The role of the Countries' Institutional Quality on the relationship between Companies' Environmental, Social, and Governance (ESG) and Financial Performance
Bibliographic record
Abstract
It is known that ESG (environmental, social, and corporate governance) performance positively influences the company's financial performance. However, little attention has been paid to macro elements that can moderatethis relationship. Based on the maxim that "institutions matter" and considering that countries with more robust institutions tend to mitigate transaction costs, information asymmetries, and investor uncertainty, this paper aims to demonstrate whether countries' institutional quality positively moderates the relationship between companies' ESG and financial performance over time. Using financial and ESG performance information from Refinitiv database and Countries' Institutional Quality from Worldwide Governance Indicators database, an unbalanced panel was built with a total of 14,699 observations, between the years 2010 and 2020, from 2,912 companies from ten countries (High Institutional Quality -Switzerland, Sweden, Canada, Australia, and Germany and Low Institutional Quality - South Africa, Brazil, India, Thailand, and China). Through the use of Linear Regression with Random Effects, using the Generalized Least Squares (GLS) estimator, and a Test of Differentiation of Regression Coefficients, it was found that the Institutional Quality of the countries positively moderates the relationship between the ESG performance and the financial performance of the companies. Upon closer analysis, it was found that institutional quality only positively and significantly moderates the relationship between environmental and social performance with financial performance. In contrast, no significant result was found for moderating the relationship between corporate governance and financial performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".