Effects of Conflicts on Labor Market Outcome and Intimate Partner Violence: Evidence from Nepal
Bibliographic record
Abstract
This study investigates the impact of conflict intensity on married women’s employment and intimate partner violence (IPV) in Nepal during and after the civil conflicts. Analyzing five waves of the Nepal Demographic and Health Survey, it reveals a negative short- and long-term effect of conflict on work probabilities for women facing reduced economic opportunities and delayed human capital accumulation. However, this result masks substantial heterogeneity by subgroups. The older cohort experiences a temporary negative effect, while the impact is enduring for younger cohorts. The long-run effect of conflict intensity was more sustained for married women who were children or teenagers at the onset of the war compared to older cohorts. These results hold under IV regressions. Data availability restricts our analysis of IPV to the post-war years. The study does not find a direct impact of conflict on the stated IPV experiences of married women but identifies an indirect effect.HIGHLIGHTSConflict in Nepal affects women’s employment and intimate partner violence.Women’s employment increased during the conflict but declined post-conflict.Conflict intensity in Nepal reduced long-term employment, particularly for younger womenIntimate partner violence shows indirect rise due to reduced women’s employment.The added-worker effect in Nepal was weak, with conflict disrupting labor opportunities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".