Assessing productive efficiency in Nigerian airports using Fuzzy-DEA

Abstract

Performance analysis has become a vital technique for managing airport practices. However, most DEA models applied to airports assume that inputs and outputs are known with absolute precision. Here, we use Fuzzy-DEA models to capture vagueness in input and output measurements obtained from Nigerian airports. These results are subsequently treated by bootstrapped truncated regressions to control the random effects inherent to any sample. Results indicate that the joint use of bootstrapped regressions and FDEA models leads to more robust results, in the sense that fewer significant contextual variables are identified as efficiency drivers. When controlling for fuzziness and randomness, capacity cost was found to be the only significant variable, in addition to a learning component represented by trend. Policy design for Nigerian airports should focus simultaneously on third-party capacity management – such as privatization - while fostering continuous improvement practices to sustain the learning curve.