Central command, local hazard and the race to the top
Edoardo Di Porto () and
Federico Revelli ()
No 2009/26, Working Papers from Institut d'Economia de Barcelona (IEB)
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 (search for similar items in EconPapers)
JEL-codes: C23 C25 H72 (search for similar items in EconPapers)
Pages: 36 pages
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Working Paper: Central Command, Local Hazard and the Race to the Top (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:ieb:wpaper:doc2009-26
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