Reconsidering the impact of environmental, social and governance practices on firm profitability
Bibliographic record
Abstract
Purpose This paper investigates the relationship between commitment to ESG practices and firm performance using a synthetic index based on ESG disclosure and ESG performance scores. Design/methodology/approach Using the Mazziotta-Pareto aggregation method, we develop a novel synthetic index of ESG engagement based on ESG rating and disclosure. This index is employed in a dynamic panel regression, implemented using the Arellano-Bond estimator, to explain profitability in a sample of 146 listed Canadian firms over the period spanning from 2014 to 2021. Findings ESG practices may either foster or hinder firm performance. In particular, a synergy emerges between the social and environmental dimensions of ESG practices, shedding light on the relevance of high standards in terms of environmental and social activities. Practical implications The study emphasizes the significance of acknowledging the various facets of ESG engagement and the necessity of transcending the current constraints of accessible ESG data and ratings. Synthetic indices combining different types of ESG information may contribute to mitigating the problems created by strategic disclosure on the part of firms, which typically results in undesirable practices such as greenwashing and social washing. Originality/value This is the first study that applies the Mazziotta-Pareto method to develop a synthetic index of ESG engagement, tackling each pillar separately. Moreover, when investigating the effect of ESG engagement on profitability, we allow for cross-pillar synergies and/or trade-offs.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".