ABOUT THE ESG INDEX

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

A composite index

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:

    • Environment: 180 countries
    • Human rights: 189 countries
    • Health & safety: 184 countries

177 countries are scored in all three sub-indexes and are therefore included in the global scoring of the ESG Index.

Indicators

Each sub-index is divided into a number of indicators, as presented below :

Environment

The Environment measure is composed of ratified key conventions (namely the Kyoto Protocol and Paris Agreement) as well as the Environmental Performance Index.

Human rights

Risk level related to Human Rights is notably measured according to the ratification status of 18 key conventions, social rights indicators (related to slavery, child labour, education, housing etc.), civil and political rights indicators (press freedom, minority rights, etc) and collective rights indicators (measures of peace, right to self determination etc.).

HEALTH & SAFETY

The last dimension, Health & Safety, is characterized by health indicators (such as life expectancy, access to drinking water) and safety indicators (safety at work, social protection). An adjustment for spatial inequality (urban / rural) is also considered.

Sources

The ESGI relies on numerous entities for their provision of raw data, namely:

    • The UN
    • The ILO
    • The World Bank
    • The World Economic Forum (WEF)
    • The World Health Organization (WHO)
    • Freedom House
    • The Walk Free Foundation
    • The Fund for Peace
    • The Cato Institute
    • Gallup
    • The US Department of State
    • Reporters sans Frontières
    • Yale and Columbia Universities
    • The Institute for Economics and Peace
    • La Conférence Syndicale Internationale
    • The Heidelberg Institute for International Conflict Research
research & development

Sonia Thurnherr
Lead Data Scientist

General framework

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.

Vintage world map with magnifying glass

Methodology

The ESG Index is  composed of 45 variables based on datasets that are exclusively borrowed from internationally recognized entities.

The ESGI 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 for 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 ESG issues, and 100 corresponds to the highest risk of ESG issues.

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.