Bidder’s private value distributions in standing timber auctions in the Jiangxi Province of China
Bibliographic record
Abstract
Timber auctions are quite recent in China. In 2009, Jiangxi Province started the first forest rights trading centers; timber auctions conducted through these trading centers have some features different from timber auctions in the developed world. As good auction design is context-specific, for Jiangxi’s timber auctions, we (i) examine the appropriateness of using the independent private value (IPV) framework, (ii) test the endogeneity of the number of bidders in the sealed-bid auctions, and (iii) examine the effect of heterogeneity of timber stands being sold. We use data from first-price sealed-bid and open ascending-price timber auctions from 2009 to 2012. Univariate and conditional kernel density estimators are used to estimate bidder’s private value distributions using IPV framework. Our results suggest that the key assumption of IPV framework does not hold, confirm the endogenous determination of number of bidders, and support the need to incorporate timber stand heterogeneity in private value analysis. Our results also show that neither format of auctions dominates for seller’s revenue and the seller’s revenue is not a monotonic increasing function of the number of bidders. We suggest that a seller should account for local forest and market conditions in the selection of auction format and the reserve price should be set by taking endogenous entry and local competition level into account.
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How this classification was reachedexpand
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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".