Unveiling the ESG Landscape: Exploring Revealed Preferences
through Archetypal Analysis of Decision-Makers in
Environmental, Social, and Governance Causes
Name
robinet-mrobinet-msms-sloan-2023-thesis.pdf
Description
Thesis PDF
Size
1.72 MB
Format
Adobe PDF
Checksum (MD5)
3c43a66cb43ec0392c39da07dac483fb
Author(s)
Robinet, Mathilde
Advisor(s)
Rigobon, Roberto
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
This research investigates the motivations and decision-making patterns driving individuals' behavior when giving to Environmental, Social, and Governance (ESG) causes. Through the concept of revealed preferences, this study offers a comprehensive exploration of the intricate landscape of ESG resource allocation. The analysis is conducted through the innovative use of the ESG Machine game, offering unique insights into how individual preferences shape unique allocation portfolios and the strategies underpinning these decisions.
The study identifies four distinct archetypes of decision-making - payoff-maximizer based, equality-based, proportional-based, and value-based - and employs a suite of indicators to measure the degree of each archetype in individuals. A key finding is the dominance of value-based decision-making, although strategies like impact maximization and equal distribution of payoffs across all causes also emerged.
These findings bear profound implications for ESG investing by offering invaluable insights that can shape the formulation of more potent investment strategies. By uncovering the complexities of individual decision-making and the differing strategies at play, this study paves the way for designing interventions that align personal values and financial choices effectively. This tailored approach has the potential to resonate deeply with individuals, engaging them based on their decision-making archetype, thereby enhancing the efficiency of ESG investments.
MIT Department
Sloan School of Management
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