Consumer Search, Productivity Heterogeneity, Prices, Markups, and Pass-through: Theory and Estimation
Bibliographic record
Abstract
We develop and estimate a search model in which identical consumers trade with price-setting firms that differ in productivity. In the model, equilibrium distributions of both prices and markups are non-degenerate and continuous with a firm’s price decreasing as its productivity increases. Variation in markups across firms is more complicated and depends on the search process and the distribution of productivity, both of which are estimated using firm-level data on retail industries in Canada. We use the estimated model to characterize the qualitative and quantitative differences in prices and markups across firms. These differences stem from firm-level variation in demand elasticities driven by productivity heterogeneity and by imperfect information about prices. Additionally, we derive analytical expressions to determine how individual firm prices and markups respond to changes in cost and demand. This allows us to empirically analyze the heterogeneity in firms’ pass-through of cost and demand shocks to prices and markups. Our findings reveal substantial heterogeneity in pass-through across firms, highlighting the distributional impact of shocks across consumers purchasing at different points of the price distribution. Finally, our analysis underscores the importance of accounting for individual firm price and markup adjustments to fully understand pass-through to average prices.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".