Oil Price Volatility vs. Sustainable Investment: Impact on Global Dividends
Bibliographic record
Abstract
Sustainability has become a central concern for businesses and investors worldwide, yet obstacles arise when investors' perceptions change, and regulatory policies hinder businesses from committing to Environmental, Social, and Governance (ESG). This study examines the impact of (ESG) performance on dividend policy across six major sectors—Financial, Industrial, Technology, Healthcare, Basic Materials, and Utilities—in fourteen countries across the Americas, Europe, and Asia (USA, Canada, Brazil, Mexico, Chile, Turkey, India, Japan, China, UK, Germany, Italy, France, and South Korea) from 2010 to 2022. We explore the relationship between ESG scores and dividend policy utilizing a comprehensive dataset from publicly traded companies. We focus on three key dividend measures: dividend per share, dividend payout ratio, and dividend growth. We assess the differential impact of overall ESG performance and individual ESG pillars (Environmental, Social, and Governance) on firms of varying sizes, small, medium, and large—within each sector. Robust econometric techniques such as Two-Stage Least Squares (2SLS), Generalized Method of Moments (GMM), and Difference-in-Differences (DID) models are employed to address potential endogeneity issues and validate findings during the economic shock of COVID-19. Our results consistently show that ESG performance positively influences dividend policies; however, the effects vary by sector and firm size. Generally, medium and large firms benefit the most. This study offers detailed information about how the ESG score affects dividend policy across diverse sectors globally. It provides insightful analyses for managers, investors, and legislators who want to comprehend how sustainable investments affect business financial choices.
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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 | 0.004 |
| 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 | 0.001 |
| 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".