Replication Data for: Transnational Legal Spillover? A Re-Appraisal of the OECD Anti-Bribery Convention
Bibliographic record
Abstract
Can prosecutions by US authorities help spread enforcement of foreign bribery laws to other countries? In this article, we explore this question by re-examining earlier scholarship that found that US prosecutions of foreign corporations under the Foreign Corrupt Practices Act (FCPA) increase the likelihood that the corporation's home state will enforce its own foreign bribery laws. Using a conditional-frailty Cox model that allows us to model foreign bribery enforcement actions as repeat-events, we do not find evidence that FCPA prosecutions lead to sustained increases of foreign bribery enforcement by target countries. We also find that prior results are not robust to the inclusion of an important confounding variable: a country's level of exposure to corruption in their trading partners. Still, while our findings indicate a more limited role of US law enforcement in this area, we nonetheless see many promising avenues for future research on transnational law enforcement and its consequences.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.043 |
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".