Understanding the determinants of consumer grocery stockpiling behavior
Bibliographic record
Abstract
Abstract Grocery stockpiling is a common behavioral response to the emergence of disasters or heightened uncertainty. Nonetheless, the phenomenon and methods for mitigating it are not well understood. Using a model of household shopping and inventory management, we conceptualize stockpiling as a result of an increase in the fixed cost of making grocery shopping trips, or the opportunity cost of time associated with shopping. In a laboratory experiment, we find that stockpiling increases (decreases) by 78 and 41% (22%) with an increase in fixed costs and price reductions (imposition of purchase limits), respectively. We also find that stockpiling leads to fewer (more) grocery trips by 33 and 22% (36%) under the same three conditions, respectively. Our experiment and subsequent cluster analysis suggest that loss aversion suppresses stockpiling. Our experiment shows that imposing purchase limits, a common retail response to stock‐outs, can trigger stockpiling during shopping trips without purchase limits. Although we do not claim external validity, our study suggests that store managers and policymakers should be careful about solutions during a stockpiling event, such that they do not exacerbate stockpiling, which may disproportionately affect vulnerable groups and disrupt supply chains.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".