Trust and Bribery: The Role of the Quid Pro Quo and the Link with Crime
Bibliographic record
Abstract
I study data on bribes actually paid by individuals to public officials, viewing the results through a theoretical lens that considers the implications of trust networks.A bond of trust may permit an implicit quid pro quo to substitute for a bribe, which reduces corruption.Appropriate networks are more easily established in small towns, by long-term residents of areas with many other long-term residents, and by individuals in regions with many residents their own age.I confirm that the prevalence of bribery is lower under these circumstances, using the International Crime Victim Surveys.I also find that older people, who have had time to develop a network, bribe less.These results highlight the uphill nature of the battle against corruption faced by policy-makers in rapidly urbanizing countries with high fertility.I show that victims of (other) crimes bribe all types of public officials more than non-victims, and argue that both their victimization and bribery stem from a distrustful environment.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".