Patterned remittances enhance women's health-related autonomy
Bibliographic record
Abstract
The consequences for women "left behind" by virtue of temporary male migration are mixed. On the one hand, concomitant changes in fertility, participation in the labor force, and social norms are often associated with increased independence for women. On the other hand, women left behind can be vulnerable to increased dependency on members of their husbands' family or face limited access to social institutions. These shifts in women's capacity for decision making can have important implications for their health and well-being. Focusing on the state of Kerala in southern India, we examine the conditions under which the remittances that migrants send home have an impact on the health of women left behind. Specifically, we assess the extent to which the timing of remittance sending can support women's autonomy and improve their ability to make autonomous healthcare decisions. We use evidence from migrant households in Kerala, a region deeply engrained in the world labor migration system for more than five decades. Analysis is conducted with data from the 2016 wave of the Kerala Migration Survey (KMS), a representative household survey, and paired with in-depth qualitative interviews with women in Kerala whose husbands and other family members have migrated to the Gulf. We show that the positive effect of remittances on women's autonomy manifests primarily through the timing of remittance receipt, not the amount of money remitted. Regular remittances are associated with higher levels of autonomy than remittances received at irregular intervals, net of amount remitted. This finding challenges the usual emphasis on remittance volume as the driving factor of social and behavioral change in sending communities. Analytical efforts should be refocused on the social-interactional component of remittance sending and how these interactions can impact women's health and autonomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".