Corruption and Default Risk: Global Evidence
Bibliographic record
Abstract
ABSTRACT The extant literature explores the consequences of corruption on firms’ growth and survival. However, its impact on default risk remains unexplored. On the basis of a sample of 189,109 firm‐years from 2004 to 2021 across 47 countries, our study reveals that a one standard deviation increase in corruption is associated with an 11.3% increase in default risk. Our channel analysis identifies information asymmetry and managerial risk‐taking as key mechanisms through which corruption influences default risk. This adverse effect is particularly pronounced in countries with opaque information environments, weak governance frameworks and inadequate external monitoring of firms. We further highlight the detrimental impact of corruption on firms’ borrowing costs and banks’ loan performance. Our study emphasizes the importance of enhancing information transparency and implementing stringent control mechanisms as a basis of mitigating corruption's detrimental effects across a range of different socio‐political contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".