Does the presence of a sustainability committee strengthen the impact of ESG disclosure on tax aggressiveness? Insights from North America
Bibliographic record
Abstract
Abstract We investigate the influence of ESG disclosure on tax aggressiveness within the North American Travel and Leisure (T&L) sectors, specifically examining the role of sustainability committees in this relationship. Our analysis utilizes longitudinal panel data from the USA B3000 and Canadian S&P/TSX indices over the period from 2010 to 2020. Employing fixed-effects panel quantile regression with two distinct measures of tax aggressiveness, our findings indicate that firms with a focus on ESG tend to display higher levels of tax aggressiveness. This suggests that some companies might use strong ESG performance as a facade to obscure aggressive tax strategies. Moreover, our research introduces new evidence that the existence of sustainability committees can both hinder corporate tax aggressiveness and foster an ethical corporate culture, which aligns higher ESG engagement with lower tax aggressiveness. Our study underscores the importance of fostering tax compliance in T&L companies, emphasizing that individuals and corporations, which often seek direct state benefits, regard robust public services as essential for encouraging adherence to tax regulations. Furthermore, sustainability committees play a crucial role in enabling firms to address broader social issues, including tackling tax aggressiveness, thus shaping their sustainability agendas.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".