Encompassing as much as 196 countries, the GCI stands out for its global approach. Its results display the corruption risk exposure deriving from both the public and private sectors. The GCI also includes issues related to white collar crimes and more specifically to money-laundering and terrorism financing.
The GCI relies on various methods for collecting data reflecting the variety of ways available to estimate corruption. This process allows to further the objective of providing our users with estimates as close as possible to real values.
The GCI is composed of two sub-indexes related to corruption and white collar crimes:
4 indicators are considered to measure corruption, weighted as follows:
Country characteristics are meant to capture prevention mechanisms, related effects, causal effects and consequential effects, with the objective of unearthing latent corruption information. This indicator aggregates results related to 4 different indicators:
The remaining weight (30%) is dedicated to White Collar Crimes, a measure based on the Basel Institute’s AML Index and a set of white collar crime indicators.
The Global Corruption Index relies on numerous entities for their provision of raw data, namely :
Lead Data Scientist
In order to provide compliance officers with the appropriate tools, Global Risk Profile has developed a unique and robust measure of worldwide corruption in compliance with current legal requirements such as the Foreign Corrupt Practices Act (FCPA), the Bribery Act and the recent French law No. 2016-1691 relative to transparency, the fight against corruption and the modernization of economic life, also referred to as “Sapin II“.
The Global Corruption Index is composed of 43 variables constructed based on datasets that are exclusively borrowed from internationally recognized entities.
The GCI follows a strict methodology:
A number of criteria were considered during the selection process, detailed in the downloadable technical methodology
The processing of missing data is handled on a case-by-case basis depending on the structure of the datasets.
For some variables, no PMM imputation was performed and only true values were considered in the analysis. This is due to the structure of the data and the absence of correlation with other variables. In the case of a missing value, the algorithm proportionally redistributes the according weight to variables measuring the same indicator.
Aside from binary variables, all datasets were tested for skewness, then transformed and recoded if necessary. The mean and standard deviation is calculated and all variables are then standardized, to allow for a proper aggregation in the global scoring. Several normalization methods exist. The one used here is that of z-scores, which converts datasets to a common scale with a mean of zero and a standard deviation of one.
The aggregation process converts all data points to a scale of 0-100, where 0 represents the lowest risk of corruption and white collar crimes, and 100 corresponds to the highest risk of corruption and white collar crimes. Each country’s global score is then calculated following the weights previously presented.
Based on the n datasets obtained from the multiple imputation process, a standard error and a 90 percent confidence interval are calculated for each dataset to reflect the variance around the different scores.