Bibliographic record
Abstract
Federal business subsidies have risen 140 per cent over the nine years ending in 2023–24, compared to 17 per cent over the previous nine years. New programs accounted for over half the growth. Clean economy measures, which rose $7 billion from 2014–15 to 2023–24, were the major contributor to growth in new programs. Even without the new climate change measures, business subsidies would have doubled over the period. Subsidies are likely to reach about $50 billion in 2027–28, which would represent 54 per cent of corporate income tax revenue, up from 42 per cent in 2014–15. Other key findings are: Small and medium-sized enterprises benefit disproportionately from subsidies. These firms account for about half of output in Canada but receive approximately two-thirds of business subsidies. Clean economy measures account for almost a fifth of business subsidies. The agri-food sector receives the second largest share, approximately 15 per cent, which is substantial relative to its four per cent output share. Business subsidies are concentrated in a small number of programs. In the current fiscal year, the top 10 programs (out of almost 150 programs) account for almost 60 per cent of subsidies, and the top 20 for almost 80 per cent. Spending programs account for a surprisingly small share of business subsidies — approximately 30 per cent in the current fiscal year. The tax system is the most important delivery mechanism (45 per cent), while government business enterprises and refundable tax credits account for just over 10 per cent each. What are taxpayers getting in return for the massive spending on subsidies? They are certainly getting more of the activities that are being subsidized and less of the activities that arenot. However, this change in the composition of economic activity won’t necessarily improve well-being because market prices generally allocate society’s scarce resources to their best uses. That is, if markets are functioning properly, subsidies harm rather than help economic performance. On the other hand, if subsidy programs address a market failure, the resulting reallocation of activity may be more efficient. Federal business subsidies that have the potential to improve economic performance, or more generally, to enhance well-being, because they address a market failure accounted for 64 per cent of business subsidies in 2023–24. However, measures accounting for about two-thirds of this spending fail a benefit-cost test — they are not successful in raising Canadians’ real income. These programs should be reviewed to determine if they can be restructured to deliver a positive net benefit. If not, they should be eliminated. Measures accounting for 36 per cent of total spending in 2023–24 are not intended to correct a market failure and are therefore transferring income from one group of Canadians to another while harming economic performance. These measures include general business subsidies and initiatives providing income support. These measures should be carefully reviewed to determine if their income redistribution effects can be justified in the context of the real income loss they cause. Since 2019–20, subsidies implemented to mitigate the impact of climate change and to creategood jobs by subsidizing high-productivity, high-wage industries have grown in importance andwill continue to do so. Climate change mitigation measures should only be implemented if they complement carbon pricing, the government’s main and most cost-effective instrument for reducing emissions. If they meet this minimum condition, climate change mitigation measures should be assessed based on their relative cost-effectiveness in reducing emissions. The proposition that subsidies intended to create good jobs are sound public policy is controversial. The impact of these measures should be tested against the data before additional funds are committed.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".