Critical review of welfare dependency in active labor market programs in Korea: existence, causes, and interpretations
Bibliographic record
Abstract
This paper comprehensively analyzes the existence and causes of welfare dependency in active labor market programs (ALMPs) administered by the Korean Government. For this analysis, we utilize the Ministry of Employment Labor’s database, using data collected from 306,410 ALMP participants from 2006 to the first quarter of 2012. According to our analysis, 4.4–12.9% of ALMP participants are likely to be in the ‘welfare trap.’ The probability of falling into the welfare trap is affected by individual characteristics. For instance: the elderly, women, and highly educated people are shown to be particularly vulnerable. Moreover, when ALMPs’ benefits are larger than the official minimum wage, individuals tend to stay in the job programs longer. At the same time, if a participant lives in a district with more people in the welfare trap, he or she is less likely to exit from ALMPs. Despite the fairly significant proportion of participants shown to have fallen into the welfare trap, most of the cases are not due to moral hazard or generously designed financial incentives; rather, our research suggests that people with a lower level of job capacity for the private labor market cannot but stay longer in ALMPs.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".