Does Price Elasticity Vary with Economic Growth? A Cross-Category Analysis
Bibliographic record
Abstract
How does price sensitivity change with the macroeconomic environment? The authors explore this question by measuring price elasticity using household-level data across 19 grocery categories over 24 quarters. For each category, they estimate a separate random coefficients logit model with quarter-specific price response parameters and control functions to address endogeneity. This specification yields a novel set of 456 elasticities across categories and time that are generated using the same method and therefore can be directly compared. On average, price sensitivity is countercyclical: It rises when the macroeconomy weakens. However, substantial variation exists, and a handful of categories exhibit procyclical price sensitivity. The authors show that the relationship between price sensitivity and macroeconomic growth correlates strongly with the average level of price sensitivity in a category. They examine several explanations for this result and conclude that a category's share of wallet is the more likely driver versus alternative explanations based on product perishability, substitution across consumption channels, or market power.
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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 | 0.026 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".