Lessons from Pension Reform in the Americas
Bibliographic record
Abstract
Abstract Latin American experiments with pension reform began when Chile converted its public pay-as-you-go system to a system of private individual accounts in the early 1980s. Several other Latin American countries then followed suit, inspired both by Chile's reforms and by World Bank recommendations stressing compulsory government-mandated individual saving accounts. Individual accounts were subsequently introduced in a number of countries in Europe and Asia. Many are now re-evaluating these privatizations in an effort to ‘reform the reform’ to make these systems more efficient and equitable. This book assesses pension reforms in this new ‘post-privatization’ era. After a discussion on demographic trends in the foreword by Nobel laureate Robert W. Fogel, Section 1 of the book includes chapters on the role of pension system default options, the impact of gender, and a discussion of the World Bank's policies on pension reform. The chapter on the evidence from Chile's new social protection survey points to key lessons from the world's first privatization. Section 2 offers analysis of several significant reform initiatives in the hemisphere, and includes chapters on the United States, Canada, Mexico, Costa Rica, Brazil, Peru, Uruguay, and Argentina.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".