Why Are Some Public Officials more Corrupt Than Others?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using detailed Peruvian data measuring bribery, I assess which types of public official are most corrupt and why. I distinguish between the bribery rate and the size of bribes received, and seek to explain the variation in each across public institutions. The characteristics of officials' clients explain most of the variation for bribery rates, but none for bribe amounts. A measure of the speed of honest service at the institution explains much of the remaining variation for both bribery rates and amounts. The results indicate that the bribery rate is higher at institutions with bribe-prone clients, and that bribery rates and bribe amounts are higher where clients are frustrated at slow service. Faster and better service would reduce corruption. Overall, the judiciary and the police are by far the most corrupt institutions.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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