The interaction of pillars in multi‐pillar pension systems: A comparison of Canada, Denmark, Netherlands and Sweden
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it