CEO Career Horizon, CEO Power, Corporate Governance and Earnings Quality: Evidence from Egypt
Bibliographic record
Abstract
This study analysed the determinants of firms' reported earnings quality (hereafter FREQ) for Egyptian firms from 2008 to 2019, using panel data.The Chief Executive Officer (CEO) career horizon (a CEO approaching retirement) is negatively associated with FREQ; CEO power dynamics (CEO duality, CEO stock ownership, CEO tenure, and CEO political connections) negatively affect FREQ; and board independence significantly moderates (weakens) the negative impact of CEO ownership and CEO tenure on FREQ.The findings do not support the weakening or substitution role of board independence for the negative impact of CEO career horizon, CEO duality and CEO political connections on FREQ.The presence of gender-critical mass serves as a substitution mechanism for the negative impact of CEO career horizon and CEO power dynamics (duality, ownership, tenure, and political connections) on FREQ.The findings on the interplay among CEO power dynamics shows that CEO duality, CEO ownership and CEO tenure augment each other in their negative role in determining FREQ.CEO educational level substitutes the negative impacts of CEO ownership and CEO tenure on FREQ.Principal analysis was observed for robustness through propensity matching score and difference-in-difference (DID) techniques.This study adds new knowledge by exploring the negative consequences of CEO career horizon and CEO power dynamics, and provides insights into the constraining role of corporate governance, strengthens reverse-causality, and uses DID approach and propensity matching techniques.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".