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Record W3126613143

An International Comparison of Tax Assistance for R&D: 2017 Update and Extension to Patent Boxes

2018· article· en· W3126613143 on OpenAlexaffabout
John Lester, Jacek Warda

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of WindsorUniversity of Calgary
Fundersnot available
KeywordsExtension (predicate logic)BusinessInternational tradeEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Business investment in research and development (R&D) is widely recognized as providing benefits to the broader economy that exceed the benefits to the firms that perform the R&D. As a result of this externality or spillover, most governments provide support for R&D in order to encourage more of it. In 2017, 29 of the 35 members of the Organisation for Economic Co-operation and Development (OECD) provided tax incentives for spending on R&D. That’s up slightly since 2014, when we last prepared an international comparison of tax assistance for R&D. On the other hand, average support levels edged down from 2014 to 2017. In addition to these expenditure-based measures, 15 OECD countries provide preferential tax treatment for the income generated by commercializing R&D and other innovative activities. These income-based measures are often described as patent boxes, since they first applied to income realized from patented products and processes. In most cases, the qualifying patents did not have to be based on R&D performed in the country offering the incentive, so patent boxes were criticized for creating an incentive to shift taxable income without encouraging additional R&D. Recently, however, most countries have accepted the OECD recommendation that both the R&D and the income from its commercialization must be located in the same jurisdiction before an income-based incentive can be provided. With this linkage, income-based incentives can be a useful policy tool, particularly for large firms. Income- and expenditure-based incentives are likely to have similar impacts on the amount of R&D undertaken by large firms, but income-based measures have the advantage of providing a greater incentive to commercialize R&D in the implementing jurisdiction. They also blunt the incentive to shift the taxable income generated by commercializing R&D to lower-tax jurisdictions. However, smaller firms, who are more likely to be cashflow constrained, will respond less strongly to income-based measures since the subsidy is available with a delay. Further, small firms have limited opportunities to shift taxable income across international borders. Should the federal government implement an income-based tax incentive for R&D performed by large firms? A key consideration is what happens to tax revenue on income from commercialization of R&D. If a lower tax rate results in higher revenue as a result of tax base shifting effects, income-based measures have a clear advantage over their expenditure-based counterparts. Some competing jurisdictions have very low corporate income tax rates, so feasible reductions in federal tax rates may not generate tax base shifting effects large enough to make the policy a success. More information on how multi-national enterprises shift intellectual-property income out of Canada is required before proceeding with income-based tax incentives for R&D.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.541
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.131
GPT teacher head0.380
Teacher spread0.250 · 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 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
Published2018
Admission routes2
Has abstractyes

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