A Modern Approach for Measuring Environmental, Social, and Governance Preferences

With the rapid growth of Environmental, Social, and Governance (ESG) investing, several concerns have been raised regarding the ability of ESG rating companies and investment managers to accurately and transparently reflect the ESG preferences of individual and institutional clients. To address this issue, we developed the ESG Machine, a website used to measure ESG preferences by applying methods from revealed preference theory. In a short time, this website gathered 17,248 decision observations from 800 individuals in 55 countries. A subset of this data is used to better understand the importance of measuring ESG preferences and how preferences vary by demographic. We first measure the rationality of individuals and the relationship to demographics and response time. Second, we examine donation amounts and the impact of prices as well as the equality and efficiency tendencies of individuals. Third, for each individual we estimate the parameters of a two-good Constant Elasticity of Substitution (CES) utility function and analyze the substitution parameters and the preferences towards the social and environmental causes. Fourth, for more than two goods we apply nested CES functions to estimate the aggregate preferences of all individuals and demographic clusters. We find that it is important to measure ESG preferences to improve the accuracy and transparency of ESG investing.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Metzman, Zachary M.

Advisor dc:contributor.advisor

• Rigobon, Roberto

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139548

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139548