The ESGI (Environmental, Social and Governance Index) is a unique tool that encompasses three major issues in risk analysis, aggregated to a global scoring through a weighted geometric mean.
These concerns are weighted as follows: environment (30%), human rights (50%) and health & safety (20%).
Advanced research has allowed Global Risk Profile to offer maximum coverage for each sub-index:
183 countries are scored in all three sub-indexes and are therefore included in the global scoring of the ESG Index.
The ESGI relies on numerous entities for their provision of raw data, namely:
Sonia Thurnherr
Head of Development Department
info@globalriskprofile.com
France recently pioneered the adoption of a binding law relative to the duty of care (“devoir de vigilance” ) of parent and ordering companies (No. 2017-399). Concerned entities are required to map their risks with regards to the environment, human rights and health & safety issues. A year long R&D procedure on the ESGI was specifically conducted to offer a tailor-made solution in relation to new legal requirements.
Based on international references such as the Universal Declaration of Human Rights (UDHR), the Global Compact, the 8 ILO fundamental conventions and the United Nations Conference on Environment and Development (UNCED), the ESG Index (ESGI) is a unique measure focusing on socially responsible conducts incorporated in an easy to use tool.
The ESG Index is composed of 65 variables based on datasets that are exclusively borrowed from internationally recognized entities.
The ESGI 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.
[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
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 ESG issues, and 100 corresponds to the highest risk of ESG issues.
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.
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