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Record W2765446234 · doi:10.1111/jifm.12077

Are offshore firms less conservative in financial reporting? International evidence

2017· article· en· W2765446234 on OpenAlexaff
Jeong‐Bon Kim, Tie Mei Li

Bibliographic record

VenueJournal of International Financial Management and Accounting · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of OttawaUniversity of Waterloo
Fundersnot available
KeywordsSubmarine pipelineMultinational corporationSubsidiaryBusinessSample (material)FinanceMonetary economicsFinancial systemEconomicsOceanography

Abstract

fetched live from OpenAlex

Abstract Using a large sample of multinational firms operating in offshore financial centers (offshore firms) from 1998 to 2014, this study investigates the financial reporting implications of economic activities involving offshore financial centers (OFCs). We find that offshore firms have a greater tendency to report less conservatively than non‐offshore firms. Moreover, we find that financial reporting is less conservative for firms operating in OFCs with more pronounced OFC attributes than for those with less pronounced OFC attributes. Finally, we also find that firms with their headquarters registered in OFCs (type I offshore firms) tend to adopt less conservative accounting practices than those with subsidiaries operating in OFCs (type II offshore firms). Our findings provide useful insights into how a multinational firm's operation in OFCs is associated with financial reporting practices.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.060
GPT teacher head0.292
Teacher spread0.232 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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