The tangibility of the intangibles: what drives banks' sustainability disclosure in the emerging economies?
Bibliographic record
Abstract
This article sheds light onto the tangibility of the intangibles, arguing that environment, social and governance sustainability ("ESG"), typically considered as intangibles, can be explained by tangible factors such as banks' fundamentals, country ESG performance, macroeconomic factors and institutional quality. Based on panel estimation of 251 banks from 45 emerging countries over the period 2005-2014, we find that size, liquidity, years of establishment and market power positively influence banks' disclosure of ESG policies and practices. Nonprofitable banks disclose ESG, probably to build reputation and to attract more customers. At the macro level, country ESG scores are positively correlated with environment and socialdisclosure, but do not have a significant effect on any governance indicators. While banks in countries with higher economic freedom tend to focus on and value the importance of ESG, this is not the case with banks in countries with more economic growth and financial openness. We also find that a financial crisis can reduce the probability of banks' disclosure. In the overall analysis, our models can explain the disclosure of environmental and social indicators better than governance indicators.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".