Bibliographic record
Abstract
We provide a theoretical framework for understanding when an official angles for a bribe, when a client pays, and the payoffs to the client's decision.We test this framework using a new data set on bribery of Peruvian public officials by households.The theory predicts that bribery is more attractive to both parties when the client is richer, and we find empirically that both bribery incidence and value are increasing in household income.However, 65% of the relation between bribery incidence and income is explained by greater use of officials by high-income households, and by their use of more corrupt types of official.Compared to a client dealing with an honest official, a client who pays a bribe has a similar probability of concluding her business, while a client who refuses to bribe has a probability 16 percentage points lower.This indicates that service improvements in response to a bribe merely offset service reductions associated with angling for a bribe, and that clients refusing to bribe are punished.We use these and other results to argue that bribery is not a regressive tax.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".