Jobs and Punishment: Public Opinion on Leniency for White-Collar Crime
Bibliographic record
Abstract
Governments routinely offer deals to companies accused of white-collar crimes, allowing them to escape criminal charges in exchange for fines or penalties. This lets prosecutors avoid costly litigation and protects companies' right to bid on lucrative public contracts, which can reduce the likelihood of bankruptcies or layoffs. Striking deals with white-collar criminals can be risky for governments because it could affect the perceived legitimacy of the legal system. This article explores the conditions under which the general public supports leniency agreements. Building on theoretical intuitions from the literature, we identify three characteristics that could affect mass attitudes: home bias, economic incentives, and retribution. We conduct a survey experiment in the United States and find moderate support for leniency agreements. Whether the crime occurs on US soil or abroad does not affect public opinion, and the number of jobs that would be jeopardized by criminal prosecution only has a small effect. Instead, survey respondents become much more supportive of a deal when it includes criminal charges for the corporate managers who were personally involved in the alleged wrongdoing. In the court of public opinion, punishing a handful of individuals appears to matter more than saving thousands of jobs.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".