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Record W4390749699 · doi:10.3390/jrfm17010026

Tax Tightrope: The Perils of Foreign Ownership, Executive Incentives and Transfer Pricing in Indonesian Banking

2024· article· en· W4390749699 on OpenAlexvenueno aff
Vidiyanna Rizal Putri, Nor Balkish Zakaria, Jamaliah Said, Farha Ghapar, Rizqa Anita

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessIncentiveTransfer pricingTax avoidanceStock exchangeForeign ownershipAccountingRevenueTax incentiveHarmFinanceMonetary economicsPublic economicsDouble taxationEconomicsForeign direct investmentMarket economyMultinational corporation

Abstract

fetched live from OpenAlex

Despite being a crucial source of funding for the government, tax revenue collection in Indonesia has yet to reach its ideal and satisfying level for the economy. Therefore, this study explores the impact of executive incentives, foreign ownership, and transfer pricing on tax avoidance. The conventional banks of Indonesia that were listed on the Indonesia Stock Exchange (IDX) between 2015 and 2019 are the subject of this study. This study employed a purposive selection technique, with a final sample of 17 banks chosen after screening to ensure they met the requirements of having foreign ownership and not having suffered losses during the study year. The results of this study show that while CEO incentives harm tax avoidance, foreign ownership has a beneficial effect. Furthermore, tax avoidance is not significantly impacted by transfer pricing. The findings of this investigation open the door for accountable authorities in the economy.

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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.199
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

Citations3
Published2024
Admission routes1
Has abstractyes

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