Impact of the expanded old age income security programme on suicide mortality among older adults in South Korea: a quasi-experimental study
Bibliographic record
Abstract
INTRODUCTION: This study examined the impact of the expansion, implemented in July 2014, of Korea's tax-financed (non-contributory) old age income security programme, the Basic Pension (BP), on suicide mortality among individuals aged 65+. METHODS: Using aggregate mortality data from 2010 to 2019, we employed event-history difference-in-differences (DD), 2×2 DD, and difference-in-difference-in-differences (DDD) approaches, leveraging two identification strategies: (1) regional variations in the proportion of BP beneficiaries and (2) a triple-difference strategy incorporating both regional variation and age-based eligibility. Event study models were used to test the common trends assumption and assess the dynamic effects of the reform. RESULTS: The event study findings revealed that the reduction in suicide mortality among older women became more pronounced over time, with significant decreases emerging 10-16 quarters after the reform's implementation. The 2×2 DD analysis reported a 20% (95% CI -33% to -6%) reduction in suicide rates among older women in high-beneficiary regions compared with low-beneficiary regions, and no significant effects were observed for men. The DDD analysis did not yield statistically significant results, but the estimated effect size for women (-17%; 95% CI -52% to 18%) was consistent in direction with the DD analysis. CONCLUSION: The doubling of social pension benefits in South Korea appears to have contributed to reduced suicide mortality among older women. These findings suggest that targeted income security programmes may help reduce suicide rates among economically vulnerable older adults. They provide valuable insights for low- and middle-income countries considering similar interventions.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".