Estimating the Efficiency of Us Airports with a Dynamic Two-Stage DEA Model

Globalization and increased competition have generated business pressures on the aviation industry. Information regarding airport efficiency helps airlines select more productive airports for increasing revenues and assists governments in allocating resources for airport improvement efforts. Understanding and ultimately enhancing the efficiency of airports is particularly timely and relevant given the aviation industry’s unprecedented downturn in profits due to the COVID-19 pandemic. This study utilizes a dynamic two-stage data envelopment analysis where the relative efficiency of 50 US airports from 2009 to 2018 is first determined based on input costs, revenues, undesirable outputs, and financial carry-overs. A truncated bootstrapped regression is then applied to assess the potential factors influencing efficiency among analyzed airports. Results suggest that 17 large hubs and 13 medium hubs are on the efficient frontier based on the average efficiency score for our 10 years of analysis. However, some inefficient airports improve their operations and obtain the efficient frontier over the study period. The least efficient airports in the sample are all large hubs, which need to reduce their input costs and debt, improve infrastructure investment, and lower flight delays and emissions to enhance their efficiency. Results also indicate that airport efficiency is influenced by regional demographics (i.e., local economic status, population density, the percentage of the high-skilled labor force) and airport operating features (i.e., international connections, fleet size, and hub size). Our findings provide updated information on airport efficiency and relevant implications regarding operational strategies for strengthening efficiency.