Bid Rigging And Entry Deterrence In Public Procurement: Evidence From An Investigation Into Collusion And Corruption In Quebec
Bibliographic record
Abstract
We study the impact of an investigation into collusion and corruption to learn about the organization of cartels in public procurement auctions. Our focus is on Montreal’s asphalt industry, where there have been allegations of bid rigging, market segmentation, complementary bidding and bribes to bureaucrats, and where, in 2009, a police investigation was launched. We collect procurement data and use a difference-in-difference approach to compare outcomes before and after the investigation in Montreal and in Quebec City, where there have been no allegations of collusion or corruption. We find that entry and participation increased, and that the price of procurement decreased. We then decompose the price decrease to quantify the importance of two aspects of cartel organization, coordination and entry deterrence, for collusive pricing. We find that the latter explains only a small part of the decrease.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".