Datasets of "Fixed Capital and Growth Imperatives: Is Commercial Aviation Trapped in a Treadmill?"
Description
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.
Files
Steps to reproduce
Financial data: The data cleaning process involved key steps to ensure accuracy and consistency before analysis. Missing data points were identified and addressed. In final selected financial data sets (files starting with "Total/ name of variable"), four missing data points were estimated using linear interpolation, assuming a linear trend between adjacent known data points, while one was filled with the last known value. For time-series data, linear interpolation works effectively by assuming a constant rate of change. Data consistency was verified by standardizing formats, units, and currency. All financial figures were converted to USD, and date formats were standardized. Currency exchange rates were primarily sourced from Compustat, with daily currency data aggregated into an average using R programming. If necessary, other sources, such as Investing.com or Oanda.com, were consulted. The data was adjusted for inflation to 2024 constant US dollars using the Producer Price Index (PPI) from the Federal Reserve Economic Data (FRED) database. No outlier or extreme value was found. No duplicate entry was found. Financial data from each subsector and variable were consolidated into a single Excel sheet for organization and accessibility. Three aggregation sheets were created: one for Capital Expenditures (CAPEX), one for Property, Plant, and Equipment - Total (Gross) (PPEGT), and one for Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA). The date range for each variable was determined to ensure a minimum of 30 years of data (24 years for airport operators). For CAPEX, data ranged from 1994 to 2023. For PPEGT, the data spanned 1989 to 2023, and for EBITDA, it covered 1990 to 2023. Data for airport operators was available from 1999 to 2023 across all three variables. Different visual representations were chosen based on the nature of each variable. A stacked area chart was used for CAPEX and PPEGT to show cumulative growth and distribution over time. A line chart was used for EBITDA to display trends and fluctuations in profitability across companies. Resource data: For resource data, the time period was split into three sections: 1988–1999, 2000–2011, and 2012–2023. Data for each year and variable was recorded for all reporting countries, ensuring that the global export volume for each year was accurately aggregated. No missing data points were found. The original unit of measurement, net weight in kilograms, was converted into metric tons for consistency and ease of comparison. In files starting with "Total data / name of resource variable," 1st tab was 1988-1999, 2nd tab was 2000-2011 and 3rd tab was 2012-2023. Resource data from 1988 to 2023 was then consolidated into a 4th tab for adjustment. "Total/Selected data - Comtrade" is a file that consolidates all data from the fourth tab of each resource variable sheet, enabling the creation of a linear chart with each variable represented by a separate line.