The impact of the ESG pillars on the payout policy among G7 countries’ firms
Bibliographic record
Abstract
This study investigates whether and how the Environmental, Social and Governance Pillars from the ESG performance influence the firms’ payout decisions. The sample is composed by 3,057 firms from the G7 countries, and the period range is from 2000 to 2022. The G7 group is formed by Germany, Canada, the USA, France, Italy, Japan, and the United Kingdom. The findings demonstrate that, at firm level, the more companies focus on Environmental issues the higher the probability of paying cash dividends and the higher the dividend amounts paid. Additionally, when firms increase their concern about Social matters the higher the dividend amounts paid, however, the more importance they give to Governance matters the lower the dividend amounts paid. Throughout the years, the concern about environmental issues, firm’s social impact and effective governance increased. Consequently, this study is highly relevant in today's context, particularly concerning payout decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".