Would H.R. 463 Improve the Competitiveness of U.S. R&D Tax Incentives?
Bibliographic record
Abstract
This report simulates effective research credit rates for a number of research-intensive firms over the 1998-2000 period using the research and development (R&D) tax incentive rules offered by the U.S., Canada, the U.K., France, Japan, India, and Singapore. Comparisons across these simulations demonstrate that the U.S. offers among the lowest rate of tax-based R&D-related incentives. Moreover, the simulated rates show that a number of major research-intensive firms get minimal levels of incentive under the U.S. credit formula. The proposed Internal Revenue Code section 41(c)(5) alternative of H.R. 463, now under consideration in the House (as is an identical bill in the Senate), would offer higher rates of credit than the existing section 41(c)(4) alternative. A number of firms receiving minimal levels of incentive under existing law would benefit from the section 41(c)(5) alternative of H.R. 463. Based on simulated rates for the 20 firms examined, H.R. 463 would raise the average credit rate from about 3.7 percent to about 4.8 percent of the amount spent on R&D. Considering the proposal to make the credit permanent and the increased credit rate, the authors conclude that H.R. 463 would represent a significant first step in helping to level the playing field with other major industrialized nations regarding making the U.S. a destination of choice for R&D investments.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".