Defining and Managing Corporate Tax Risk: Perceptions of Tax Risk Experts*
Bibliographic record
Abstract
ABSTRACT We examine the “black box” of corporate tax risk management by providing unique insights into practitioners' tax risk perceptions, tax risk management practices, and influences leading to variation in tax risk management practices across firms. Opening this black box is important as tax risk has become an increasingly relevant aspect in corporate tax practice—little is yet known about how firms define and manage tax‐related risks. We perform our analysis based on 33 expert interviews, which we conducted with 42 tax risk experts. The first important finding from our interviews is that tax risk is a multifaceted and context‐dependent construct, consisting of six tax risk components: financial, reputational, compliance, political, tax process, and personal liability risk. Furthermore, we find that perceived tax risk varies substantially between corporate insiders and corporate outsiders. Our interview insights further reveal that firms' most frequently used tax risk management practices relate to some form of tax communication. The tax departments' rationale for using tax communication as a key tax risk management practice is to protect the firm—in particular, the CFO—from three types of pressure: public pressure, peer pressure, and regulatory pressure. Our analysis has important implications for future studies. First, our insights reveal that several tax risk components are not sufficiently covered by common tax risk measures used by the archival literature. Second, we find that communication has a key role in managing tax risk. This deviates from the purely supportive role that extant risk management frameworks have assigned to communication.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".