Tax Aggressiveness and Accounting Fraud
Bibliographic record
Abstract
ABSTRACT There are competing arguments and mixed prior evidence on whether firms that are aggressive in their financial reporting exhibit more or less tax aggressiveness. Our research contributes to resolving this issue by examining the association between aggressive tax reporting and the incidence of alleged accounting fraud. Relying on several proxies for tax aggressiveness to triangulate our evidence, we generally find that tax aggressive U.S. public firms are less likely to commit accounting fraud. However, we caution that our results are sensitive to how tax aggressiveness is measured. More specifically, four (two) of the five (three) proxies for firms’ effective tax rates (book‐tax differences) load positively (negatively) during the 1981–2001 period, implying that fraud firms are less tax aggressiveness. Our inferences persist when we isolate the 1995–2001 period in which accounting impropriety steeply rose and corporate tax compliance steeply fell. Moreover, we continue to find that tax aggressive firms are less apt to fraudulently manipulate their financial statements when we apply factor analysis to identify tax avoidance with a common factor extracted from the underlying proxies and match on propensity scores to ensure that the fraud and nonfraud samples have very similar nontax characteristics.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".