CEO Dynamics and Real Earnings Management: A Gender Diversity Perspective from Sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa’s (SSA) corporate environment, like many emerging markets, is marked by institutional voids, weak oversight structures, and patriarchal leadership norms, which heighten the risk of real earnings management (REM). This study examines how CEO characteristics and audit committee gender diversity influence REM among listed manufacturing firms in 12 SSA countries from 2012 to 2023. Anchored in agency theory and Upper Echelon Theory, this study draws on 1189 firm-year observations and employs Pooled OLS, Random Effects, Fixed Effects, Feasible Generalised Least Squares (FGLS), and System GMM estimators. Findings show that female CEOs are consistently associated with lower REM, underscoring the ethical conservatism linked to gender-inclusive leadership. CEO ownership shows a positive and significant association with REM in System GMM, though findings vary across models, indicating potential institutional effects. The firm size is negatively and significantly related to REM in Pooled, RE, and FGLS models, but becomes nonsignificant in FE and System GMM, suggesting the role of external scrutiny may be sensitive to model dynamics. Leverage exhibits a positive and significant relationship with REM in most models, but turns negative and nonsignificant under System GMM, pointing to endogeneity concerns. Interaction effects and country-specific regressions affirm that governance impacts differ across contexts. Policy reforms should prioritise gender-diverse leadership and tailored oversight mechanisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".