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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Paper provided by Institut d'Economia de Barcelona (IEB) in its series Working Papers with number
2009/26.
Find related papers by JEL classification: C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures
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