Financial performance and safety in the aviation industry
Bibliographic record
Abstract
Purpose This paper investigates whether financial factors, which are presumed to influence an airline's maintenance, purchasing, and training policies, are associated with the air carrier's safety performance. Design/methodology/approach In this paper, we employ a series of univariate and multivariate tests (OLS and Poisson regressions) to examine whether an airline's financial well-being as well as a country's legal and economic environment affect the airline's accident rate. Our study is the first to employ an international sample that covers 110 airlines in 26 countries over the period 1990–2009. Findings We document an inverse relationship between the profitability of air carriers and their accident propensity. Other financial variables such as liquidity, asset utilization, and financial leverage also appear to affect an airline's safety record, although these findings do not reach significance in all models. Flight equipment maintenance and overhaul expenditures are negatively related to accident rates. In addition, our results show that country-level variables related to the legal and economic environment have a significant effect on airline safety. Specifically, airlines in countries with strong law enforcement, more stringent regulatory systems, and better economic performance have superior safety performance. A series of robustness tests confirms our results. Originality/value The unique contributions of the study are (1) that it is the first to explore the drivers of safety performance in a cross-country context and (2) that it introduces a novel index of capacity when computing accident rates. By using data from 110 airlines in 26 countries, the study does not only provide insights into the firm-level but also the country-level determinants of an airline’s safety performance. The results of this research should be of interest both to academics and to regulators who develop, oversee, and implement policies targeted at improving aviation safety on a national and supranational level.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".