Deconstruction of ESG Impacts on US Corporate Bond Pricing: The Cost of Capital Benefits Across Industry Sectors
Publication: Journal of Management in Engineering
Volume 40, Issue 1
Abstract
The growing interest in the financial materiality of Environmental, Social, and Governance (ESG) ratings has prompted recent investigations into their risk pricing impact in the corporate bond market. The specific implications for the Architecture, Engineering, and Construction (AEC) industry have not been explored, as prior work has primarily focused on broad-based ESG integration. To fill this gap, our study employed an interpretable machine learning technique using a sample universe of U.S. corporate bonds spanning from 2010 to 2021 to estimate the impact of ESG ratings on corporate bond issuance spreads. The results revealed an average ESG benefit of 10 basis points across all sectors. However, it is important to note that the effects of ESG ratings on bond pricing demonstrate variation across sectors and individual ESG constituent ratings. Significantly, our findings show that social and governance ratings emerge as the primary drivers influencing bond issuance costs, whereas the impact of environmental scores is comparatively less significant. Within AEC-related industries, empirical data on the influence of ESG ratings indicate discounted pricing by the market is particularly channeled through environmental and governance scores. These findings emphasize the value-added impact of enhanced ESG performance on the cost of debt financing, presenting a financially material opportunity for operational and management decision-making. By adopting sustainable strategies to improve ESG performance, organizations in the AEC industry can potentially achieve lower costs of debt when issuing bonds to secure financing for construction projects. The managerial implications extend to policymakers, corporate managers, and creditors, as they all stand to benefit from the financial implications of ESG performance.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge funding from the Center for Digital Asset Finance at the University of Michigan for Dan Li. We gratefully acknowledge Neha Gupta, Vittorio Orlandi, and Dr. Cynthia Rudin from the Almost Matching Exactly Lab at Duke University for their contributions and feedback on the results and discussions. Any mistakes or oversights in this study remain the responsibility of the authors.
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Journal of Management in Engineering
Volume 40 • Issue 1 • January 2024
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© 2023 American Society of Civil Engineers.
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Received: Feb 17, 2023
Accepted: Aug 10, 2023
Published online: Oct 10, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 10, 2024
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