Does Public Corruption Affect Bank Failures? Evidence from the United States
Bibliographic record
Abstract
Corruption influences firm behavior and performance even in relatively transparent countries like the United States. In this paper, we examine whether corruption at the state level affected bank failures during the subprime mortgage crisis. Our measure of corruption is the number of corruption convictions of government employees (adjusted for population) based on the Public Integrity Section (PIN) reports from the Department of Justice, capturing the degree of “public corruption” in the US. After disaggregating the data based on bank size and geography, we find that corruption is associated with more bank failures for smaller banks and fewer bank failures for banks located in the South. This research marks a pioneering attempt to examine the connection between corruption and bank failures while underscoring the significance of political risk for financial institutions. Given the recent setbacks experienced by Silicon Valley Bank, Signature Bank, and First Republic Bank, this research provides valuable recommendations for policymakers. The findings suggest the need for regulators to mandate greater transparency regarding banks’ exposure to undisclosed risks, such as political risk. It also advocates for implementing internal control mechanisms to curb corrupt activities.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".