A critique on the Corruption Perceptions Index: An interdisciplinary approach

The purpose of this study is to test the assumptions of the Corruption Perceptions Index (CPI) and examine the causal relationships among its data sources for improvement priorities. In doing so, we use a novel interdisciplinary methodology including cluster analysis, classification analysis, partial least squares structural equation modeling and importance-performance map analysis. Our methodology enables policymakers to identify the critical data sources that a given country should focus on in order to improve its position in the CPI ranking relative to other countries. Based on corruption perceptions of 176 countries in the 2016 CPI, our results provide evidence against the CPI's assumptions, as individual data sources have unequal effects on the CPI and exhibit the causal interrelations among one another. Corruption perceptions are not homogeneous across countries, with developed countries showing lower levels of perceived corruption than emerging countries. The presence of synergistic effects among the CPI's data sources suggests that national policymakers consider multiple data sources of the CPI for decision-making process rather than simply focus on any single one of these data sources or their equally-weighted aggregation. Moreover, policymakers should allocate the country's resources – which are often limited – with the first priority to improving the data source score of the Economist Intelligence Unit Country Risk Ratings, the critical driver of the CPI. Interestingly, the modified CPI which removes insignificant data sources outperforms the non-modified CPI in terms of the goodness-of-fit assessment, the unbiasedness and the association with the World Bank's Control of Corruption.