentives, challenges which are as much political as technical.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".