Corporate governance compliance and accrual earnings management in eastern Africa
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine whether compliance with corporate governance (CG) requirements has constrained earnings management (EM) for companies listed in Kenya and Tanzania. Design/methodology/approach The sample comprises of 48 companies listed on the Nairobi Stock Exchange and the Dar es Salaam Stock Exchange. The data are collected from annual reports over the period 2005-2014, a total of 480 firm-year observations. Panel data models are used in the analyses. Findings The results show that discretionary accruals (DAs) average about 11.3 per cent, whereas audit quality is negatively and significantly related to DAs. However, board independence, board gender diversity and director share ownership were positively and significantly related to DAs suggesting that CG may not have constrained EM in eastern Africa. Research limitations/implications The findings should be understood within the context that only annual reports and audited financial statements that were filed with Capital Markets Authority (Kenya) and Capital Markets and Securities Authority (Tanzania) are used as source of information. Originality/value The study potentially contributes in three main ways. First, this is the first cross-country analysis that has examined the effect of CG structures on EM in an African context. Second, literature on CG and EM has been extended. Finally, the authors have extended research by observing the limitations of CG in reducing EM in an environment that is experiencing weaknesses in CG structures.
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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 | 0.005 |
| 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) | 0.002 | 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".