Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation
Bibliographic record
Abstract
We offer an econometric framework that models consumer's consideration set formation as an outcome of her costly information search behavior. Because frequently purchased products are characterized by frequent price promotions of varying depths of discounts, a consumer faces significant uncertainty about the prices of the brands. The consumers engage in a fixed-sample search strategy that results in their discovering the posted prices of a subset of the available brands. This subset is referred to as the consumer's “consideration set.” The proposed model is estimated using the scanner data set for liquid detergents. Our key empirical results are: (i) consumers zincur significant search costs to discover the posted prices of the brands; (ii) whereas in-store displays and feature ads do not influence consumers' quality perceptions of the brands, they significantly reduce search costs for observing the prices of the brands; (iii) per capita income of consumer's household significantly increases her search costs; and (iv) the consumers' price sensitivity is seriously underestimated if we were to assume that consumers get to know all the posted prices at zero cost. The proposed model is also estimated for the ketchup category to enable us to do cross-category comparisons of consumers' price search behavior
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".