Abstract

This paper investigates the divergence of environmental, social, and governance (ESG) ratings based on data from six prominent ESG rating agencies: Kinder, Lydenberg, and Domini (KLD), Sustainalytics, Moody’s ESG (Vigeo-Eiris), S&P Global (RobecoSAM), Refinitiv (Asset4), and MSCI. We document the rating divergence and map the different methodologies onto a common taxonomy of categories. Using this taxonomy, we decompose the divergence into contributions of scope, measurement, and weight. Measurement contributes 56% of the divergence, scope 38%, and weight 6%. Further analyzing the reasons for measurement divergence, we detect a rater effect where a rater’s overall view of a firm influences the measurement of specific categories. The results call for greater attention to how the data underlying ESG ratings are generated.

Scientific Portfolio AI- Generated Summary

This paper investigates the divergence of ESG ratings from six prominent ESG rating agencies. The authors map the different methodologies onto a common taxonomy of categories and decompose the divergence into contributions of scope, measurement, and weight. The results call for greater attention to how the data underlying ESG ratings are generated.

The authors find that the divergence of ESG ratings is substantial, with an average pairwise correlation of 0.61. They also find that the divergence is driven by differences in scope, measurement, and weight. Scope differences account for 55% of the divergence, measurement differences account for 30%, and weight differences account for 15%.

The authors further decompose the measurement divergence into three components: rater effect, data source effect, and model effect. They find that the rater effect is the largest component, accounting for 60% of the measurement divergence. The data source effect and model effect account for 25% and 15% of the measurement divergence, respectively.

The authors also investigate the impact of ESG rating divergence on portfolio construction. They find that the impact is substantial, with the difference in portfolio performance between the top and bottom quintiles of ESG ratings ranging from 2.7% to 7.4% per year, depending on the weighting scheme used.

The authors conclude that the divergence of ESG ratings is a serious issue that needs to be addressed. They call for greater transparency and standardization in the ESG rating industry, as well as greater attention to how the data underlying ESG ratings are generated. They also suggest that investors should be cautious when using ESG ratings to construct portfolios and should consider using multiple ESG rating agencies to reduce the impact of rating divergence.

Overall, this paper provides a comprehensive analysis of the divergence of ESG ratings and its impact on portfolio construction. The authors’ findings highlight the need for greater transparency and standardization in the ESG rating industry, as well as greater attention to how the data underlying ESG ratings are generated. Investors should be cautious when using ESG ratings to construct portfolios and should consider using multiple ESG rating agencies to reduce the impact of rating divergence.