Separability, spillovers, and segmented markets : Evidence from dairy in India
Bibliographic record
Abstract
A long history of empirical research has focused on testing whether and when household consumption and production decisions are separable. The underlying premise is that if markets were perfect, household consumption would be independent of production. In this paper, we propose a novel explanation for the heterogeneity in the strength of correlation between production and consumption based on market channel choice. We examine how the coexistence of informal and formal value chains, and their relative importance can complicate the relationship between household production and consumption differently for different households. Our analysis of a rich household panel data set from 30 villages in eight states in rural India, focusing on dairy, leads us to four key conclusions. First, milk consumption is correlated with production and while markets play a role in consumption smoothing, they are not a complete substitute for household production. Second, a large presence of formal milk buyers in a village is associated with lower milk consumption of dairy cow-owning households, overturning the positive association of participation in formal value chains with household milk consumption. Third, contrary to expectations, for households that do not own dairy animals and net buyers, the presence of formal value chains remains uncorrelated with milk consumption. Fourth, we infer, test for and find suggestive evidence of segmented milk markets, i.e., different types of households (those not owning dairy animals, small landowners and net buyers vis-à-vis dairy animal owning households, large landowners and net sellers) participate in different milk markets that do not interact with each other. Our findings underscore the role of market segmentation in mediating the relationship between production and consumption. Policy makers focused on market development or production-based strategies to improve consumption of nutritious foods would do well to pay attention to the structure and nature of markets while designing interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".