Central Command, Local Hazard and the Race to the Top
33 Pages Posted: 10 Oct 2009
Date Written: September 28, 2009
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.
Keywords: vehicle taxation, spatial auto-correlation, censored data
JEL Classification: C23, C25, H72
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