MétaCan
Menu
Back to cohort
Record W3170694393 · doi:10.1111/1911-3846.12703

Tax Haven Incorporation and the Cost of Capital*

2021· article· en· W3170694393 on OpenAlexvenueno aff
Christina Lewellen, Landon M. Mauler, Luke Watson

Bibliographic record

VenueContemporary Accounting Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsTax havenTax avoidanceTax creditEconomicsCost of capitalPublic economicsIndirect taxHavenCorporate taxCost of equityMonetary economicsDouble taxationBusinessTax reformMicroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT Incorporating the firm's corporate parent in a tax haven is a major decision that receives significant attention from many stakeholders, yet certain implications of this corporate strategy remain unclear. While tax haven incorporation offers tax savings, it also imposes risks that are potentially costly and hence important to consider. We predict and find a higher cost of equity capital in firms with parent companies that are incorporated in tax havens but that are primarily based in nonhaven countries. We also predict and find that the observed cost of equity premium is more pronounced in firms with greater tax risk, firm‐level information risk, and country‐level legal risk. We also employ corporate inversions in a difference‐in‐differences test and again find a positive relation between tax haven parent incorporation and the cost of capital. Our findings imply that an increased cost of capital is a material cost of tax haven parent incorporation. We contribute to the literatures on valuation of tax haven use, tax and nontax costs of corporate tax strategies, corporate inversions, and the relation between taxes and the cost of capital. Our study provides evidence on the tax and nontax risks of a uniquely observable tax strategy (i.e., tax haven parent incorporation) that could factor into firms' decisions about whether to incorporate in a tax haven and policymakers' efforts to deter such activity.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.071
GPT teacher head0.301
Teacher spread0.230 · 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 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".

Quick stats

Citations28
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueContemporary Accounting ResearchSame topicCorporate Taxation and AvoidanceFrench-language works237,207