A Cross‐national Comparison of R&D Expenditure Decisions: Tax Incentives and Financial Constraints*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We provide evidence on the impact of tax incentives and financial constraints on corporate R&D expenditure decisions. We contribute to extant research by comparing R&D expenditures in the United States and Canada, thereby exploiting the differences in the two countries' R&D tax credit mechanisms and generally accepted accounting principles. The two tax incentive mechanism designs are consistent with differing views of the degree of financial constraints faced by firms in these economies. Our sample also allows us to explore the effects of capitalizing R&D on Canadian firms. Employing a matched design, we document relations between tax credit incentives and R&D spending consistent with both Canadian and U.S. public companies responding as though they are not financially constrained. We estimate that the Canadian credit system induces, on average, $1.30 of additional R&D spending per dollar of taxes forgone while the U.S. system induces, on average, $2.96 of additional spending. We also find that firms that capitalize R&D costs in Canada spend, on average, 18 percent more on R&D. Collectively, this evidence is important to the ongoing debates in both countries concerning the appropriate design of incentives for R&D and is consistent with the assumptions found in the U.S. tax credit system, but not those found in the Canadian system.
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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 | 0.003 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it