About the Global Corruption index

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:

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]).

Case deletion

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.