Incentive Plans, Pay-for-non-financial Performance and ESG Criteria: Evidence from the European Banking Sector
Bibliographic record
Abstract
The new regulations of banking compensation following the sub-prime crisis require that incentive plans must be linked not only to performance parameters, but also to non-financial or qualitative metrics related to social value produced by banks. This paper aims to analyze this issue by developing a qualitative rating (ESG-remuneration performance rating) to be used not only to investigate the spread and the diversification of such qualitative indicators, but also to analyze the best practices by banks. At a methodological level, the content analysis approach is adopted. The sample covers all of the “European globally systemically important institutions” (G-SIIs), while the investigation period regards the three-years 2014-2016. The main results are encouraging as they show a good diffusion of qualitative metrics by bank incentive plans; however, the intensive use, synthesized by the “ESG-remuneration performance rating”, is still inadequate. Moreover, the analysis reveal other criticalities linked to the implementation of the balance scorecard and the use of measurement tools in order to quantify the qualitative metrics correctly. (Note 1).
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".