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Record W3125655922 · doi:10.55016/ojs/sppp.v11i1.43144

Business Subsidies in Canada Comprehensive Estimates for the Government of Canada and the Four Largest Provinces

2018· article· en· W3125655922 on OpenAlexaboutno aff
John Lester

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyGovernment (linguistics)GeographyBusinessEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Business subsidies in Canada: the “winner” is Alberta; the loser is the taxpayer The federal government and the four largest provinces in Canada spend about $29 billion a year on business subsidies, delivered through program spending, the tax system, government business enterprises and direct investments by government. These subsidies represent almost half of the corporate income tax revenue collected by the five jurisdictions. Surprisingly, given its reputation as a bastion of free enterprise, Alberta is the most prolific subsidizer. In the 2014-15 fiscal year, per person subsidies were $640 in Alberta, about $100 ahead of the next most generous jurisdiction, Québec. Alberta has probably added to its “lead” through measures introduced in the October 2015 Fiscal Update and the 2016 budget. Alberta also stands out by having the least transparent public reporting of business subsidies. What motivates governments to subsidize business? Abstracting from cynical efforts to win votes, business subsidies have two broad objectives: to improve economic performance and to achieve a social objective by supporting specific firms, industries or regions. On average in the five jurisdictions, the split between the two categories is about 70-30 in favour of economic development measures. Assessing value for money from programs with a social objective is subjective, but measures intended to improve economic performance should be assessed on their ability to raise real income. Business subsidies can only raise real income if markets fail to allocate labour and capital to their best uses. The classic case is R&D. When a firm undertakes R&D, some of the knowledge created inevitably spills over to the benefit other firms. Firms are focused on their own benefits and costs when deciding how much to spend on R&D, not the benefits received by other firms, so society has an interest in encouraging additional R&D. While markets generally do a good job allocating capital to its most productive uses, governments express concern about the ability of small firms to access external financing. Just over half of business subsidies are intended to address these two issues. Governments also provide subsidies in order to create what are often described as “good jobs,” meaning employment in high-wage, high-productivity industries. There is ample evidence that wages differ by sector even after differences in worker skills and working conditions are taken into account. That opens up the possibility that subsidizing high-wage jobs will make us better off. Almost 10 per cent of government subsidies are pursuing “industrial policy” objectives. But real income won't necessarily go up, even in these circumstances. Benefit-cost analyses of key programs suggest that, at best, only a third of subsidies intended to raise real income achieve their objective. The main reason these subsidies are unsuccessful is that they have to be funded, either by raising taxes or cutting program spending, both of which harm economic performance. And avoiding the pitfall of excessive subsidization can be challenging. For example, small firms performing R&D get about 43% of their funding from governments, which is substantially beyond an effective level. Industrial policy measures are particularly tricky to get right. Governments have to identify sectors and firms that pay a premium for a given set of skills and working conditions, determine the subsidy that generates a social benefit net of the costs of providing assistance and avoid transferring income from low to high-wage taxpayers. With so much money at play, business subsidies should be reported more transparently and managed more effectively. For greater transparency, governments should prepare a comprehensive annual report on business subsidies delivered through program spending, the tax system and through the activities of government business enterprises. The report would describe the programs, state their objectives and report funding levels. When discussing program objectives, the report should set out in general terms the expected benefits and costs of government intervention and discuss who benefits from the measure and who is expected to pay for it. Making a commitment to set out the expected benefits and costs of all new business subsidies as they are introduced might prevent the worst offenders from being implemented in the first place.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.276
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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