Obtaining a Data-Driven Analysis on ESG Remuneration

We worked on a remuneration project with a company from the United Kingdom. As our project is complete, we certainly had some struggles, but they’ve led to a finalized product for which our group is very proud. 

Our project involved analyzing remuneration and ESG performance for companies in the United Kingdom. Our client wanted to know if ESG-related remuneration incentives drove ESG performance. To address their question, we focused on the top 30 companies in terms of market capture within the London Stock Exchange and conducted individual analyses for each company. We took five years of data regarding ESG-related remuneration incentives, performance in terms of those incentives, and actual ESG performance. With our client’s goals for the project, we needed to identify if there was a causation or correlation between ESG-related remuneration incentives and ESG performance. 

To give the client what they expected, we wanted a final result that was very data-driven but included a qualitative approach too. To give a data-driven result, we had difficulties identifying the appropriate methodology to conduct. From my experience in quantitative analysis-type classes, it became apparent that a statistical regression model was the most appropriate method to accomplish our goal. However, to complete a regression, the data needs to be logically sound and mostly basic which our data wasn’t - we had many different values or components involved in both ESG remuneration and ESG performance. In turn, we needed a more statistically-sound summary of ESG remuneration and ESG performance. 

To find a statistically sound summary, we created two terms to regress - ESG and ESG Remuneration. Both of these variables were measured for each year of observation, which was five years, and for each company. The term ESG refers to ESG performance and the quality of the ESG metrics reported by companies. Similarly, ESG Remuneration refers to the quality of incentives specifically related to remuneration and the performance of the company within those incentives. We then ran a regression analysis on those numbers to find a weak correlation. Last, for a qualitative component, we added an assessment of data limitations. 

This project gave us valuable insights into the consulting world. We maintained a real relationship with a client, received constructive feedback from them on our deliverables, and produced a final product that we expect the client to use. I also got to experience working with two people who have a very different working style to my own. This was a learning experience for each of us as we needed to adapt our working styles and expectations of each other. While this led to some initial hurdles, we reached a team working structure that was very efficient and resulted in a strong product. I’m proud of what we’ve been able to achieve. 

I appreciate this course for the opportunity to conduct a consulting-type project and recommend this work to any future student. It’s a lot of work but it’s well worth it.