About the Global Corruption index

Data-driven metrics at the core of risk evaluation on a national scale

A composite index

Encompassing as much as 199 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.

Indicators

The GCI is composed of two sub-indexes related to corruption and white collar crimes:

Corruption

4 indicators are considered to measure corruption, weighted as follows:

    1. The ratification status of key conventions (OECD, UN), 15%
    2. The level of perceived public corruption (Transparency International’s Corruption Index, World Bank data, World Justice Project Organisation data), 25.5%
    3. The reported experience of public and private corruption (Transparency International’s Global Corruption, Barometer, World Bank’s Enterprise Survey), 17%
    4. A selection of country characteristics closely linked to corruption, 42.5%

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:

    1. Citizen’s voice and Transparency
    2. Government Functioning and Effectiveness
    3. Legal Context
    4. Political Context
White collar crimes

The remaining weight (20%) is dedicated to White Collar Crimes, a measure based on (1) the Basel Institute’s AML Index and (2) the membership to FATF and / or related bodies

Sources

The Global Corruption Index relies on numerous entities for their provision of raw data, namely :

    • The UN
    • The OECD
    • The World Bank
    • The FATF
    • Transparency International
    • The World Justice Project Organisation
    • The Economist Intelligence Unit (EIU)
    • The Basel Institute on Governance
    • The International Budget Partnership (IPB)
Research & development

Sonia Thurnherr
Lead Data Scientist

General framework

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“.

 

glass globe on water

Methodology

The Global Corruption Index is  composed of 28 variables constructed based on datasets that are exclusively borrowed from internationally recognized entities.

The GCI follows a strict methodology:

selection process

A number of criteria were considered during the selection process, detailed in the downloadable technical methodology

Missing data

The processing of missing data is handled on a case-by-case basis depending on the structure of the datasets.

    • In the case of time series datasets with visible trends, we proceed with a linear extrapolation from the five last available years. This method allows to estimating parameters based on real past values.
    • The second approach used is the method of the Last Observation Carried Forward (LOCF), which is a common statistical approach for time series data that consists in imputing the last available observation. Similar to the first method, only the last five available years are considered.
    • The last approach is that of multiple imputations through Predictive Mean Matching (PMM).  This approach allows us to preserve the distributions in the data and ensures that imputed values are plausible as it fills in values from real observations (Vink et al., 2014[1]). PMM provides a random value from a donor, based on the closeness of the regression-predicted values of the donor , with that of the recipient . This implies that linear regressions are not used to generate imputed values but rather to determine the donor (Schenker, N. & Taylor, J.M.G., 1996[2]).
 
[1] Vink, G., Frank, L. E., Pannekoek, J., and van Buuren, S. (2014). Predictive mean matching imputation of semicontinuous variables. Statistica Neerlandica. 68(1). 61-90
[2] Schenker, N., & Taylor, J. M. G. (1996). Partially parametric techniques for multiple imputation. Computational Statistics & Data Analysis, 22(4), 425–446
Case deletion

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.

standardization

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.

aggregation

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.

measure of uncertainty

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.