Camouflaged Compensation: Do South African Executives Increase Their Pay through Share Repurchases?
Bibliographic record
Abstract
Increasingly, researchers in developed economies are associating the exponential growth in share repurchases with executives’ desire to increase company share price and thus the value of their own share-based compensation. As research on this topic in emerging economies is sparse, this paper investigates the relationship between share repurchases and executive share-based compensation in South Africa. Certain weaknesses in South African corporate governance relating to share repurchases exacerbate the risk of camouflaged rent extraction and unethical behaviour. Regression analyses were executed, using data on share repurchases and executive share-based compensation variables for listed South African companies for the period 2002–2017. Statistically significant positive relationships were identified between share repurchases and executive share-based compensation. The results support the proposition that South African executives may be repurchasing shares in a bid to increase the value of their share-based compensation (in line with the managerial power theory), rather than maximising long-term shareholder value. This paper emphasises the need for improved corporate governance relating to share repurchases in South Africa. Given the income inequality in South Africa, the findings also have social justice implications.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".