Replication Data for: Selective Bribery: When Do Citizens Engage in Corruption?
Bibliographic record
Abstract
Corruption often persists not only because public officials take bribes, but also because many citizens are willing to pay them. Yet even in countries with endemic corruption, few people always pay bribes. Why do citizens bribe in some situations but not in others? Integrating insights from both principal-agent and collective action approaches to the study of corruption, we develop and empirically evaluate an analytical framework for understanding selective bribery. Our framework reveals how citizens' motivations, costs, and risks influence their willingness to engage in corruption. A conjoint experiment conducted in Ukraine in 2020 predominantly corroborates our pre-registered predictions. By shedding light on conditions that dampen citizens' readiness to pay bribes, our findings offer insights into the types of institutional reforms that may reduce corruption and ultimately help countries to escape self-reinforcing corruption cycles.
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How this classification was reachedexpand
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.776 |
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; both teacher heads agree on what is shown here.
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".