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Record W4383033114 · doi:10.1111/agec.12786

Separability, spillovers, and segmented markets : Evidence from dairy in India

2023· article· en· W4383033114 on OpenAlexfundno aff
Sudha Narayanan, Digvijay S. Negi, Tanu Gupta

Bibliographic record

VenueAgricultural Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
FundersMultiple Sclerosis Scientific Research FoundationBill and Melinda Gates Foundation
KeywordsConsumption (sociology)Production (economics)EconomicsUncorrelatedMarket segmentationValue (mathematics)MicroeconomicsDairy industryBusinessAgricultural economicsFood science

Abstract

fetched live from OpenAlex

Abstract A long history of empirical research has focused on testing whether and when household consumption and production decisions are separable. If markets were perfect, household consumption would be independent of production. In this article, we propose that market channel choice complicates this relationship. Our analysis of household panel data from rural India, focusing on dairy, leads us to four key conclusions. First, milk consumption is correlated with production, and markets 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 in dairy 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, that is, different types of households participate in different markets for milk that do not seem to interact with each other. Policymakers focused on market development or production‐based strategies need to factor in the possibility of market segmentation based on market channels while designing interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.023
GPT teacher head0.206
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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