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The Dynamic Relationship Between Corruption—Inflation: Evidence from Panel Vector Autoregression

Author

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  • Seifallah Sassi

    (University of Carthage)

  • Amira Gasmi

    (University of Carthage)

Abstract

This paper aims to explain the mixed causality nexus between corruption and inflation. For that, we apply a panel vector autoregression model on a large sample of 180 countries over the period 1996–2014. Using two corruption indexes and subsample estimations, results provide evidence that the inflation—corruption nexus is bidirectional. The causal effect is more important from corruption to inflation. Interactions remain significant but heterogeneous across subsamples with different income levels. The corruption effect is persistent only in low—middle income economies and its adverse effect on inflation is weaker in high-income economies. The two-way relationship between inflation and corruption reflects the inability to control inflation and the situation of the poverty trap in some countries.

Suggested Citation

  • Seifallah Sassi & Amira Gasmi, 2017. "The Dynamic Relationship Between Corruption—Inflation: Evidence from Panel Vector Autoregression," The Japanese Economic Review, Springer, vol. 68(4), pages 458-469, December.
    • Handle: RePEc:spr:jecrev:v:68:y:2017:i:4:d:10.1111_jere.12134
    DOI: 10.1111/jere.12134
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    E31; P44; D73; C33;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • P44 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - National Income, Product, and Expenditure; Money; Inflation
    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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