ESG Score and Market Value: The Role of the Family Firm Evidence from the Italian Listed Market
Bibliographic record
Abstract
In recent years, environmental, social, and governance (ESG) practices and disclosure has become a critical component of corporate finance and accounting. Indeed, increasingly, companies seek to demonstrate their accountability to the environment and society in order to meet the expectations of different stakeholders interested in ESG performance (Garcia-Sanchez & Garcia-Sanchez, 2020). Customers, regulators, employees, suppliers, social and activist groups, media and lenders are all potential stakeholders in sustainability accountability (Arif et al., 2021; Camilleri, 2015; Sajjad et al., 2020). Nevertheless, the shareholders, institutional investors and individuals, looking to invest their money in firms with sustainable finance goals and high levels of ESG performance (Lourenço et al., 2014). Companies are supposed to make and disclose sustainable initiatives in organizational decisions (Garcia-Sanchez et al., 2014) and managers convey the focus of their efforts towards a more sustainable environment and society paying much attention to ESG indicators (Broadstock et al., 2019). This paper investigates the value of ESG score for family firms (FF) and non-family firms (NFF). Stemming from previous studies (Martínez-Ferrero & Frías-Aceituno, 2015), ESG performance in family business context is still unexplored. Indeed, most of the scholars have focused their effort on exploring the adoption of FF’s non-financial disclosure while, to the best of our knowledge, no one has investigated the effect of the type of firm (family or non-family) in the relationship between non-financial performance (ESG) and financial performance. The results of the analyzes demonstrate that there is no significant relationship between non-financial performance (ESG performance) and the value of companies. Indeed, for both the hypotheses there is just a positive but non-significant correlation. This evidence was verified for both FF and NFF panel.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".