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Central Command, Local Hazard and the Race to the Top

Edoardo Di Porto () and Federico Revelli ()

Department of Economics and Statistics Cognetti de Martiis. Working Papers from University of Turin

Abstract: 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.

Pages: 32 pages
Date: 2009-10
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