Tax Reporting Behavior Under Audit Certainty
Bibliographic record
Abstract
ABSTRACT This study uses a confidential data set of firms assigned to the Internal Revenue Service's Coordinated Industry Case (CIC) program to examine the effect of audit certainty on firms' tax reporting behavior. We first model the determinants of assignment to the program. Although the ability and incentive to avoid taxes are related to CIC assignment, we find that the IRS assigns firms primarily based on size and complexity. We then test whether audit certainty has a detectable effect on tax payments. Our results show that tax payments do not change when firms enter the CIC program, suggesting the CIC program does not have higher deterrence or enforcement effects relative to the IRS's standard selection and audit process for large corporations not included in the CIC program. However, supplemental analysis suggests that audit certainty does alter managers' expectations regarding future tax payments. Our paper provides new empirical evidence on the strategic game between the taxpayer and the tax authority and has important implications for tax authorities as they consider the costs and benefits of certain audit programs.
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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 | 0.008 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".