Denying Leniency to Cartel Instigators: Costs and Benefits
Bibliographic record
Abstract
A large number of countries have introduced successful leniency programs into their competition \nlaw enforcement to encourage colluding firms to come forward with evidence that will help \ndetect cartels and punish price-fixers. This paper studies a feature of some of these programs \nthat has received relatively little attention in the literature: the inclusion of “No Immunity for \nInstigators Clauses” (NIICs). These provisions deny leniency benefits to parties that instigate \ncartel behavior or function as cartel ringleaders. Our results show that NIICs can lead to \nincreased or decreased levels of cartel conduct. By removing the instigator’s benefit from \ncooperating with the authorities, a NIIC undoes some of the destabilizing benefit the leniency \nprogram was intended to generate and thereby furthers cartel stability. On the other hand, the \ninstigator faces an asymmetrically severe punishment under a NIIC and this can reduce the \nincentive to instigate in the first place.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".