The Coupling of Ownership and Control and the Allocation of Financial Resources: Evidence from Hong Kong
Bibliographic record
Abstract
Theoretical and empirical research regarding the impact of corporate ownership on the behaviour and performance of firms have typically focused on consequences stemming from the separation of ownership and control. While large scale business enterprise characterized by such a separation is dominant in the US, Japan and the UK, firms in which ownership and control is coupled in the hands of individuals and their families are apparent in many other large developed economies and are dominant in most emerging markets. This paper examines consequences regarding the generation and allocation of financial resources stemming from the coupling of ownership and control among Hong Kong based firms. In doing so, we join insights from the economics literature regarding the incentive and risk bearing consequences of coupled ownership and control with the extant management, sociology and history literatures regarding Chinese family business groups and develop and six hypotheses pertaining to patterns in the allocation of financial resources. Results indicate that coupled ownership and control is positively related with dividend payout levels and financial liquidity while it is negatively related to investments in capital expenditures. Consistent with these results, we also find that coupled ownership and control is positively related to short‐term (accounting) profitability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".