Bibliographic record
Abstract
The knowledge created by private spending on research and development (R&D) generates benefits for society as well as for the firm performing the research, so there is a strong case for government intervention to encourage R&D. But intervening in the market has costs, and these costs may exceed the benefits derived from the additional R&D. This article describes an approach for assessing the net economic benefit arising from R&D support programs and presents results for two federal programs: the scientific research and experimental development (SR & ED) tax credit and the industrial research assistance program (IRAP).The benefit-cost approach used in this article calculates the impact of R&D subsidies on real income taking into consideration the benefit created by knowledge spillovers from the induced R&D, the cost of financing the subsidies with taxes that unavoidably harm economic performance, the cost of shifting resources from their market-determined uses, and administration and compliance costs. The SR & ED credit has two components: a regular 20 percent credit and an enhanced 35 percent refundable credit for smaller Canadian-controlled firms. The regular credit generates a net economic benefit, but the enhanced credit fails a benefit-cost test, owing to higher compliance costs and a higher subsidy rate. IRAP also fails a benefit-cost test, despite the assumption that IRAP-funded R&D generates higher spillovers than R&D funded by the tax credit, owing to the high cost of administering and complying with the program.The policy recommendations flowing from the analysis in this article are that the enhanced SR & ED tax credit should be aligned with an unchanged regular credit rate and that the IRAP model of providing firms with a substantial amount of one-on-one advice and imposing relatively burdensome reporting requirements should be revisited in order to reduce costs. The 2012 federal budget took a different approach: the regular credit rate was reduced to 15 percent, and IRAP funding was doubled without any changes to the program's structure. In contrast to the policy recommendations made in this article, these changes will reduce the net benefit from both the SR & ED tax credit and IRAP. In particular, without any changes to program structure, the additional IRAP funding will substantially increase the net loss from the program.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".