On household food stock and waste under risk
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".