Gender diversity performance and voluntary disclosure: Mind the (gender pay) gap
June Huang and
Shirley Lu
Accounting, Organizations and Society, 2025, vol. 114, issue C
Abstract:
We study whether voluntary gender diversity disclosure is predictive of gender diversity performance. Exploiting a mandate in the United Kingdom that requires firms to disclose 2017 gender pay gap (“GPG”) data for the first time, we find that providing voluntary gender diversity disclosure in 2016 is correlated with having a worse gender pay gap in 2017. Our results are concentrated in industries with worse gender diversity reputations, consistent with legitimacy theory, where firms facing more public pressure use voluntary disclosure to help legitimize their reputations. We further examine whether this disclosure reflects a firm's intent to improve its gender diversity performance over time. We find that forward-looking disclosures, such as gender diversity targets, are positively associated with GPG improvement from 2017 to 2019. Collectively, these gender pay gap findings shed light on how voluntary ESG disclosure can be used to predict current and future ESG performance.
Keywords: Gender pay gap; Gender diversity; Voluntary disclosure; ESG ratings; ESG; Corporate social responsibility (search for similar items in EconPapers)
JEL-codes: D80 M14 M41 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0361368225000066
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:aosoci:v:114:y:2025:i:c:s0361368225000066
DOI: 10.1016/j.aos.2025.101594
Access Statistics for this article
Accounting, Organizations and Society is currently edited by Christopher Chapman
More articles in Accounting, Organizations and Society from Elsevier
Bibliographic data for series maintained by Catherine Liu ().