Collusion through market sharing agreements: evidence from Quebec’s Road Paving Market
Bibliographic record
Abstract
I study a case of market sharing agreements to provide evidence of coordination between colluding firms on the degree to which they compete against each other (henceforth referred to as head-to-head competition) and their bidding behavior. I also quantify the impact that coordinating head-to-head competition has on procurement costs. My focus is on the two largest firms bidding in provincial road paving procurement auctions in Quebec between 2007 and 2015. I use the police investigation into collusion and corruption in the Quebec construction industry launched in October 2009 to capture the end of this cartel. I find that after this date, the two suspected firms i) were more likely to bid in the same auction and ii) submitted significantly lower bids when they competed in the same auction. A structural model of entry and bidding shows that if the firms had kept competing head-to-head at the same rate as in the collusive period but had stopped colluding on bids, bids would have increased by about 3.86% with respect to the competitive scenario observed after the police investigation began. This finding suggests that there were additional procurement costs associated with firms coordinating on the degree of head-to-head competition.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".