Milk, money, and gender: Exploring the link between women's decision‐making in dairy production and welfare investments in boys versus girls
Bibliographic record
Abstract
Abstract Greater women's bargaining power and decision‐making within a household have been shown to increase investments in human capital. This study links women's participation in decision‐making in dairy production with household investment in girls and boys in health, nutrition, and education. We survey households in the urbanizing region of Bangalore, India. We utilize a multinomial treatment effects model to analyze the individual and household factors that are associated with women's participation in sole or joint decision‐making. We then assess how the type of decision‐making influences a household's investments. The results first show that female decision‐making households are more disadvantaged on average than other types of decision‐making households. Second, we observe that female decision‐makers for dairy production are more likely to have more children and earn a higher income than their husbands. Third, the main findings show that households in which women engage in joint decision‐making have higher levels of investment across all categories for girls. Conversely, investments decrease in education and nutrition for both girls and boys when the wife is the sole decision‐maker. Lastly, investments are enhanced further for girls when households can sell milk at a higher price.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".