Social pension expansion and suicidal behaviour of older adults in Korea: A quasi-experimental study
Bibliographic record
Abstract
This study examines the effects of a significant reform to South Korea's social pension system—the introduction of the Basic Pension (BP) in July 2014—on suicidal behavior among older adults. We utilized data from the Korean Welfare Panel Study (2011–2021) to evaluate whether this reform has helped reduce suicidal behavior among BP beneficiaries compared to non-beneficiaries. Employing the Callaway and Sant’Anna Difference-in-Differences (CSDID) approach for a robust analysis, our findings report a significant reduction in suicidal behavior, particularly among women, with an overall reduction of 1.3%. The study highlights the policy effect of the BP in enhancing the economic and mental health stability of older adults, demonstrating the effectiveness of a generous old-age income security program in mitigating factors that contribute to high suicide mortality. These insights are crucial for policymakers aiming to strengthen the welfare state and improve public health outcomes in similar contexts globally. • Limited evidence exists on the impact of social pensions on suicidal behavior in older adults. • South Korea's Basic Pension (BP) reform reduced suicidal behavior by 1.3%. • BP impact was greater among women than men, with a 1.9% reduction in suicidality. • Gains in income and life satisfaction strengthened suicidality reduction.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| 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".