Datasets of "Fixed Capital and Growth Imperatives: Is Commercial Aviation Trapped in a Treadmill?"
Bibliographic record
Abstract
Financial Data This study sources financial data from Wharton Research Data Services (WRDS), specifically the Compustat Fundamentals database. Compustat North America covers U.S. and Canadian publicly traded companies, while Compustat Global includes firms outside North America. The dataset includes historical financial data from 1950 onward. Companies in the commercial aviation sector were identified using WRDS’s “Find Companies” tool and categorized into five sub-sectors: Airplane Manufacturers, Suppliers, Maintenance, Airlines, and Airport Operators. The selected financial variables include: Capital Expenditures (CAPX): Funds used for additions to property, plant, and equipment (PPE), excluding acquisitions. Property, Plant, and Equipment - Total (PPEGT): Gross valuation of tangible fixed assets used in operations. Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA): A measure of operational profitability before reinvestment in fixed capital. The financial data reveals a consistent rise in capital expenditures and tangible fixed assets across five commercial aviation sub-sectors, reflecting increased investments in infrastructure and equipment. EBITDA has also grown significantly, with airlines and maintenance companies experiencing the highest surges, indicating that firms must generate larger operational surpluses to sustain expanding fixed capital investments. To adjust all financial data to 2024 constant US dollars, the Producer Price Index (PPI) from the Federal Reserve Economic Data (FRED) database was used. The PPI, dating back to 1912, reflects wholesale price changes, ensuring financial figures remain comparable over time. Trade Data Global trade data was obtained from the UN Comtrade Database using the Harmonized System (HS) classification for exports. Selected categories highlight aviation’s material footprint beyond fuel consumption: Aircraft tires (HS 401130) – Maintenance and wear-related material flows. Aircraft engines (HS 840710) – Technological propulsion components. Propellers and rotors (HS 880310) – Key aerodynamic components. Aircraft seats (HS 940110) – Passenger-related infrastructure. The resource data highlights a sharp rise in global exports of key aircraft components from 1988 to 2023. Aircraft seating saw the highest growth, increasing 33-fold, while aircraft tires and propellers expanded 21-fold. Piston engine exports rose 14-fold. This surge reflects rising global demand for aviation parts, driven by fleet expansion, maintenance needs, and the growing complexity of aircraft systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".