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Record W2599490427 · doi:10.55016/ojs/sppp.v7i1.42492

An International Comparison of Tax Assistance for Research and Development: Estimates and Policy Implication

2014· article· en· W2599490427 on OpenAlexaffabout
John Lester, Jacek Warda

Bibliographic record

VenueThe School of Public Policy Publications · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEconomicsTax policyEconometricsPublic economicsRegional scienceTax reformGeography

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.021
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.197
GPT teacher head0.423
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2014
Admission routes2
Has abstractyes

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