The impact of corruption on economic growth in developing countries and a comparative analysis of corruption measurement indicators

Abstract

Although corruption has attracted researchers’ attention for more than 30 years, it remains one of the most significant political challenges all countries face. Even though corruption measures have improved, they lack reliability and clarity. Two aspects of corruption are examined in this paper: a) its measurement and b) its effects on the economic performance of 83 developing countries in the period 2012–2018 with AR (1) and FM-OLS data processing techniques. It provides an extensive reference for and critical assessment of different corruption index approaches, focusing on the already known and widespread indicators. Furthermore, it refers to the measures most suited for statistical analyses regarding perceptions and experiences. In addition, the study’s empirical results show that corruption hinders the economic growth of those developing countries. Different levels of corruption impact economic growth in different regions; specifically in Latin American countries, corruption impacts positively on economic growth or vice versa; in the other regions, it is negative. Finally, investment, human development, government growth, and institutional quality play essential roles in economic growth.

Keywords:

• Corruption indexes
• governance
• perception
• economic growth
• regional analysis

JEL classification:

• D73
• E60
• O4
• C23
• R11.

Acknowledgements

The authors are very grateful to the anonymous reviewers and the journal’s editorial team for providing constructive comments to enhance the quality of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. A detailed description of the CPI index with its disadvantages and advantages is given in Appendix B.

2. A comparison of the above basic indicators and a detailed listing of the criticism of second-generation indicators are provided in Appendix C.

3. Detailed information about them and a presentation of the advantages and disadvantages of the 2nd and 3rd generation indicators, in contrast, are given in Appendix D.

4. According to Nickell (1981) when panel data models with fixed effects and lagged dependent variables are estimated by the standard within estimator if the time dimension, T, is small, bias depends on the 1/T and disappears as T grows large.

5. This picture is slightly different in regions such as Sub-Saharan Africa (SSA), where the negative relationship is weak, as well as in Latin American countries (AME), where the relationship turns positive (grease the wheels).

Additional information

Funding

The authors received no direct funding for this research.