Impact of the minimum wage increase on intimate partner violence (IPV): a quasi-experimental study in South Korea
Bibliographic record
Abstract
BACKGROUND: Poverty is associated with intimate partner violence (IPV), but whether exogenous increases in wage could reduce IPV among low-income women is still unclear. We examined whether the 2018 minimum wage hike led to a reduction in IPV risk among women. METHODS: Using the 2015-2019 Korean Welfare Panel Study, we employed a difference-in-differences (DID) approach to assess the effect of the minimum wage hike on IPV. The analysis focused on married women aged 19 or older. We categorised participants into a target group (likely affected by the minimum wage increase) and a comparison group based on their hourly wage. Three IPV outcomes were examined: verbal abuse, physical threat and physical assault. We conducted DID analyses with two-way fixed-effects models. RESULTS: The increase in minimum wage was correlated with a 3.2% decrease in the likelihood of experiencing physical threat among low-income female workers (95% CI: -6.2% to -0.1%). However, the policy change did not significantly influence the risk of verbal abuse, physical assault or a combined IPV outcome. The study also highlights a higher incidence of all IPV outcomes in the target group compared with the comparison group. CONCLUSIONS: The 2018 minimum wage increase in Korea was associated with a modest reduction in physical threat among low-income female workers. While economic empowerment through minimum wage policies may contribute to IPV prevention, additional measures should be explored. Further research is needed to understand the intricate relationship between minimum wage policies and IPV, and evidence-based prevention strategies are crucial to address IPV risk.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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".