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The Taxation of Company Distributions in Respect of Hybrid Instruments in South Africa: Lessons from Australia and Canada

2021· article· en· W3120840252 on OpenAlexaboutno aff
Liezel Gaynor Tredoux, Kathleen Van der Linde

Bibliographic record

VenuePotchefstroom Electronic Law Journal/Potchefstroomse Elektroniese Regsblad · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)DebtFinancial instrumentLegislationLiabilityEconomicsTax avoidanceTax lawPublic economicsBusinessIncentiveFinanceTax reformLawMarket economyPolitical science

Abstract

fetched live from OpenAlex

Tax legislation traditionally distinguishes between returns on investment paid on equity and debt instruments. In the main, returns on debt instruments (interest payments) are deductible for the paying company, while distributions on equity instruments (dividends) are not. This difference in taxation can be exploited using hybrid instruments and often leads to a debt bias in investment patterns. South Africa, Australia and Canada have specific rules designed to prevent the circumvention of tax liability when company distributions are made in respect of hybrid instruments. In principle, Australia and Canada apply a more robust approach to prevent tax avoidance and also tend to include a wider range of transactions, as well as an unlimited time period in their regulation of the taxation of distributions on hybrid instruments. In addition to the anti-avoidance function, a strong incentive is created for taxpayers in Australia and Canada to invest in equity instruments as opposed to debt. This article suggests that South Africa should align certain principles in its specific rules regulating hybrid instruments with those in Australia and Canada to ensure optimal functionality of the South African tax legislation. The strengthening of domestic tax law will protect the South African tax base against base erosion and profit shifting through the use of hybrid instruments.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.233
Teacher spread0.212 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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