The impact of board characteristics on the extent of earnings management: conditional evidence from quantile regressions

Muhammad Usman*, Jacinta Nwachukwu, Ernest Ezeani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Purpose: This paper aims to examine the impact of board characteristics on earnings management (EM) among UK non-financial firms.

Design/methodology/approach: Using a sample of the UK Financial Times Stock Exchange 350 firms from 2010 till 2019, the authors investigated the relationship between board characteristics (board size, board gender diversity, board tenure, board independence, chief executive office-duality and board meetings) and EM by using the quantile regression technique.

Findings: This study found a non-linear association between board characteristics and discretionary accrual. The empirical evidence showed that board mechanisms reduce the extent of earnings manipulation among UK firms with higher discretionary accruals (DACC) than firms with low and medium DACC levels.

Research limitations/implications: The results will benefit UK firms by helping them to rethink their board composition. It will also help policymakers understand how the corporate board can help ensure the quality of financial reports.

Originality/value: This study used the quantile regression approach, which helps to clarify the mixed findings of prior studies that used conventional regression techniques.

Original languageEnglish
Pages (from-to)600-616
Number of pages17
JournalInternational Journal of Accounting and Information Management
Issue number5
Early online date19 Sept 2022
Publication statusPublished - 30 Sept 2022


  • Board characteristics
  • Corporate governance
  • Earnings management
  • Quantile regression

ASJC Scopus subject areas

  • Management Information Systems
  • Accounting
  • Economics, Econometrics and Finance(all)


Dive into the research topics of 'The impact of board characteristics on the extent of earnings management: conditional evidence from quantile regressions'. Together they form a unique fingerprint.

Cite this