Tax audits and the policing of corporate taxes: Insights from tax executives
Bibliographic record
Abstract
Abstract We interview public company tax executives to provide new evidence on how corporate taxpayers experience and navigate the income tax audit process. Interviewees describe being “targeted” by “tax police” and having to “defend” their positions. Thus, we adopt a structural metaphor of tax audits as police investigations and use a framework from the policing literature to explain what influences taxpayers' perceptions of fairness during audits. Perceptions of fairness are important as targets of investigations are more likely to cooperate and accept outcomes when they perceive policing processes as fair. Tax executives aim to obtain fair and consistent treatment by compiling documentation, consulting with peers and external advisors, and educating tax agents. Audits are adversarial, however, and taxpayers also act strategically to secure favorable outcomes and appeal or litigate when they believe outcomes are unfair. Interviewees note variation in the extent to which tax authorities create frameworks that facilitate fair audit processes and whether tax agents implement these frameworks. Our study offers new insights into the tax audit process from corporate taxpayers' perspectives. First, public company taxpayers view tax audits as redundant to financial statement audits of their tax positions. Thus, tax audits may have limited scope to deter tax noncompliance. Second, tax executives are not passive actors; they take deliberate actions to shape audit outcomes. Third, audits are less efficient for everyone when taxpayers perceive them as procedurally unfair. Investments by tax authorities that increase perceptions of fairness may enhance audit efficiency by increasing taxpayers' cooperation and acceptance of outcomes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".