<scp>IRS</scp> scrutiny and corporate innovation
Bibliographic record
Abstract
Abstract The IRS administers tax laws enacted by Congress. As part of the IRS's duties, they often consider taxpayers' financial statements to help ensure accurate tax reporting and payments. We posit that enhanced financial statement disclosures of tax information under FASB Interpretation Number 48 (FIN 48) lead to more IRS scrutiny and alter the incentives for corporate innovation. Using patent applications as a measure of corporate innovation, we employ a difference‐in‐differences research design with publicly listed US firms as the treatment group and privately held US firms not subject to the disclosure requirements as the control group. We find robust evidence that, following the onset of FIN 48, the number of patent applications by publicly listed firms decreased between 15.4% and 24.3% relative to private firms. This decline in patent applications is attributable to incremental innovation, suggesting that firms lower innovation related to projects with tax benefits that are more likely to be scrutinized by the taxing authorities. These findings suggest that there are real effects of IRS scrutiny and, in particular, real effects of tax disclosures under FIN 48 on corporate innovation.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".