Failing to deter: analysing Spain’s ineffective antitrust measures and cartelist activities
Bibliographic record
Abstract
This paper critically examines the effectiveness of Spain’s antitrust measures in deterring cartel behaviour. Despite aligning with EU standards, Spain’s penalties for anticompetitive practices have proven ineffective. An analysis of sanctions imposed reveals that both the 2009 Communication and the post-2015 framework fail to deter cartel formation effectively. Corporate fines often do not exceed the expected benefits of collusion, undermining their deterrent function. While increasing fines might enhance effectiveness, such measures risk unintended consequences, including firm insolvency and reduced market competition. Therefore, implementing complementary sanctions could serve as a valuable addition. While Spain’s enforcement system already includes fines for individuals and bidder exclusion, these measures face significant challenges. The lack of detailed definitions and the absence of clear guidelines on the subjects considered liable make imposing these fines more difficult. Additionally, the bidder exclusion mechanism was not properly transposed into Spanish legislation, leading to its temporary suspension by the National High Court pending resolution of appeals.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".