The Politics of Automatic Stabilization Mechanisms in Public Pension Programs
Bibliographic record
Abstract
Demographic and fiscal pressures have increased pressures on governments in most wealthy countries to reduce the generosity of their public pension programs. Mechanisms that automatically adjust public pension levels to take account of factors such as increased life expectancy and slower economic growth are appealing to politicians because it saves them from having to take loss-imposing actions that are likely to incur political blame. This paper analyzes the financial and political potential of automatic stabilizing mechanisms (ASMs), beginning with a discussion of design issues and alternatives. This is followed by a discussion of potential adoption, implementation, and sustainability challenges for automatic stabilizing mechanisms and a review of experiences with stabilization mechanisms in three countries: Canada, Sweden and Germany. The paper argues that ASMs are vulnerable to erosion over time, especially when the losses that the ASM would impose are substantial, and when elections are impending. Preserving the integrity of ASMs is most likely where the parties that initially supported their adoption continue to be able to sustain cartel-like behavior with respect to pension policymaking. Overall, the analysis in this paper suggests that automatic stabilizing mechanisms are no panacea for the problems of countries facing serious long-term pension financing problems.
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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 | 0.031 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".