Does Boardroom Ethnic Diversity Shape ESG Performance? Insights from the US Banking Sector
Bibliographic record
Abstract
This research investigates the relationship between ethnic diversity in the boardroom and the ESG performance of US banks during the 2016-2021 period. To this aim, we implement the 2-step system GMM estimation technique, which addresses endogeneity issues that have posed challenges in many studies. Our findings indicate that boardroom ethnic diversity negatively influences ESG performance. Moreover, in a nonlinear analysis, we provide evidence of a U-shaped relationship between boardroom ethnic diversity and the ESG performance of banks. These results remain robust when, instead of ESG performance, we examine the social and corporate governance performance of banks. We also demonstrate that the impact of boardroom ethnic heterogeneity on ESG performance varies with bank size. Furthermore, we reveal that during the pandemic, the previously negative impact of ethnically diverse directors on ESG performance shifts and ultimately becomes positive. Consequently, our conclusions serve as an important source of information to lawmakers and regulators and enrich the corporate governance research concerning the nexus between board characteristics and ESG performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".