Flying safe: The impact of corporate governance on aviation safety
Bibliographic record
Abstract
This study examines the impact of various measures of corporate governance on airline safety, addressing a significant gap in the literature that explores safety performance within the aviation industry. Using data from seventy countries spanning the period from 1990 to 2016, we investigate the relationship between corporate governance quality indicators and airline accident rates while controlling for airlines’ financial health. Our findings suggest that airlines with less qualified and busier directors, as well as those experiencing higher degrees of director succession, are more prone to accidents. Conversely, longer CEO tenure is associated with a lower accident rate. Furthermore, our findings highlight the importance of a well-developed regulatory environment and transportation infrastructure: airlines based in countries with more stringent legal regulations, robust law enforcement, and superior air transport infrastructure exhibit better safety performance. Our research underscores the critical role of corporate governance in ensuring airline safety and emphasizes the significance of regulatory frameworks and infrastructure investments in shaping safety outcomes in the aviation industry. These results carry significant policy implications for aviation safety regulators responsible for developing, overseeing, and implementing policies aimed at improving aviation safety. • Corporate governance in airlines influences safety outcomes. • Director qualifications impact airline accident rates. • CEO tenure correlates with lower airline accident rates. • Stringent legal regulations improve airline safety performance. • Effective governance strategies mitigate aviation accident risks.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".