Effect Of Tax Income, Exchange Rate, Tunneling Incentive And Multinationality On Transfer Pricing Decisions
Bibliographic record
Abstract
The-price agreed upon for the transfer of goods, services, or intangible assets between two businesses with a unique relationship based on the principles of fairness and prevalence is known as transfer pricing. Companies in the consumer goods category that list on the Indonesia Stock Exchange (IDX) between 2017 and 2021 make up the study's population. Samples result were 8 companies with a research period of 5 years and this study get 40 observation data. The study's findings demonstrate that tax income and tunneling incentive have a negative effect on transfer pricing decisions. Decisions regarding transfer pricing are unaffected by exchange rate and multinationality. Variable tax income, exchange rate, tunneling incentive, and multinationality have simultaneously effect on transfer pricing decisions. Future researchers who desire to conduct transfer pricing-related research can use this study as a guide. To acquire more data and improve the results of statistical tests and panel data regression tests, it is suggested that the research object be expanded and that additional years of research be added.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".