ESG Disclosure and Employee Turnover. New Evidence from Listed European Companies
Bibliographic record
Abstract
We explored how company transparency, as measured by ESG (Environmental, Social and Governance) disclosure, affected the employee turnover of 212 multinational corporations that were listed in the European capital market during the 2010-2017 period. We also examined the role of the business environment by looking at the company’s ESG reporting system and its economic sector. To analyze how ESG disclosure affected employee turnover at any point of its conditional distribution, we used a panel data quantile regression model. ESG disclosure was found to be negatively associated with employee turnover. Employee turnover, as well as the extent to which it is affected by ESG disclosure, was found to depend strongly on the conditional distribution of the turnover rate, the sector and whether ESG disclosure is mandatory or voluntary. Our findings were confirmed by a robustness check analysis. In conclusion, the relationship between company transparency and employee turnover depends strongly on the institutional context and, especially, on disclosure regulation. The more a company is scrutinized, the more it will try to be socially responsible to maintain and/or improve its reputation and thus reassure and satisfy its stakeholders. Abstract We sought to analyze the relationship between ESG (Environmental, Social and Governance) disclosure and employee turnover. We also examined how this relationship is affected by regulation of ESG reporting and by sector characteristics. A panel data quantile regression model was applied to data from 212 multinational corporations that were listed in the European capital market during the 2010-2017 period. ESG disclosure was found to be negatively associated with employee turnover. Employee turnover, as well as the extent to which it is affected by ESG disclosure, was found to depend strongly on the conditional distribution of the turnover rate, the economic sector, and whether ESG disclosure is mandatory or voluntary. A robustness check clearly confirmed our findings.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".