Bribery: Who Pays, Who Refuses, What Are the Payoffs?
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it