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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".