The Big and the Small of Tax Support for R&D in Canada
Bibliographic record
Abstract
Innovation is critical in the knowledge-based economy. It is generally accepted that governments have an important role to play in promoting innovative activity and R&D. Both the federal and provincial governments in Canada provide tax subsidies, and other forms of support, for R&D. Changes to various programs offered by the federal government were introduced in Budget 2012, most particularly related to the Scientific Research and Experimental Development (SR&ED) tax credit program. This paper analyzes the state of tax subsidies for R&D both pre- and post-budget, and at both the federal and provincial level. It is shown that there is a patchwork of effective tax subsidy rates in Canada, which vary both between and within provinces, between small versus large firms, and across sectors and types of R&D activity. The result is a misallocation of R&D resources and a system of government support that is less effective than it could be. On some dimensions Budget 2012 was a move in the right direction, but on other dimensions matters were made worse, resulting in a reconfiguration of tax support across R&D activities that is more distortionary and less efficient. Most particularly, the post-budget tax system heavily favours small firms over large firms, and labour intensive R&D over capital intensive R&D. This paper offers a lucid examination of R&D tax support pre- and post-budget, and argues persuasively that Canadian governments should adopt a more uniform, less distortionary approach to tax subsidies for R&D if they are truly interested in setting innovation free.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".