Impact of the Environmental, Social, and Governance Rating on the Cost of Capital: Evidence from the S&P 500
Bibliographic record
Abstract
We use the S&P 500 to investigate whether companies with a good ESG score benefit from a lower cost of capital. Using Bloomberg’s financial data and MSCI’s ESG score for 498 companies, we calculated the measures of descriptive statistics, finding that companies with better ESG ratings enjoy both a lower cost of equity and a lower cost of debt. However, their WACC shows no improvement with a higher ESG score. Companies with a poor ESG rating have a lower WACC due to the higher proportion of debt capital, coupled with a higher cost of debt, compared to the cost of equity capital. Calculating the Pearson correlation coefficient, we found a slightly negative linear relationship between the ESG score and the beta factor, and between the ESG score and the cost of debt. No linear relationship was found between the WACC and the ESG score. Finally, linear regression analysis shows a negative and significant effect of the ESG score on the root beta factor. This research indicates that companies with better ESG scores benefit from lower cost of equity and debt. Our results may encourage companies to operate more sustainably to reduce their cost of capital.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".