Bribery in the Workplace: A Field Experiment on the Threat of Making Group Behavior Visible
Bibliographic record
Abstract
Can reputational threat among coworkers reduce bribery in organizations? I exploit within- and across-organizational variation in bribery to design and implement a field experiment in the maternity wards of five Moroccan public hospitals. I test whether threatening to reveal information about ward workers’ involvement in bribery to their coworkers dissuades them from taking bribes from patients. Healthcare workers cut back on taking bribes in higher-incidence maternity wards but not in lower-incidence wards. Qualitative data show that bribery’s baseline incidence sets the costs of revealing. Workers tolerate only so much bribery in their wards before they face the negative social consequences of belonging to a work group that takes bribes. They thus correct their behavior when it crosses a threshold. Moreover, ineffective applications of the field interventions betrayed welfare-diminishing effects. I furnish evidence for a novel kind of policy lever against workplace bribery and shed new light on the dynamics of bribery inside organizations. Funding: Funding from different programs at Stanford University—Stanford Interdisciplinary Graduate Fellowship, Abbasi Program for Islamic Studies Summer Research Grant, Graduate Research Opportunity Grant, Sociology Research Opportunity Grant, Stanford Center on Philanthropy and Civil Society Grant, Freeman Spogli Institute’s Mentored Global Research Fellowship, and Stanford Institute for Innovation and Entrepreneurship in Developing Economies Fellowship—are gratefully acknowledged. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2021.15264 .
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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 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".