Collusion through market sharing agreements: evidence from Quebec’s Road Paving Market
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.010 |
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