Do ESG Funds Deliver on Their Promises?
Bibliographic record
Abstract
Corporations have received growing criticism for contributing to climate change, perpetuating racial and gender inequality, and failing to address other pressing social issues. In response to these concerns, shareholders are increasingly focusing on environmental, social, and corporate governance (ESG) criteria in selecting investments, and asset managers are responding by offering a growing number of ESG mutual funds. The flow of assets into ESG is one of the most dramatic trends in asset management. But are these funds giving investors what they promise? This question has attracted the attention of regulators, with the Department of Labor and the Securities and Exchange Commission (SEC) both taking steps to rein in ESG funds. The change in administration has created an opportunity to rethink these steps, but the rapid growth and evolution of the market mean regulators are acting without a clear picture of ESG investing. We fill this gap by offering the most complete empirical overview of ESG mutual funds to date. Combining comprehensive data on mutual funds with proprietary data from the several of the most significant ESG ratings firms, we provide a unique picture of the current ESG environment with an eye to informing regulatory policy. We evaluate a number of criticisms of ESG funds made by academics and policymakers and find them lacking. We find that ESG funds offer their investors increased ESG exposure. They also vote their shares differently from non-ESG funds and are more supportive of ESG principles. Our analysis shows that they do so without increasing costs or reducing returns. We conclude that ESG funds generally offer investors a differentiated and competitive investment product that is consistent with their labeling. In short, we see no reason to single out ESG funds for special regulation.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".