ESG Strategy and Tax Avoidance: Insights from a Meta-Regression Analysis
Bibliographic record
Abstract
This research examines the relationship between environmental, social, and governance (ESG) criteria and tax behavior, with a particular focus on tax avoidance (TA). Despite the extensive literature on ESG and tax behavior, there remains a research gap concerning their interaction in the financial sector. The study is based on a dataset of 125 observations from 33 articles covering the period 2012–2023. The results of the meta-regression suggest that both ESG and TA indicators account for the different findings of the primary studies. Part of the observed heterogeneity can also be explained by the diversity of data samples and econometric approaches. Using the results of the meta-regression, we attempt to predict the association between ESG and TA in hypothetical and plausible study designs. The findings show no or small-to-moderate association between the two, suggesting that companies tend to separate ESG strategies from TA and underscoring the need for more consistent measurement practices. Notably, the link between the main variables appears to be strengthened in environments with extreme behaviors, both in terms of ESG and tax strategy. Distinct from prior meta-studies that centered on CSR and taxation, our analysis isolates the ESG/TA nexus by accounting for measurement heterogeneity (different ESG and TA proxies) and demonstrates that extreme behaviors largely drive the observed association. By examining the determinants of the heterogeneity of primary research into the ESG/TA relationship, this meta-analysis provides valuable insights that can guide future research, practical implementation, and regulatory policies. In particular, researchers should rely on long-run measures of TA (e.g., multi-year ETRs) and harmonized ESG indicators to reduce bias and enhance comparability across studies, thereby providing policymakers with more robust and consistent evidence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".