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Central command, local hazard and the race to the top

Author

Listed:
  • Edoardo Di Porto

    (LABOR, Centre for Employment Studies)

  • Federico Revelli

    (University of Torino)

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.

Suggested Citation

  • Edoardo Di Porto & Federico Revelli, 2009. "Central command, local hazard and the race to the top," Working Papers 2009/26, Institut d'Economia de Barcelona (IEB).
    • Handle: RePEc:ieb:wpaper:doc2009-26
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    References listed on IDEAS

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    More about this item

    Keywords

    vehicle taxation; spatial auto-correlation; censored data;
    All these keywords.

    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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