Why do Family Firms Pay Cash Dividends in Emerging Markets? Corporate Control and Family Succession in Korea
Bibliographic record
Abstract
Following the economic crisis in 1997, the Korean government introduced the enhanced corporate governance and reform policy, which drove family-controlled firms to search strategic reaction for control succession and wealth transfer. This paper explores alternative explanations for why Korean firms choose to pay cash dividends around this corporate reform period. What lead firms to pay cash dividends remains largely unexplained by the reducing agency cost, signaling, or life-cycle theories. This study focuses on relations between the ownership structure and cash dividends payout, seeking effects deriving from (i) controlling shareholder (CS) and (ii) their family members. The logit analysis result shows that firms with large control rights, especially higher ownership of other family members of CS are more likely to pay cash dividends. After adjusting for the characteristics that affect the degree of cash dividends, ownership variables are positively related to payout ratios and dividend yields. CS family members' ownership has a statistically stronger effect on payout ratios than CS's. These results provide the evidence of incentive for corporate control succession within the family with least costs carried by the family members of controlling shareholders who positively influence payout decisions and dividend ratios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".