Rules, Discretion, and Corruption in Procurement: Evidence from Italian Government Contracting
Bibliographic record
Abstract
The benefits of bureaucratic discretion depend on the extent to which it is used for public benefit versus exploited for private gain. We study the relationship between discretion and corruption in Italian government procurement auctions, using a confidential database of firms and procurement officials investigated for corruption by Italian enforcement authorities. We show that discretionary procedure auctions (those awarded based on negotiated rather than open bidding) are associated with corruption only when accompanied by limits to competition. We further show that, while these "corruptible" discretionary auctions are chosen more often by officials who are themselves investigated for corruption, they are used less often in procurement administrations in which at least one official is investigated for corruption. These findings fit with a framework in which more discretion leads to greater efficiency as well as more opportunities for theft, and a central monitor manages this trade-off by limiting discretion for high-corruption procedures and locales. Overall, our results suggest that competition may allow procurement authorities to extract the benefits of discretion while limiting the resultant risks of abuse.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".