# name
Central command, local hazard and the race to the top

# metadata.dc.description.abstract[].value
This paper explores for the first time the consequences of centrally  imposed local tax limitations on the modelling and estimation of spatial auto-correlation in local fiscal policies, and compares three spatial interaction estimators: a) the conventional maximum likelihood estimator that ignores censoring; b) a spatial Tobit estimator; c) a discrete hazard estimator. Implementation of the above empirical approaches on the case of local vehicle taxation in Italy provides a reasonably coherent picture in terms of the direction and size of the spatial interaction process, and offers a plausible spatial interpretation of the race to the top in provincial vehicle taxes.

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IEB (Institut d’Economia de Barcelona) – Working Papers

# metadata.dc.title[].value
Central command, local hazard and the race to the top