The Moderating Role of Worldwide Governance Indicators on ESG–Firm Performance Relationship: Evidence from Europe
Bibliographic record
Abstract
Engaging in Environmental, Social, and Governance (ESG) activities entails costs that influence a firm’s financial and market performance. However, it is expected that the long-term benefits of ESG engagement outweigh these costs, leading to superior performance. Despite extensive research on the ESG–performance relationship, findings remain mixed. This study examines the moderating effect of country governance, measured by the Worldwide Governance Indicators (WGIs), on the relationship between firms’ ESG scores and their financial and market performance in the European context. Using a two-stage least squares (2SLS) regression model and a dataset spanning 12 years (2011–2022) for 2083 listed European firms, we find that WGI significantly moderates the ESG–performance relationship. Our results indicate that ESG engagement alone has a negative impact on financial performance (ROA), suggesting that the costs associated with ESG investments often outweigh their short-term benefits. However, strong governance structures mitigate these costs, transforming ESG investments into value-enhancing activities. Conversely, ESG engagement positively influences market performance (Tobin’s Q), signaling long-term value to investors. Yet, in jurisdictions with strong governance frameworks, this effect diminishes, as ESG compliance becomes a baseline expectation rather than a differentiating factor.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".