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Record W4386000693 · doi:10.1111/1911-3846.12900

Does tax enforcement disparately affect domestic versus multinational corporations around the world?

2023· article· en· W4386000693 on OpenAlexvenueno aff
Lisa De Simone, Bridget Stomberg, Brian Williams

Bibliographic record

VenueContemporary Accounting Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationEnforcementBusinessTax avoidanceInternational economicsSubsidiaryTax policyInternational tradeCorporate taxDouble taxationTax reformPublic economicsEconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

Abstract Global tax enforcement policies have received increased attention since the financial crisis, with much stated focus on curbing perceived harmful tax practices of multinational corporations. Yet there is a dearth of evidence on possible differential effects of home‐country tax enforcement on multinationals. We take a step toward filling this void in the tax policy discussion by examining whether there is a differential relation between changes in home‐country enforcement and the tax avoidance of domestic versus multinational corporations. Using OECD data on 50 countries from 2005 to 2019, we find increases in home‐country enforcement are associated with lower levels of tax avoidance for domestic firms than for multinational corporations. Using a subset of firms from the Bureau van Dijk database, we find that multinationals avoid more tax in foreign countries when home‐country enforcement increases. Results are stronger for multinationals with a higher proportion of subsidiaries in low‐tax countries and when enforcement spending is low. These findings have implications for policy‐makers and highlight the importance of coordinated enforcement efforts across jurisdictions—such as the recently proposed global minimum tax—to successfully curb multinationals' worldwide tax avoidance.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.122
GPT teacher head0.367
Teacher spread0.245 · 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 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

Citations40
Published2023
Admission routes1
Has abstractyes

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