CEO gender and readability of annual reports: do female CEOs’ demographic attributes matter?
Bibliographic record
Abstract
Purpose This study examines the association between chief executive officer (CEO) gender and the readability of annual reports by considering some demographic attributes of female CEOs. Design/methodology/approach Ordinary least squares (OLS) regression is used to test the research hypotheses on a sample of S&P 500 firms between 2004 and 2016. Findings The results show that female CEOs are significantly positively associated with the readability of 10-K reports – in line with ethical-sensitivity theory. Further results show that this association is variable depending on the demographic attributes of female CEOs – in line with upper echelon theory. Specifically, older female CEOs and those with financial expertise are significantly associated with more readable 10-K reports. In contrast, female CEOs hired from within the firm are negatively associated with the readability of 10-K. Research limitations/implications This study provides evidence on the effect of female CEOs and their demographic attributes on annual report readability, which was not addressed in prior research. Practical implications The findings show that the appointment of female CEOs seems like a helpful avenue to reduce concerns among the regulators about the textual complexity of annual reports. However, the most important policy implication of the study is that the decision to appoint female CEOs should be based more on their demographic attributes than on gender equality recommendations and full trust in women's behavioral consequences. Originality/value This study contributes to the academic literature on readability and gender. Prior research has not clarified which attributes and skills of female CEOs drive their abilities to improve shareholder value and make more ethical decisions. This study suggests that female CEOs are not better “per se” to improve corporate governance practices, and the impacts of female CEOs are not the same and differ according to their demographic attributes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".