Retirement Factors Driving South Korea’s Highest Older Adult Poverty Rate Among OECD Nations: A Decomposition Analysis
Bibliographic record
Abstract
Abstract This study investigates why South Korea has the highest older adult poverty rate (40%) among OECD countries by decomposing poverty gaps between Korea and other nations. Using data from the Luxembourg Income Study, we apply the Oaxaca-Blinder decomposition method to compare Korea with eight OECD countries (Norway, Germany, Greece, the United States, the United Kingdom, Canada, Australia, and Japan). Our findings reveal that 84–100% of the poverty rate differences are explained by structural factors. If Korea had Germany’s socioeconomic structure, its poverty rate would drop from 51.9% to 5.8%. Korea’s high older adult employment rate helps reduce poverty by 3.9–7.9 percentage points compared to countries like Japan and Australia. At the same time, Korea’s extensive private transfer income lowers poverty by 2.9–4.9 percentage points. However, the most significant factor driving Korea’s high poverty rate is its inadequate public pension system. If Korea’s public pension benefits aligned with those of developed nations, poverty would decline by 24.1–50.2 percentage points. Despite mitigating effects from labor market participation and private transfers, insufficient public pension income remains the dominant cause of older adult poverty in Korea. These findings highlight the urgent challenge of ensuring income security amid rapid population aging and low fertility rates.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".