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Central Command, Local Hazard and the Race to the Top

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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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  • Di Porto Edoardo & Revelli Federico, 2009. "Central Command, Local Hazard and the Race to the Top," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200909, University of Turin.
    • Handle: RePEc:uto:dipeco:200909
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures

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