Long-term Consequences of Men’s Migration for Women’s Well-being in a Rural African Setting
Bibliographic record
Abstract
Labor migration is a massive global reality, and its effects on the well-being of nonmigrating household members vary considerably. However, much existing research is limited to cross-sectional or short-term assessments of these effects. This study uses unique longitudinal panel data collected over 12 years in rural Mozambique to examine long-term connections of women's exposure to husband's labor migration with women's material security, their perception of their households' relative economic standing in the community, their overall life satisfaction, and their expectations of future improvements in household conditions. To capture the cumulative quality of such exposure, we use two approaches: one based on migrant remittances ("objective") and the other based on woman's own assessment of migration's impact on the household ("subjective"). The multivariable analyses detect a significant positive association between "objective" migration quality and household assets, regardless of women's current marital status and other characteristics. However, net of household assets, "objective" quality shows a positive association with life satisfaction, but not with perceived relative standing of the household or future expectations. In comparison, "subjective" quality is positively associated with all the outcomes even after controlling for other characteristics. These findings illustrate the gendered complexities of long-term migration impact on nonmigrants' well-being.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".