Empirical Analyses of Nonlinear Effects of Reserve Prices on Ending Prices in Online Auctions
Bibliographic record
Abstract
Empirical results about the effect of open reserve price on ending prices in auctions are mixed, with some researchers finding a positive effect on ending price and others reporting a negative or no effect. The objective of this research is to propose a new theoretical framework. First, without an open reserve price, auctions attract more bidders, resulting in increased competition and higher ending prices—a so-called competition effect. Second, a high open reserve price may serve as a price floor or a reference price, influencing bidders’ valuations and increasing ending prices. Quantile regression is used, which has the advantage of providing robust parameter estimates of the effect of open reserve prices on ending prices for different levels of the reserve price. Estimates of a longitudinal data set for four products collected over a period of one year show an u-shaped relationship between open reserve prices and ending prices, where either no reserves or high reserves results in higher ending prices. By comparing ending prices of auctions with an open reserve versus a secret reserve, it is shown that open reserve prices have a reference-price effect rather than a price-floor effect.
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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 | 0.013 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".