Empirical Analyses of Nonlinear Effects of Reserve Prices on Ending Prices in Online Auctions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it