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ESG, corporate responsibility and sustainable investing now hold a top spot in the financial lexicon. As with beauty, it seems that what constitutes a good ESG performer also lies in the eye of the beholder. Stock picking and portfolio construction are increasingly taking into account ESG criteria. Since there is no exact definition of ‘ESG’ or ‘sustainability’, one might reach different conclusions depending on the data provider. As an example, a recent MIT study found that even the correlation of ESG ratings among a group of six different providers is on average 0.54, with a range from 0.38 to 0.71. The same study shows a correlation for credit ratings at 0.99 between 2 providers. This shows a strong divergence in ESG ratings, that is less common in other kind of ratings such as credit ratings.
The integration of ESG into investment decision making can be done in a variety of ways. A popular method is the ‘best in class’ or ‘worst in class’ selection approach. After ranking the universe by ESG score or rating, the worst or best performing companies are respectively excluded or defined as eligible for investment. Linking this to the above mentioned MIT study, the investment universe can vary depending on the selected provider. Another study from Harvard Business School, states that the largest disagreement among ESG providers occurs when the average ESG rating is high or low relative to median ESG rating (Christensen, Serafeim&Sikochi,2020). This effect will consequently have an even larger impact on this kind of ESG approaches. It means that, depending on the provider, a company could be a top ESG performer in one screening, but at the bottom of another screening, all depending on the selected ESG data provider. Hence, insights into the methodology of the rating agencies and having a strong view on ESG are important to mitigate these challenges. The MIT study also reveals that ‘the divergence in ratings could hamper the ambition of companies to improve their ESG performance, because they receive mixed signals from rating agencies about which actions are expected and will be valued by the market’. Hence, active ownership and constructive dialogue with investees are important elements to consider.
The disagreement over corporate ESG ratings thus presents decision makers with a challenge. But to tackle the issue, it is interesting to discuss why these ratings diverge. The previously-mentioned academic paper from MIT discusses 3 types of divergences, namely scope divergence, measurement divergence and weight divergence. Scope divergence refers to a situation when ratings are not based on the same underlying components. For example, when one rating agency constructs a rating based on carbon emission, waste and lobbying activity and another rating agency does not consider waste, the ratings will differ. Measurement divergence can be defined as differences in the measurement of an underlying component that is part of the rating. For example, a rating agency that wants to quantify employee health and safety might look at the accident rate and another one at the injuries resulting in lost time. This already explains why rating agencies might assess corporates differently. Lastly, the weight divergence is defined as differences in the weightings of components that make up the ESG rating. Interestingly, MIT concludes that mainly scope and measurement divergence cause deviation among ratings from different providers. A last interesting finding is that they find evidence that the measurement divergence might be partly driven by a ‘rater‘ effect. This means that a company that receives a high score in one category is more likely to receive high scores in all other categories of the same rater or provider.
Source: Florian Berg, Julian F. Koelbel, and Roberto Rigobon (2019). Aggregate Confusion: The Divergence of ESG Ratings. MIT Sloan School of Management. The above graph shows the 25 firms with the highest disagreement from the study.
As there is no consensus between rating agencies on the exact manner to rate the ESG profiles of corporates, it is crucial to have a good understanding of how these ratings are constructed, i.e. the methodology behind the ratings. This is important prior to selecting a provider, but also once the provider is selected and the ratings are integrated in the investment process. Using a research driven approach is essential to derive information from these ratings. Furthermore, challenging the data/rating providers and engaging with the rated companies are important steps towards further improving ones view on the most relevant and material ESG characteristics. Therefore, a focus on material ESG topics and data is crucial to achieve high quality ESG integration and construct high performing ESG portfolios.
For both quantitative as well as fundamental SRI or ESG approaches, it is of utmost importance to understand the ratings, how they are constructed and why differences among providers occur. This is key when one wants to combine ratings from different providers (note that regulators are even considering more stringent disclosure requirements for data providers towards financial clients, especially on their methodologies). In addition, those insights have to be combined with rigorous pre-processing and feature engineering to derive high quality input for quantitative models, fundamental research and ESG integration. With all the upcoming new sustainable finance regulation, several ESG metrics will become more objectively quantifiable. This is definitely something that we can only applaud, as it will mitigate some of the current challenges. Together with the proposed EU taxonomy of sustainable activities, more consensus on ESG performance is likely to be expected in coming months and years.
Florian Berg, Julian F. Koelbel, and Roberto Rigobon (2019). Aggregate Confusion: The Divergence of ESG Ratings. MIT Sloan School of Management.
Dane Christensen, George Serafeim, and Anywhere Sikochi (2020). Why is Corporate Virtue in the Eye of The Beholder? The Case of ESG Ratings. Harvard Business School.
Sakis Kotsantonis and George Serafeim (2019). Four Things No One Will Tell You About ESG Data. Harvard Business School.