Social Security on Auto-Pilot: International Experience with Automatic Stabilizer Mechanisms
Bibliographic record
Abstract
As the baby-boom generation enters retirement, a long-forecast funding crisis of the U.S. Social Security system is about to become a reality. Many other high-income countries are faced with similar financial problems within their public pension systems. Some of those countries have adopted legislative measures to reduce their funding deficits, and a few have included automatic adjustment mechanisms by which staged adjustments would be made in either benefits or revenues without the need for new legislation. We examine the cases of automatic stabilizer mechanisms (ASMs) in Canada, Sweden, Germany and Italy, with the former two being relatively successful examples, while the latter two are cases of ASMs that were more problematic. Drawing on these international examples, we examine various ASMs that could be implemented in the United States. We suggest three reforms: an increase in the retirement age, adoption of a chained Consumer Price Index, and an adjustment of the indexation of the taxable wage ceiling to stabilize the ratio of taxable to covered wages at its 1983 value of 90 percent. Together, these three reforms would reduce the 75-year actuarial deficit to about 0.5 percent of taxable wages. We conclude, though, that until the current deficit is fully eliminated, an ASM aimed at maintaining financial balance would not make sense for the Social Security program. However, the international experience does offer a number of lessons for future reforms of the U. S. retirement system.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".