Abstract
Studies about airport operational efficiency models generally disregard the correlation between operational efficiencies and economic drivers. The goal of this study is, firstly, to isolate and detail the key economic drivers and then find their efficient frontier. The methodology employed was Data Envelopment Analysis (DEA) as a non-parametric and linear programming model. It provides relative measures of efficiency using multiple inputs and outputs for a given Decision-Making Unit (DMU) without requiring a prior production function. The number of variables in this study was limited in function of the DMUs analyzed, which consisted of the following Brazilian airports: Congonhas Airport (CGH), Guarulhos International Airport (GRU) and Viracopos International Airport (VCP). Two of the airports, GRU and VCP, were found to be efficient considering this study’s combination of very limited variables, meaning that theses airports, from this isolated standpoint, are maximizing their commercial, passenger parking and marketing revenues, given their terminal area and the number of yearly passengers.
Highlights
Various studies have examined airport operational performance or efficiency, fewer authors have focused on determining an efficient frontier that combined operational efficiencies and economic drivers
As far as The results are concerned, to Barros (2008) and Barros and Dieke (2008), who found significant variances in efficiencies in both studies mainly due to scale and location, the present study indicates the need for a clustered Data Envelopment Analysis (DEA) approach and further statistical analysis
This study provides the basis for various enhancements in efficient frontier analysis, including data selection, statistical tools and a DEA correlation analysis
Summary
Various studies have examined airport operational performance or efficiency, fewer authors have focused on determining an efficient frontier that combined operational efficiencies and economic drivers. Financial ratios such as Return on Assets and EBITDA Margins can be a natural solution to the problems of limited number of airports in the sample and their significant differences in terms of physical and economic dimensions and operation profiles. In this respect, Brazilian airports were analyzed (Fernandes, Pacheco and Braga, 2014) concerning a broad measure of profitability against their number passengers and the potential of their cities. It provides relative measures of efficiency, using multiple inputs and outputs for a given Decision Making Unit or DMU without requiring a prior production function
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