Revisiting corporate governance mechanisms and real earnings management activities in emerging economies
Bibliographic record
Abstract
Purpose This study aims to investigate the effects of main corporate governance (CG) mechanisms used in Iran on the relationship between managers’ rewards and real earnings management activities. Design/methodology/approach Panel data analysis is performed on 101 companies listed on the Tehran Stock Exchange during the past seven years (from 2015 to 2021). Findings The percentage of non-executive members of the company’s board of directors and the percentage of acquisition of the company’s largest shareholders have a negative significant effect on the relationship between abnormal operating cash flows and managers’ remuneration. Moreover, the separation of the CEO from the chairman and vice chairman of the board has also a negative significant effect on this relationship. However, concentration of ownership does not have a significant effect on the relationship between abnormal operating cash flows and managers’ rewards. Practical implications The study provides policymakers and governing bodies with a better understanding of the effects of the percentage of non-executive board members, concentration of ownership, percentage of major shareholders and duality of the role of CEO (or president) from the chairman and vice chairman of the board on the relationship between managers’ rewards and earnings management. Originality/value Previous studies focus mainly on accrual-based earnings management. This study investigates real earnings management and provides empirical evidence on the most effective and significant CG dimensions in Iran. It embraces the fact that CG may have the same principal concept in different markets, but the mechanisms may vary significantly, thus opening the door for more comparative future research.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".