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Record W3119978661 · doi:10.1016/j.jwb.2020.101186

Host country corporate income tax rate and foreign subsidiary survival

2021· article· en· W3119978661 on OpenAlexaff
Bassam Farah, Rida Elias, Dwarka Chakravarty, Paul W. Beamish

Bibliographic record

VenueJournal of World Business · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsWestern University
FundersUniversity Research Board, American University of Beirut
KeywordsMultinational corporationSubsidiaryForeign direct investmentSample (material)International businessEconomicsCorporate taxBusinessIncome taxHost (biology)Monetary economicsInternational economicsTax avoidanceDemographic economicsDouble taxationPublic economicsMacroeconomicsFinance

Abstract

fetched live from OpenAlex

Host country tax considerations are critical to multinational enterprise (MNE) foreign direct investment decisions, but understudied in international business (IB) research. We address this gap by examining the relationship between host country corporate income tax rates (HCCITRs) and foreign subsidiary survival. We develop our hypothesis drawing upon location/country-specific advantage theory and international tax literature. Our longitudinal sample (1990–2013) comprises 13,468 MNE subsidiaries in 78 countries. Results indicate a one standard deviation (7.7 %) decrease in HCCITR increases subsidiary survival probability (at any given time) by 33 %. This effect is stronger compared to several well studied explanatory variables in IB survival analysis.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.213
Teacher spread0.189 · 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 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

Citations37
Published2021
Admission routes1
Has abstractyes

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