Detecting Collusion in Timber Auctions : An Application to Romania
Bibliographic record
Abstract
Romania was one of the first transition countries in Europe to introduce auctions for allocating standing timber (stumpage) in public forests. In comparison with the former system in the country-administrative allocation at set prices-timber auctions offer several potential advantages: greater revenue generation for the government, a higher probability that tracts will be allocated to the firms that value them most highly, and stronger incentives for technological change within industry and efficiency gains in the public sector. Competition is the key to realizing these advantages. Unfortunately, collusion among bidders often limits competition in timber auctions, including in well-established market economies such as the United States. The result is that tracts sell below their fair market value, which undermines the advantages of auctions. This paper examines the Romanian auction system, with a focus on the use of econometric methods to detect collusion. It begins by describing the historical development of the system and the principal steps in the auction process. It then discusses the qualitative impacts of various economic and institutional factors, including collusion, on winning bids in different regions of the country. This discussion draws on information from a combination of sources, including unstructured interviews conducted with government officials and company representatives during 2003. Next, the paper summarizes key findings from the broader research literature on auctions, with an emphasis on empirical studies that have developed econometric methods for detecting collusion. It then presents an application of such methods to timber auction data from two forest directorates in Romania, Neamt and Suceava. This application confirms that data from Romanian timber auctions can be used to determine the likelihood of collusion, and it suggests that collusion reduced winning bids in Suceava in 2002 and perhaps also in Neamt. The paper concludes with a discussion of actions that the government can take to reduce the incidence of collusion and minimize its impact on auction outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".