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Record W2948668102

Tax Policy, Capital Structure, and Income Trusts

2007· article· en· W2948668102 on OpenAlexaff
Benjamin Alarie, Edward Iacobucci

Bibliographic record

VenueTSpace · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapital structureIncome taxDebtTax shieldIncentiveFinanceInternational taxationPublic economicsCapital (architecture)EconomicsDebt financingState income taxBusinessDouble taxationGross incomeTax reformMarket economy
DOInot available

Abstract

fetched live from OpenAlex

This article analyzes the economic benefits and costs of the income trust vehicle for entrepreneurs organizing their business affairs. In doing so it examines the precise nature of the relationships between capital structure, income taxes and income trusts, reaching the conclusion that neither tax advantages nor corporate finance efficiencies alone are able to explain the market's recent enthusiastic response to income trusts. The article proceeds as follows. Abstracting from the role of taxation, Part II outlines the economic incentives entrepreneurs have for using debt financing in their capital structures. Part III focuses on the role of taxation in motivating entrepreneurs to choose debt financing. Part IV explains why entrepreneurs are motivated to select the most efficient organizational and capital structure for their businesses. Part V applies the foregoing analysis relating to economic efficiencies and tax advantages to the income trust phenomenon, in the process showingthat the tax advantages associated with income trusts cannot be understood without understanding the underlying economic efficiencies associated with debt financing, and that other economic efficiencies cannot be fully understood without contemplating the tax considerations associated with debt financing. Part VI concludes.

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)0.000
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 score0.516
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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
Published2007
Admission routes1
Has abstractyes

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