Divergence of ESG Ratings - Views by a Professional ESG Researcher

Recently I came across this research paper about “Aggregate Confusion: The Divergence of ESG Rating” by Florian Berg, Julian F. Koelbel, and Roberto Rigobon. It takes on an issue that is faced by every investor who wishes that his funds are channeled to fund companies that are ESG friendly. Today in this article I, A professional ESG researcher and will talk on this topic from both an investor and an analyst perspective who researches companies to assess their involvement in ESG.

This article is an attempt to collate all the important points from the said research paper and present them in a layman’s language.

The 50-page research paper coins two very important terms – “aggregate confusion” and “rater effect”.

According to the researchers the correlation between five prominent ESG rating agencies around the globe - KLD, Sustainalytics, Video-Eiris, Asset4, and RobecoSAM has a correlation of 0.61 for ESG ratings provided by them, on the other hand, Moody’s and S&P’s credit ratings are correlated at 0.99.

Now when a decision-maker sees this drastic difference in ratings provided by ESG rating providers, it becomes extremely difficult for them to take the correct decision. This confusion is termed as “aggregate confusion” by the researchers, now the question here is when all the raters (ESG rating providers) are using the same source, that is public disclosures by companies they rate why are their ratings different?

On investigating they found three things that drive these differences – Scope divergence, Weight divergence, and Measurement divergence.

Let’s understand each:
Scope divergence – When one company is considering GHG emissions, employee turnover, human rights, and lobbying in its scope, others might not include lobbying.
Weight divergence – When one company might give human rights more weight while calculating the final score others might give lobbying a greater weightage.
Measurement divergence – For one indicator, let’s say a firm’s labor practices could be evaluated based on workforce turnover, or by the number of labor-related court cases taken against the firm, which again leads to different ratings.

This three intertwined and every rater using its own algorithm to rate companies gives a greater difference in the final ESG rating.

Another concept coined by the researcher is the "rater effect", which essentially means if the rating agencies are rating the companies higher in one aspect it is highly likely that it will rate the companies on a higher side in other indicators as well.

My Take
Being an ESG Researcher, I can totally agree with the concept of “aggregate confusion”, although Scope divergence is very unlikely to happen because for most indicators of EU taxonomy, the regulator has already specified what has to be taken and whatnot.
The other two - Weight divergence and Measurement divergence are something that can happen more often since every rater is allowed to use their own method.

According to me the only possible solution that can solve this issue will be EU taxonomy directing companies to use a certain method to give weights to each indicator and guiding them on what to use as a measure for an indicator.

Also, the rater effect is something I can't agree on because I don't recall something like this happening in my organization. I may be wrong, not denying that but any discussion is most welcome.

Above mentioned solutions may seem very simple and easy but it becomes very difficult to get all the ESG rating agencies on board.

However, it is in the best interest of all participating parties (Regulator, ESG rating agencies, investors, companies that are being rater) that the divergence between the ratings provided by all raters comes to a minimum.

Do let us know your thoughts on the above topic in the comment section and let a healthy conversation begin.

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The above information is to spread financial literacy. We are not SEBI registered financial advisors, kindly consult your financial advisor before making any investment decision.



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