Combatting corruption and collusion in public procurement: Lessons from Operation Car Wash
Bibliographic record
Abstract
This article examines the question of how a nation can combat corruption and collusion and prevent these practices from plaguing and undermining public procurement processes. This matter is especially important to Brazil where Operation Car Wash exposed widespread corruption and collusion affecting public procurement. Although focusing on Brazil, this article reflects on a broader academic and policy debate as to how a nation can escape from a ‘high-corruption’ equilibrium, especially one strengthened by its interaction with supplier collusion. In particular, whether endemic corruption can be combatted through an invigorated law enforcement push, combined with incremental reform, or whether some ‘big bang’ approach, with complete institutional overhaul, is required to establish a new equilibrium. The article notes that the Brazilian experience provides support for the hypothesis that, where corruption is endemic, better laws and law enforcement may be insufficient on their own to break a cycle and to remove the incentives and opportunities for corruption and collusion that exist. However, it also recognizes that, for many jurisdictions, wholesale big bang reform is unlikely to be feasible. It thus proposes a multi-pronged, and self-reinforcing, set of reforms to trigger change, concentrated on weaknesses diagnosed in the system. In particular, it suggests that where corruption affects public procurement, beyond specific adjustments to procurement, competition and anti-corruption laws, procurers, anti-corruption and competition enforcement agencies need to work closely together to coordinate policies, achieve synergies and to combat incentives and opportunities for corruption and collusion within procurement processes. Such reforms must be combined with measures to tackle broader factors contributing to systemic corruption. Although inspired by the Brazilian case study, the diagnosis and proposed reform strategy provides a workable model for use in other jurisdictions.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".