Country-level governance, accounting standards, and tax avoidance: a cross-country study
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of country-level governance and accounting standards on corporate tax avoidance. Design/methodology/approach This paper is an empirical work using a sample of listed companies from 36 countries. Findings This paper finds that firms resident in countries with stronger country-level governance engage in less tax avoidance. Aspects of stronger country-level governance include higher government effectiveness and regulatory quality, and stronger enforcement of law and control of corruption. This paper also finds that firms adopting international accounting standards ( IFRS ) engage in less tax avoidance than those using local accounting standards. Further examination of the effect of interactions between country-level governance and the adoption of IFRS on tax avoidance finds that there is a substitute relationship between country-level governance and the adoption of IFRS . Social implications This study has significant implications for policy makers, corporate management and academics. It documents that when a country implements governance targeting improving government effectiveness, enhancing regulatory quality, strengthening enforcement of laws and controlling corruption, this will lead to less corporate tax avoidance. It also shows that the adoption of IFRS will reduce corporate tax avoidance, probably by enhancing accounting quality and disclosure, and that the adoption of IFRS provides a bond mechanism in reducing tax avoidance in countries with weak governance. Originality/value This paper is the first study to examine the impact of country-level governance on tax avoidance at the corporate level. It is also the first study to examine how country-level governance interplays with IFRS in shaping firms’ tax avoidance activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".