Is Silence Golden? An Investigation of Zero Unrecognized Tax Benefit Firms
Bibliographic record
Abstract
ABSTRACT We find over 31 percent of U.S. firms report a zero ending balance for their unrecognized tax benefits (UTBs). We refer to these firms as zero UTB firms and explore explanations for their prevalence. We find zero UTB firms are less complex than positive UTB firms. Specifically, they are smaller, less profitable, have less R&D and foreign earnings, and fewer operating segments and XBRL tags. We also find some evidence that these firms have less efficient tax departments. When splitting zero UTB firms by tax haven activity, we find that those with haven operations are more aggressive in their financial reporting than haven firms with positive UTBs. We also find that zero UTB firms both with and without tax haven operations are subject to less IRS scrutiny than positive UTB firms. Overall, although zero UTB firms are generally less complex, our results suggest that a subset may warrant further scrutiny. JEL Classifications: H25; H26; H32; M41; M48.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".