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Record W4391814225 · doi:10.1111/cjag.12354

On household food stock and waste under risk

2024· article· en· W4391814225 on OpenAlexvenueno aff
Jian Li, Wuyang Hu, Ping Qing, Jean‐Paul Chavas

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsEndogeneityFood wasteStock (firearms)EconomicsFood safetyFood chainBusinessAgricultural economicsEconometricsGeographyFood science

Abstract

fetched live from OpenAlex

ABSTRACT This paper investigates household behavioral response to disruptions in the food supply chain, with a focus on the role for risk and its effects on household food stocks and food waste. We present an empirical analysis based on data from Chinese consumers over multiple periods in 2019 and during the COVID‐19 crisis of 2020. We investigate how household behavior changed during the COVID‐19 crisis, documenting both food stockpiling and increased food waste. The econometric analysis relies on a control function approach to handle endogeneity. We decompose the effects of increased risk on waste during the crisis into two components: the direct effect reflecting household decisions conditional on food stock; and the indirect effect associated with induced adjustments in food stock. Both effects on food waste are found to be positive, reflecting difficulties households have in managing large food stocks. We present evidence that one percentage point increase in household stocks during a period of supply disruption contributed to a 0.055–0.297 percentage point increase in food waste across food categories. We also present evidence that these effects may persist over time.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.028
GPT teacher head0.162
Teacher spread0.134 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomieSame topicFood Waste Reduction and SustainabilityFrench-language works237,207