Earnings Management and Status of Corporate Governance under Different Levels of Corruption—An Empirical Analysis in European Countries
Bibliographic record
Abstract
This study investigates the effect of the characteristics of the board of directors on the accrual and real earnings management level, focusing on the role of the corruption level. The employed dataset consists of 469 European-listed firms from 2011 to 2019. Using a fixed-effect panel data regression model, the results documented that larger boards lack coordination and communication in less corrupt economies, facilitating earnings manipulation through accruals and sales. In highly corrupt countries, oversized boards are associated with increased manipulation of production costs and discretionary expenses. Board meetings are positively related to accrual and sales manipulation in low-corruption countries, and board independence leads to reducing discretionary expenses regardless of corruption level. Board tenure negatively affects accruals and discretionary expenses but tends to increase manipulation through production costs in low-corruption contexts. Additionally, when the CEO serves as the board chairman, it encourages the manipulation of discretionary expenses while reducing real earnings manipulation through sales and production costs. In aggregate, the level of corruption can influence a board’s effectiveness under specific conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".