Recession-Induced Price Sensitivity: Descriptive Evidence from Whiskey Prices in a Control State
Bibliographic record
Abstract
This paper examines how the COVID-19 recession changed whiskey purchasing in Iowa. This paper examines its impact on whiskey purchasing in Iowa’s control-state market. To decompose recessionary channels, we analyze SKUs with changing prices separately from those with fixed prices. Stock keeping units are divided into two groups: constant and changing prices. For SKUs that changed price, we compute arc elasticities by comparing each pre-recession year (2017-2019) to 2020 at the SKU-month level. For SKUs with constant posted prices, we track quantity growth and its acceleration from 2019-2020 relative to 2018-2019 to identify demand shifts. Summary measures of revenue, bottles sold, and litres are also reported. Price sensitivity rose for a subset of products in 2020, with several items showing clearly negative elasticities of meaningful size. Demand shifted when prices did not change. Lower-priced items gained volume and higher-priced items lagged. Revenue and bottles sold increased in 2020, while total liters declined, which points to smaller packages and higher prices per liter. These results show that the recession operated through both price and non-price channels. These findings offer actionable insights for managers and policymakers on portfolio and pricing strategies during crises. Furthermore, they lay crucial descriptive groundwork for future research aiming to establish causal estimates with richer datasets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".