How Has Tax Affected the Changing Cost of R&D? Evidence from Eight Countries
Bibliographic record
Abstract
This chapter describes the evolution of the tax treatment of investment in R&D in Australia, Canada, France, Great Britain, Germany, Italy, Japan and the USA between 1979 and 1994. Estimates of the cost of R&D are provided and the methodology used is contrasted with other ones used in the literature. Four findings are highlighted. First, there appear to be substantial differences in the cost of R&D across countries at any given point in time. Second, there has been a general trend towards more generous tax treatment of R&D, although some countries have moved much more rapidly than others. Third, there is an increasing diversity in the cost of R&D between countries. Finally, in order to illustrate the substantial within-country heterogeneity that can arise from differences in design and implementation, several stylised tax systems are applied to a sample of firm level data and the resulting distribution of tax rates is presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".