Development of the ESG Pillar Scores and Data Availability: Empirical Evidence from the Insurance Industry
Bibliographic record
Abstract
The aim of this paper is to empirically investigate the development of the ESG score and, in particular, the respective pillar scores, as well as the data availability based on region, firm size, and business sector within the insurance industry. We also analyze the interrelationships of the ESG score and data availability, focusing on descriptive statistics and correlation analysis. For this purpose, we use data from the London Stock Exchange Group (LSEG), over a period of 13 years (2010–2022). Our results show that region, firm size, and the different core businesses of insurers lead to different developments in ESG scores and data availability. Differences can also be found in the general level of the scores. However, there is no clear pattern in the evolution of ESG scores and data availability. Furthermore, we find significant and strong interrelationships within the ESG score and its data availability. In summary, the findings of this study provide a foundation for improving the interpretation and application of ESG metrics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".