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Panel Data Analysis of the Time-Varying Determinants of Corruption

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  • Guillaume Fréchette

Abstract

There is a long history of models attempting to identify the causes of corruption, yet empirical analysis is complicated. Not only is data difficult to obtain and often available only for few countries and a limited number of years, but such estimation involves inherent complexities. This paper focuses on the use of panel data techniques to better identify factors that affect bureaucratic corruption. Furthermore, this paper identifies an endogeneity problem which arises in the analysis of the causes of corruption, and a new instrumental variable is proposed to solve it. To help in this endeavor, a data set is employed which provides information for as many as 135 countries over a span of sixteen years. Results show that neglecting the endogeneity problem leads to severely biased results. Using panel data techniques reveals that the availability of rents is a crucial determinant of corruption and that previous research may have underestimated the economic significance of rents on corruption. Furthermore, corruption is shown to be procyclical. Depuis longtemps, des modèles sont utilisés dans le but de déterminer les causes de la corruption. Toutefois, l'analyse empirique demeure complexe. Qui plus est, les données sont difficiles à recueillir et couvrent souvent un nombre restreint de pays et une période limitée. Ce genre d'évaluation présente aussi des complexités inhérentes. Le présent document met l'accent sur le recours à des techniques de données de panels dans le but de mieux connaître les facteurs qui influent sur la corruption bureaucratique. En outre, cette analyse souligne le problème d'endogénéité qui ressort de l'analyse des causes de la corruption et propose une nouvelle variable instrumentale permettant de contrer celui-ci. Pour faciliter la démarche, ce document utilise un ensemble de données fournissant des renseignements sur au moins 135 pays et pour une période de seize ans. Les résultats indiquent que si le problème d'endogénéité n'est pas pris en compte, les résultats sont sérieusement biaisés. De plus, la corruption est décrite comme étant procyclique.

Suggested Citation

  • Guillaume Fréchette, 2006. "Panel Data Analysis of the Time-Varying Determinants of Corruption," CIRANO Working Papers 2006s-28, CIRANO.
    • Handle: RePEc:cir:cirwor:2006s-28
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    File URL: https://cirano.qc.ca/files/publications/2006s-28.pdf
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    References listed on IDEAS

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    Cited by:

    1. Liam Wren-Lewis, 2015. "Do Infrastructure Reforms Reduce the Effect of Corruption? Theory and Evidence from Latin America and the Caribbean," The World Bank Economic Review, World Bank, vol. 29(2), pages 353-384.
    2. Ghulam Shabbir & Mumtaz Anwar, 2007. "Determinants of Corruption in Developing Countries," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 46(4), pages 751-764.
    3. Patrawart, Kraiyos, 2008. "Can Equality in Education Be A New Anti-Corruption Tool?: Cross-Country Evidence (1990-2005)," MPRA Paper 9665, University Library of Munich, Germany.
    4. Berdiev, Aziz N. & Kim, Yoonbai & Chang, Chun-Ping, 2013. "Remittances and corruption," Economics Letters, Elsevier, vol. 118(1), pages 182-185.
    5. Farzanegan, Mohammad Reza, 2009. "Illegal trade in the Iranian economy: Evidence from a structural model," European Journal of Political Economy, Elsevier, vol. 25(4), pages 489-507, December.
    6. Cherkaoui Malki, Sofiane, 2017. "Corruption and Media Concentration: A Panel Data Analysis," MPRA Paper 81073, University Library of Munich, Germany.

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    More about this item

    Keywords

    corruption; endogeneity; income; rents; corruption; endogénéité;
    All these keywords.

    JEL classification:

    • H8 - Public Economics - - Miscellaneous Issues
    • K4 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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