Do IRS Audits Deter Corporate Tax Avoidance?
Bibliographic record
Abstract
ABSTRACT We extend research on the determinants of corporate tax avoidance to include the role of Internal Revenue Service (IRS) monitoring. Our evidence from large samples implies that U.S. public firms undertake less aggressive tax positions when tax enforcement is stricter. Reflecting its first-order economic impact on firms, our coefficient estimates imply that raising the probability of an IRS audit from 19 percent (the 25th percentile in our data) to 37 percent (the 75th percentile) increases their cash effective tax rates, on average, by nearly two percentage points, which amounts to a 7 percent increase in cash effective tax rates. These results are robust to controlling for firm size and time, which determine our primary proxy for IRS enforcement, in different ways; specifying several alternative dependent and test variables; and confronting potential endogeneity with instrumental variables and panel data estimations, among other techniques. JEL Classifications: M40; G34; G32; H25.
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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 | 0.003 | 0.038 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".