Earnings management and board oversight: an international comparison
Bibliographic record
Abstract
Purpose This paper attempts to investigate the relationships between the board of directors' characteristics and earnings management being a proxy of earnings quality in two separate countries, France and Canada. Specifically, it aims to investigate how certain contextual features affect differently earnings management behavior, and to reveal which factors are the most prominent incentives of management discretion in both cases. Design/methodology/approach The paper uses a performance matched discretionary accruals (PMDA) measure as a proxy for earnings management. Three separate panel‐regressions are then performed on a full sample, comprising a French sub‐sample and a Canadian sub‐sample, to detect board characteristics and institutional features' impacts on the PMDA. Regressions are based on a panel of 180 French and Canadian listed firms' data over the period 2006‐2008. Findings Evidence shows that CEO stock ownership, independent monitoring and institutional investor's property are strong earnings management determinants in both the French and Canadian frameworks. Nevertheless, leadership structure and board size seem to be neutral. Furthermore, French firms show specific earnings management incentives which are related to high ownership concentration, low equity widespread and high contractual debt costs. Dominant minority ownership and capital market forces are the key earnings management incentives in the Canadian context. These findings are robust to alternative sensitivity tests. Research limitations/implications Even though the findings answer some questions, earnings management incentives are still to be decided. Future research could further highlight the impact of contractual, legal, cultural, ethical and political country‐specific factors related to financial reporting. Originality/value This paper investigates how an effective board of directors is able to provide a monitoring mechanism to ensure high quality of earnings. Moreover, it builds on cross‐country variations in corporate governance features and contextual‐specific factors to reveal earnings management behavior's incentives in two separate environments, namely French and Canadian ones. The underlying promise is that poor corporate governance (weak board monitoring), high ownership concentration, and intensive financial market forces create incentives that largely influence manager's willingness to report earnings that don't reflect a firm's true performance.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".