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Power-law distribution in the external debt-to-fiscal revenue ratios: Empirical evidence and a theoretical model

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  • Dufrénot, Gilles
  • Paret, Anne-Charlotte

Abstract

This paper provides evidence that the external debt-to-fiscal revenue ratio in emerging countries follows a power-law distribution. Such a distribution reflects the fact that external debt distress or debt crises correspond to extreme events that have been found to happen fairly often. We formally test the hypothesis of a power-law, going further than the usual visual inspection of the distribution of the variable of interest on a doubly logarithmic scale. We also show that such a distribution can be derived from a theoretical model in which uncertainty comes from tax evasion and corruption. Using the framework of an optimal stochastic growth model, we model the external debt-to-fiscal revenue ratio as a diffusion process for which the stochastic steady state distribution is derived using the properties of Itô diffusion processes.

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  • Dufrénot, Gilles & Paret, Anne-Charlotte, 2019. "Power-law distribution in the external debt-to-fiscal revenue ratios: Empirical evidence and a theoretical model," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 341-359.
  • Handle: RePEc:eee:jmacro:v:60:y:2019:i:c:p:341-359
    DOI: 10.1016/j.jmacro.2019.04.002
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    1. Gilles Dufrénot & Carolina Ulloa Suarez, 2019. "Public finance sustainability in Europe: a behavioral model," Working Papers halshs-02356400, HAL.

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    More about this item

    Keywords

    Power-law; Stochastic growth; External debt; Emerging countries;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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