ESG Ratings and Financial Performance in the Global Hospitality Industry
Bibliographic record
Abstract
Existing research critically examines the influence of environmental, social, and governance (ESG) ratings on corporate financial performance (CFP), with outcomes varying considerably. This study employs a dataset of publicly traded firms across 16 countries within the hospitality sector from 2005 to 2022 to examine the ESG-CFP relationship. Fixed effects regression results demonstrate a positive linkage between ESG ratings and CFP, utilizing both comprehensive ESG ratings and discrete pillar ratings. These findings remain robust across various performance measures including return on assets, return on equity, and Tobin’s Q. Heteroscedasticity and endogeneity concerns are mitigated through generalized least squares and two-stage least squares methods, respectively. Moreover, the positive impact of ESG on CFP exhibits greater potency in the United States relative to other countries and was more pronounced during the COVID-19 era. These findings offer valuable insights for business executives, investors, and policymakers in supporting ESG initiatives, guiding investment decisions, and formulating effective policy directives.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".