Economic crises, population aging and the electoral cycle: Explaining pension policy retrenchments in 19 OECD countries, 1981–2004
Bibliographic record
Abstract
After decades of recurrent improvements in the generosity of public pension programs, since the early 1980s many pension reforms aimed to decelerate pension spending growth and strengthen the finances of these programs by retrenching the duration and/or value of pension entitlements. To understand this historical reversal in public pension provision, this article examines the forces affecting the enactment of contemporary pension retrenchments in 19 OECD countries. Based on a synthetic review of the pension policy literature, it identifies 90 pension retrenchments passed in these countries between 1981 and 2004. A growing literature on pension policy reform suggests that these policy events occur only when policy-makers can devise mechanisms to reduce their political blame. Building on this research, this article argues that the strategic consideration of economic and electoral cycles constitute two blame-avoidance strategies. First, by passing a pension retrenchment early in the electoral cycle, policy-makers can expect to face less electoral retaliation. Second, due to uncertainty in demographic projections, the demographic transition constitutes a weak discursive strategy to legitimate pension retrenchments. For this reason, population aging only affects the likelihood of reform by increasing the impact of economic crises. The article presents results from conditional frailty models for recurrent and sequential events that support this argument.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".