Control-Enhancing Mechanisms and Earnings Management: Empirical Evidence from Pakistan
Bibliographic record
Abstract
Separation of ownership and control plays a significant role in determining the agency cost, and there are many consequences of this agency problem. The control-enhancing mechanisms enhance control of controlling shareholders who expropriate small shareholders. Controlling shareholders are different in different countries; majorly, family firms are controlling firms in Pakistani context. The use of control-enhancing mechanism is rampant in emerging economies, and even some developed countries, related research especially in Pakistan requires evidence. This study exhibits a pooled cross-sectional analysis of listed companies in Pakistan between 2005 and 2016. In this research, we have examined the influence of control-enhancing mechanisms on firms’ earnings management and which mechanism (pyramid control, multiple control chains, and cross-holding control) is significantly influencing the earnings management of firms. We have analyzed both types of earnings manipulation techniques (accrual and real earning management). Our results explicate that the pyramid control and multiple control chain mechanisms are significantly positively related to the accruals earning management and real earnings management, unveiling that firms with these controls manipulate earnings with discretionary accruals as well as with real activity manipulation. Real activity manipulation enhances firms to overproduce the inventory (decreasing the unit price) and to reduce the discretionary expenses (increasing the reported earnings).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".