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Record W1501969595 · doi:10.55016/ojs/sppp.v8i1.42494

Potash Taxation: How Canada’s Regime is Neither Efficient nor Competitive from an International Perspective

2017· article· en· W1501969595 on OpenAlexaffabout
Duanjie Chen, Jack Mintz

Bibliographic record

VenueThe School of Public Policy Publications · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPerspective (graphical)PotashLaw and economicsEconomicsComputer scienceChemistryPotassiumArtificial intelligence

Abstract

fetched live from OpenAlex

Saskatchewan — and by extension, Canada — is the largest producer of potash in the world, accounting for over 30 per cent of global production. Perhaps the good fortune of having an abundance of such a valuable natural resource has engendered an approach whereby tax policy has not been considered a top priority. That would at least be one explanation for the alarmingly inefficient and uncompetitive potash regime that currently exists in Saskatchewan. New Brunswick’s potash-taxation regime is at least somewhat better designed than Saskatchewan’s, although it hardly stands as a model of efficiency. In both cases, poorly designed policies are hindering the provinces’ economic potential and, in turn, Canada’s. Put bluntly, when compared against its international peers, Saskatchewan’s potash-tax regime is not only the most complex and inefficient, it can also be the least competitive since its tax incentives conditioned on dated production levels and investment sizes cannot be used in perpetuity. Whereas its international peers tend to tax all potash investment projects equally, the marginal effective tax and royalty rates (METRR) on potash investment projects in Saskatchewan, either itemized or aggregated, are so widely varied that it is possible to calculate a METRR gap between two different projects as much as 48 percentage points. The convoluted nature of Saskatchewan’s regime benefits no one — not producers, investors, or the provincial government, which is left without any revenue certainty from its most significant natural resource. In fact, in recent years, where potash production and sales value rebounded substantially in Saskatchewan from 2009 levels, excessive tax allowances resulted in the province incurring three years of tax revenue losses from its potash production tax. New Brunswick’s potash-taxation regime is less complex than Saskatchewan’s, but it is not efficient. The province recently introduced a price-sensitive royalty-rate structure that imposes a higher degree of taxation on risky projects. Greater efficiency can be achieved by using a royalty system that is mainly rent based. Neither Saskatchewan nor New Brunswick needs to endure such a muddled and counter-productive approach to potash taxation. Simple solutions exist that would make both regimes far more efficient and competitive internationally. Both provinces should convert potash levies to an essentially rent-based royalty regime that ultimately taxes only revenues net of all the capital spending and operational costs. Any existing production- and sales-based ad valorem levies could be combined into a single royalty based on sales value, net of transportation and distribution costs and credited against the rent-based tax, thereby enabling, a steady revenue source for the government. Both unused capital and operational costs (deductible from the taxable rent) and sales-based royalty should be carried forward at the government’s long-term bond rate.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.152
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0100.005
Scholarly communication0.0140.002
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.039
GPT teacher head0.286
Teacher spread0.247 · 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 designNot applicable
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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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