CEO remuneration, board composition and firm performance: empirical evidence from Australian listed companies
Bibliographic record
Abstract
Classical economic theories establishing a relationship between CEO remuneration and firm performance has paid particular attention to solve conflict of interest between managerial team and firm shareholders, by designing an optimum CEO remuneration that motivate executives to work in the best interest of shareholders. Many international and less Australian empirical researches suggest that there is overwhelming evidence that firm performance is strongly linked with CEO remuneration. In this paper, we reassess the association of firm performance and CEO remuneration variables using dynamic econometric models and comprehensive data from Australian Stock Exchange (ASX). We find a positive and strong association between CEO pay of top 200 Australian public listed companies and company performance. Obtained findings are similar to USA, UK and Canada studies results. We further test the effect of board and ownership features on CEO remuneration–performance sensitivity in the top 200 Australian public companies listed on ASX. Specifically, for the period of 2003-2007, our results highlight the importance of ownership structure in influencing remuneration–performance relationship. Monitoring block holders boost the responsiveness of long term incentives (LTI) remuneration to performance, thus straightening shareholder and manager welfares. However, based on a short term investment horizon strategy, insider block holders increase (decrease) the sensitivity of short-term incentives remuneration (long term incentives pay). Surprisingly, for the period 2008-2013, our findings suggest that ownership and board features did not influence significantly CEO pay-performance sensitivities. Finally, we find that larger boards increase (decrease) the responsiveness of CEO’s known remuneration (long term incentives) to 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".