Search and Choice in Online Consumer Auctions
Bibliographic record
Abstract
Price dispersion in simultaneous online auctions is a puzzle in light of the relatively low search costs required to find the lower price. Much of this price dispersion appears to be due to a lack of switching by bidders between auctions, which in turn could be due to inertia related to search costs. We identify some of the influencing factors through a controlled field experiment involving pairs of simultaneous auctions. Keeping the sellers and the goods sold identical between two auctions, we vary auction design features between and within pairs including shipping cost, open reserve, secret reserve price, and duration, and we provide bidders with incentives to search. We use a choice model that examines individual choice between pairs of simultaneous auctions. We find that within-pair price dispersion is substantial and that prices and auction choice by bidders are indeed related to search costs. We find strong inertia in auction choice and find that this effect significantly interacts with time left in the auction. Although individuals do not always choose a lower-priced auction, they are more likely to do so when search costs are low or search incentives are high.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".