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Do firms with environmental, social, and governance reputational risk take into account board gender diversity? An analysis on a global scale.

  • Published In: Social Science Quarterly (Wiley-Blackwell), 2024, v. 105, n. 4. P. 1396 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tran, Mai‐Minh‐Anh; Nguyen, Ngoc‐Yen‐Chi; Quyen, Nguyen‐Khanh‐Ha; Tran, Phuong‐Nhu; Phan, Nguyen‐Minh‐Thu; Le, Anh‐Tuan 3 of 3

Abstract

Objective: This article studies whether a firm's environmental, social, and governance (ESG) reputational risk influences board gender diversity. Besides, we are also interested in the moderating role of gender equality and country development level toward the relationship between reputational risk and board gender diversity. Method: Using a comprehensive sample of firms from 52 countries between 2007 and 2019, we employ multiple regression with fixed effects. Our findings remain robust when using alternative measures of variables and addressing endogeneity concerns by employing a two‐stage systems generalized method of moments estimation and an instrumental variable approach. Results: Companies with high levels of ESG reputational risk tend to add more women directors to resolve these problems. Furthermore, the empirical results point out that the positive impact of ESG reputational risk on board gender diversity is more pronounced for countries with a higher degree of gender equality or developed countries. Conclusions: Overall, our study is the first international study to link ESG reputational risk via media channels to board gender diversity. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Science Quarterly (Wiley-Blackwell). 2024/07, Vol. 105, Issue 4, p1396
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0038-4941
  • DOI:10.1111/ssqu.13411
  • Accession Number:179072037
  • Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell 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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