An International Comparison of Tax Assistance for Research and Development: Estimates and Policy Implication
Bibliographic record
Abstract
Business spending on research and development (R&D) is generally recognized as a private activity providing broader economic benefits that justify government support. But subsidizing R&D has costs as well as benefits, and governments need to exercise judgement to ensure that subsidies are set at a level that results in a net economic benefit for society as a whole, not just for the recipients of the assistance. A key finding of the international comparison undertaken in this paper is that Canada and nine other of the 36 countries in the comparison group are providing R&D subsidies that are likely too high to generate a net economic benefit. Subsidy rates in this group of countries range from 25 to 45 per cent. The risk of excessive subsidization is confined to small firms in Canada, which receive a subsidy of almost 41 per cent through the tax system. Canada’s subsidy rate for small firms is the third highest, behind Chile and France. Other countries providing subsidy rates close to 40 per cent are Spain and India. This paper also assesses several design features of tax assistance measures, including enhanced benefits for small and young firms, refundability of benefits and incentives based on increases in R&D spending above a base level. While the best policy for R&D subsidies may be a uniform rate for all businesses regardless of age or size, the case for favouring young firms is somewhat stronger than for favouring all small firms. Focusing on young firms avoids providing benefits to small firms that are not growth-oriented; but, it is difficult to design a program that can be completely restricted to young firms since entrepreneurs would have an incentive to create new firms to avoid losing higher benefits. Even with this “leakage”, however, an age-dependent incentive could be more cost-effective than size-dependent enhanced benefits. There is a particularly strong case for providing refundability to young firms, which are unlikely to have taxable income while the first round of R&D is undertaken While refundability for large firms has the advantage of increasing the effective subsidy rate on R&D to its target level, it runs the risk of revenue losses as multinational firms have less incentive to ‘book’ taxable income in Canada. A reasonable compromise would be to adjust the value of unused credits and deductions to maintain their present value. International comparisons of tax assistance for R&D typically highlight country rankings and express satisfaction with the most generous regimes. This paper draws attention to the possibility of providing too much of a good thing, a warning that governments in Canada should keep in mind when preparing next year’s budgets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".