Are spatial effects important while assessing airport efficiency? – A case of India

Abstract

Numerous studies have attempted to assess airport efficiency by economic assessment tools focusing on financial aspects and productivity, while neglecting the role of location and spatial effects. However, it is commonly observed that data collected are not independent, but spatially related. Thus, it becomes important to understand the spatial correlations among the airports especially in India where the connectivity of airports is majorly complementary than competitive. For airports, two types of spatial effects are widely acknowledged – spatial heterogeneity and spatial dependence. Spatial econometric models allow accounting for these spatial effects among the observations. This research emphasizes discovering spatial effects in Indian airports’ partial factor productivity (PFP) to determine the spatial autocorrelation – global (Moran’s I index) and local (Geary’s C statistic). The tests reveal evidence of significant spatial effects in data. The paper also analyses the air-traffic movements (ATM) using spatial regression to determine the presence of spatial heterogeneity by models like Ordinary Least Squares (OLS), Spatial Durbin Model (SDM), Spatial Autoregressive Model (SAR), Spatial Lag X Model (SLX), and Spatial Error Model (SEM). The main contributions of this paper are the proven necessity of incorporation of spatial effects into airport efficiency assessment and the proposed methodological framework for spatial analysis.