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Determinants of corruption: Can we put all countries in the same basket?

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  • Blaise Gnimassoun
  • Joseph Keneck Massil

Abstract

This paper aims to study the determinants of corruption by examining specificities relating to the region and the level of economic development. Starting from a cross-sectional study on 130 countries, we rely on the Bayesian Model Averaging (BMA) approach to address the issue of model uncertainty and identify the key determinants of corruption according to the level of development and the region. Our results highlight the need for specific remedies in the fight against corruption given the regional, sociocultural, economic and institutional specificities. Indeed, the key determinants of corruption in sub-Saharan Africa are not the most relevant in the East Asia and Pacific region. Similarly, the most important determinants in developed countries are not the most worrying in developing countries.

Suggested Citation

  • Blaise Gnimassoun & Joseph Keneck Massil, 2016. "Determinants of corruption: Can we put all countries in the same basket?," EconomiX Working Papers 2016-12, University of Paris Nanterre, EconomiX.
  • Handle: RePEc:drm:wpaper:2016-12
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    References listed on IDEAS

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    1. Aidt, Toke & Dutta, Jayasri & Sena, Vania, 2008. "Governance regimes, corruption and growth: Theory and evidence," Journal of Comparative Economics, Elsevier, vol. 36(2), pages 195-220, June.
    2. Winford H. Masanjala & Chris Papageorgiou, 2008. "Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 671-682.
    3. Swamy, Anand & Knack, Stephen & Lee, Young & Azfar, Omar, 2001. "Gender and corruption," Journal of Development Economics, Elsevier, vol. 64(1), pages 25-55, February.
    4. David E. Bloom & Jeffrey D. Sachs, 1998. "Geography, Demography, and Economic Growth in Africa," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(2), pages 207-296.
    5. Gnimassoun, Blaise, 2015. "The importance of the exchange rate regime in limiting current account imbalances in sub-Saharan African countries," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 36-74.
    6. Glaeser, Edward L. & Saks, Raven E., 2006. "Corruption in America," Journal of Public Economics, Elsevier, vol. 90(6-7), pages 1053-1072, August.
    7. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
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    Cited by:

    1. Omar A. Guerrero & Gonzalo Castañeda, 2021. "Does expenditure in public governance guarantee less corruption? Non-linearities and complementarities of the rule of law," Economics of Governance, Springer, vol. 22(2), pages 139-164, June.
    2. Omar A. Guerrero & Gonzalo Casta~neda, 2019. "Does Better Governance Guarantee Less Corruption? Evidence of Loss in Effectiveness of the Rule of Law," Papers 1902.00428, arXiv.org.

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    More about this item

    Keywords

    Corruption; Political Economy; Public Economics; Bayesian Model Averaging; Cross-Sectional Models.;
    All these keywords.

    JEL classification:

    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State
    • P35 - Political Economy and Comparative Economic Systems - - Socialist Institutions and Their Transitions - - - Public Finance
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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