Central command, local hazard and the race to the top
AbstractThis 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 InfoPaper provided by Institut d'Economia de Barcelona (IEB) in its series Working Papers with number 2009/26.
Length: 36 pages
Date of creation: 2009
Date of revision:
vehicle taxation; spatial auto-correlation; censored data;
Other versions of this item:
- 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.
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures
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