JOURNAL ARTICLE
Accounting Firm Investment in Diverse Talent: Evidence from Charitable Giving to Historically Black Colleges and Universities (HBCUs).
Published In: Accounting Horizons, 2024, v. 38, n. 1. P. 49 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Cowle, Elizabeth N.; Draeger, Michelle A.; Smith, Kecia W. 3 of 3
Abstract
SYNOPSIS: The accounting profession suffers from diversity, equity, inclusion, and belonging (DEIB) deficiencies. Although the largest accounting firms have touted their investments in diversity efforts, the extent to which firms have invested in recruiting diverse talent is unclear. Using accounting firms' charitable giving data, we show that, although firm giving to historically Black colleges and universities (HBCUs) has increased over time, the total allocation percentage to HBCUs has remained relatively stagnant. Further, monetary giving to HBCUs is concentrated among a few HBCUs with higher prestige, and all HBCUs receive relatively little overall compared with predominantly white institutions (PWIs). Discussions with HBCU accounting faculty and accounting firm professionals support our main findings and suggest additional forms of investment that accounting firms can leverage to help address DEIB deficiencies. Overall, our study provides actionable recommendations that can inform public accounting firms' efforts as they seek to develop a more diverse workforce. Data Availability: Data are available upon request. JEL Classifications: M41. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Accounting Horizons. 2024/03, Vol. 38, Issue 1, p49
- Document Type:Article
- Subject Area:Economics
- Publication Date:2024
- ISSN:0888-7993
- DOI:10.2308/HORIZONS-2022-067
- Accession Number:175794850
- Copyright Statement:Copyright of Accounting Horizons is the property of American Accounting Association 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.