JOURNAL ARTICLE
Labor Unemployment Risk and CEO Incentive Compensation.
Published In: Management Science (INFORMS), 2024, v. 70, n. 2. P. 885 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Ellul, Andrew; Wang, Cong; Zhang, Kuo 3 of 3
Abstract
This article examines how changes in unemployment risk for rank-and-file workers influence chief executive officers' (CEOs') risk-taking incentives through adjustments in executive compensation. Using state-level variations in unemployment insurance (UI) benefits as an exogenous proxy for workers' unemployment risk, the study finds that more generous UI benefits lead boards of directors to increase the convexity of CEO pay—measured by "flow vega," the sensitivity of CEO wealth to stock return volatility—primarily through greater stock option grants. This effect is stronger in firms with more independent and gender-diverse boards, greater long-term institutional ownership, and in labor- or human-capital-intensive industries. A difference-in-differences analysis supports a causal interpretation, and instrumental variable regressions show that increased CEO vega induces greater corporate risk-taking, including higher leverage, R&D investment, and cash flow volatility. Overall, the findings suggest that boards internalize workers' unemployment risk when designing managerial incentives to align executive risk-taking with shareholder value while considering labor market frictions.
Additional Information
- Source:Management Science (INFORMS). 2024/02, Vol. 70, Issue 2, p885
- Document Type:Article
- Subject Area:Economics
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4714
- Accession Number:175542976
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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