Currencies, digital dollars, tax dilemmas: exploring the ties between cryptocurrencies and tax aggressiveness
Bibliographic record
Abstract
Purpose In the early 21st century, the convergence of corporate social responsibility (CSR) and cryptocurrencies has significantly impacted the corporate and financial world. This study aims to examine the intersection of CSR, more specifically corporate tax behavior, and corporations’ engagement with cryptocurrencies. Since Bitcoin’s emergence in 2008, these digital assets have disrupted traditional financial systems, prompting inquiries about their environmental impact and ethical implications for investors. This research aims to evaluate whether corporations involved in cryptocurrencies exhibit distinct tax behavior compared to those abstaining from these digital assets, with a particular focus on tax aggressiveness. Design/methodology/approach This study analyzes a sample of US-listed corporations that publicly associate themselves with cryptocurrencies, contrasting them with a similar group of corporations that do not. Using binary logistic regression, this study explores the relationship between corporate cryptocurrency engagement and tax aggressiveness, considering factors such as environmental, social and governance (ESG) scores and firm size. Findings The findings indicate that corporations with higher ESG scores are less likely to participate in cryptocurrencies, suggesting a potential perception of these assets as less socially responsible. Surprisingly, less tax-aggressive corporations show a greater inclination toward cryptocurrency involvement, challenging the assumption that such engagement inherently correlates with irresponsible tax behavior. Originality/value This research contributes to broader discussions on CSR, signaling theory and the evolving ethical and regulatory landscape surrounding cryptocurrencies. By examining corporate tax behavior within the context of cryptocurrency participation, this study sheds light on the intricate dynamics at play in this emerging digital landscape.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".