Pre‐Hurricane Consumer Stockpiling and Post‐Hurricane Product Availability: Empirical Evidence from Natural Experiments
Bibliographic record
Abstract
The provision of essential supplies is a key service provided by retailers when demand spikes due to consumer stockpiling during environmental emergencies. Moreover, it is important for retailers to quickly recover from these events by replenishing the stock of essential supplies to meet the continuing needs of local residents. The main purpose of this research is to study consumer precautionary stockpiling behavior prior to the onset of hurricane landfalls and determine the impact of this behavior on in‐store product availability for various formats of retail store outlets. Specifically, we focus on the bottled water product category, an essential emergency category in hurricane preparedness. This study combines an event analysis methodology with econometric models using archival retail scanner data from 60 US retail chains located in 963 counties and real‐time data from four recent US continental hurricanes. We find that supply‐side characteristics (retail network and product variety), demand‐side characteristics (hurricane experience and household income), and disaster characteristics (hazard proximity and hazard intensity) significantly affect consumer stockpiling propensity as the hurricanes approach. The increased consumer stockpiling has immediate and longer‐term impacts on retail operations, namely, in‐store product availability. Among various retail formats, drug stores are associated with the highest consumer stockpiling propensity before hurricanes, while dollar stores and discount stores are associated with the lowest in‐store product availability following hurricanes. Our study points to the need for retailers and policymakers to carefully monitor factors affecting consumer stockpiling behavior that will allow for better allocation of critical supplies during the hurricane season.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".