Euclidean versus network distance in business location: A probabilistic mixture of hurdle-Poisson models

Abstract : While the question of spatial weight matrix specification is now largely discussed in the spatial econometrics literature, the definition of distance has heretofore attracted less attention. The choice of the distance measure is often glossed over, with the ultimate use of the Euclidean distance. This paper investigates this issue in the case of establishments locating in the Paris region. High congestion, speed limits, or physical uncrossable barriers can diminish or totally eliminate the linkage between neighboring areas challenging the choice of the Euclidean distance in representing the spatial effects. To compare the various distance measures, we develop a probabilistic mixture of hurdle-Poisson models for several activity sectors. Each model class uses a different distance definition to capture spillover effects. The following distance measures are considered: Euclidean distance, two road distances (with and without congestion), public transit distance, and the corresponding travel times. Overall, the obtained results are in line with the literature regarding the main determinants of establishments' location. However, we find that for some activity sectors, such as construction, the peak period road travel time for private vehicles is the most likely to correctly capture spatial spillovers, whereas for other sectors, such as real estate, the Euclidean distance slightly prevails. This tends to show that spatial spillovers are channeled by different means, contingently on the activity sector. In addition, we find that the proposed mixture of hurdle-Poisson models that uses several latent classes performs significantly better than the 'pure' hurdle-Poisson models based on a single distance measure, emphasizing the usefulness of our approach.
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Sabina Buczkowska, Nicolas Coulombel, Matthieu de Lapparent. Euclidean versus network distance in business location: A probabilistic mixture of hurdle-Poisson models. 2016. ⟨hal-01377757v2⟩

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