Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows

Abstract

We use a spatial econometric extension of the traditional regression-based gravity model introduced in LeSage and Pace (2005) to model commodity flows between 35 regions in Austria. Our focus is on a formal methodology for incorporating information regarding the highway network into the spatial connectivity structure of the spatial autoregressive econometric model. We show that our simple approach to incorporating this information in the model produces improved model fit and higher likelihood function values. The model introduced by LeSage and Pace (2005) accounts for spatial dependence in the origin-destination flows by introducing a spatial connectivity matrix that allows for three types of spatial dependence in the flows from origins to destinations. We modify this origin-destination connectivity structure to include information regarding the presence or absence of a major highway artery that passes through the regions. Empirical estimates of the relative importance of the different types of origin-destination connectivity between regions indicates that the strongest spatial autoregressive effects arise when both origin and destination regions have neighboring regions located on the highway network. This is an intuitively plausible result that should be viewed in the context of past regression-based origin-destination gravity models that assume the flows between origin-destination pairs making up the sample data observations are independent. Our approach builds on that of LeSage and Pace (2005) to provide a formal spatial econometric methodology that can easily incorporate network connectivity information in spatial autoregressive models that can be estimated using slightly altered conventional algorithms that are widely available.