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Superávit e déficit fiscal dos municípios brasileiros: uma aplicação do modelo de viés de seleção em painel [Fiscal surplus and déficit of Brazilian municipalities: an application of the panel bias selection model]

Author

Listed:
  • Sergio Naruhiko Sakurai

    (FEA-RP)

Abstract

This paper aims at investigating the primary fiscal balance of Brazilian municipalities from 1999-2007 by employing the bias selection methodology for in-panel data proposed by Wooldridge (1995). More specifically, the study assumes that fiscal surpluses and deficits should not be taken as purely random events; alternatively, it assumes the existence of a first stage that determines the existence of a fiscal surplus or deficit. The results, controlled for the presence of sampling bias, show that fiscal surpluses and deficits are partially influenced by the fiscal characteristics and demographic structure of the population. However, political traits do not exert any significant influence on the fiscal balances of Brazilian municipalities.

Suggested Citation

  • Sergio Naruhiko Sakurai, 2014. "Superávit e déficit fiscal dos municípios brasileiros: uma aplicação do modelo de viés de seleção em painel [Fiscal surplus and déficit of Brazilian municipalities: an application of the panel bias se," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 24(3), pages 517-540, September.
    • Handle: RePEc:nov:artigo:v:24:y:2014:i:3:p:517-540
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    More about this item

    Keywords

    Brazilian municipalities; fiscal balance; panel data econometrics; sample selection bias;
    All these keywords.

    JEL classification:

    • H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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