Ownership structure and profitability of listed firms in an emerging market
Bibliographic record
Abstract
Motivated by the agency theory and the need to examine the effect of separation of ownership and management, this study examines the determinants of profitability in different firm ownership structures and how different ownership structures impact the profitability of listed firms between 2003 and 2013, using pooled annual data of 23 Ghanaian listed firms. Employing a number of static models (OLS, Random Effects and 3 Stage Least Squares), we find evidence that while profit determinants vary for listed firms given their ownership structures, ownership structures also affected profitability differently. Specifically, for listed firms, profitability was determined by capital intensity, liquidity, financial risk, age and GDP; for non-family owned listed firms, profitability was determined by capital intensity, liquidity, market share and age; for foreign-owned firms, profitability was determined by capital intensity, liquidity, age and GDP; and for non-foreign ownership, profitability was determined by capital intensity, liquidity, financial risk, growth, age and GDP. When we examine the impact of ownership structure on profitability and find that family-owned listed firms make 30% less profits compared to nonfamily owned ones, whilst foreign-owned firms make 13% more profits than non-foreign owned ones. These findings confirm the agency theory which posits that separation of ownership and management, though may lead to agency problems, can positively affect profits. The study recommends that family-owned listed firms should consider diluting ownership in order to grow more profits.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".