The Influence of Corporate and Country-Level Governance on Firm Payout Policies: A Summary of Worldwide Market
Bibliographic record
Abstract
Since the pandemic in 2020, many firms paused or reduced their dividend payout as shifting to a defense strategy. However, the total dividends payout in the second quarter of 2022 by S&P500 firms is 11.8% more than that in the period of 2021 and share buyback spending is 11.7% higher. Thus, the company’s payout policy is still a significant concern for investors. This research summarizes several findings on the interaction of payout policy with corporate governance and with country-level governance and discusses the dependence between these two factors. This research finds that country-level governance is positively related to a firm’s payout policy, and because of the correlation between firm-level and country-level governance, the interaction between firm-level governance and dividend policy varies with legal strength: in weak legal regimes, stronger corporate governance leads to higher dividend payout, and in strong legal regimes, they correlate in the opposite way, as the shock repurchase is preferred over dividend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".