The association between CEO incentive rewards and earnings management
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate whether or not there is a link between CEO incentive-based compensation and earnings management and to examine how institutional environment's features influence such link. Design/methodology/approach – To test the predictions, the authors use a panel of 1,500 American, Canadian, British, and French firm-year observation over the period 2004-2008. Findings – The authors find a significant association between earnings management and CEO incentive-based compensation. Moreover, the analysis provides evidence that institutional factors are strong determinants of this association. Specifically, the results show that firms from countries within the Anglo-American corporate governance model, which provides greater protection of shareholder rights, ensures strict enforcement of law, and scores high on board oversight, tend to have lower level of earnings management. The analysis shows however, that beside the formal corporate governance quality, it is relevant to consider weaker shareholder protection and lower law enforcement indexes to explain earnings management in firms from countries within the Euro-Continental corporate governance model. Originality/value – This paper is the first to provide insights regarding the extent to which CEO incentive rewards imply management discretion and to indicate how much institutional features matter. The analysis contributes to two distinct strands of research. It extends prior research on the association between executive compensation and earnings management and adds to the literature demonstrating a relationship between institutional factors and financial decisions.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".