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Central Command, Local Hazard and the Race to the Top

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  • Di Porto Edoardo

    ()

  • Revelli Federico

    ()
    (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.

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Bibliographic Info

Paper provided by University of Turin in its series Department of Economics and Statistics Cognetti de Martiis. Working Papers with number 200909.

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Length: 32 pages
Date of creation: Oct 2009
Date of revision:
Handle: RePEc:uto:dipeco:200909

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