Assessing Tax Risk: Practitioner Perspectives
Bibliographic record
Abstract
ABSTRACT This study uses insights from tax practitioners and tax authorities to define and develop an estimate of ex ante tax risk that is independent of common tax outcomes studied in prior literature. Validation tests confirm that our tax risk measure (i) represents the predictable and unpredictable uncertainty inherent in the three sources of tax risk (i.e., economic risk, tax law uncertainty, and inaccurate information processing) and (ii) is a construct different from tax avoidance, tax uncertainty, and general business risk. Using our tax risk measure, we address two research questions of interest to academics and practitioners. First, we examine the association between tax risk and long‐run tax avoidance and find a negative association between tax risk and future long‐run cash effective tax rates (ETRs). Second, we consider the extent to which unrecognized tax benefits (UTBs) reflect tax risk, tax avoidance, or financial reporting incentives and demonstrate that our tax risk measure explains a substantial portion of UTBs, incremental and relative to measures of information risk, conditional conservatism, unconditional conservatism, and tax avoidance. Our study offers a measure of tax risk that, consistent with the Scholes‐Wolfson paradigm, reflects the tax risk inherent in all business activities, not just tax avoidance activities; has unique industry effects; and contributes to our understanding of the factors that affect tax planning decisions and result in variation in firms' ETRs. Our findings will help managers and tax practitioners focus on industry‐specific tax risk components, assess risk during tax planning initiatives, exercise caution when engaging in additional risk if ETRs are low, and adapt tax risk strategies to fit specific company needs. We enhance future tax research by improving the definition and measurement of tax risk.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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; both teacher heads agree on what is shown here.
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".