Advertising, Marketing and Promotional (AMP) Expenses in Customs Valuation
Bibliographic record
Abstract
Customs duties are an important aspect of international trade and compliance is mandatory for all companies and organisations engaged in overseas business. These customs duties generate revenue to the importing state and no link is needed between the two parties of different countries for the importing state to levy duty. Primarily, General Agreement on Tariffs and Trade (GATT) Article VII and Customs Valuation Rules, 2007, govern customs valuation in India and lay down the standards regarding transaction value, related party transactions and ‘price paid or actually payable’. Each of these authorities will be explained in this paper, along with recent rulings of Customs Excise and Service Tax Appellate Tribunal (CESTAT) and a comparative analysis of India with customs valuation in other jurisdictions, such as USA, Canada and the European Union. The objective of this paper is to establish whether advertising, marketing and promotional expenses should be included in the ‘price actually paid of payable’ of the goods or services imported. The considerations affecting this analysis are twofold: the extent of the term, ‘post-importation expenses’ and the nature of ‘buyer’s own account’. Further, the researcher will study the observations of the Technical Committee on Customs Valuation. Even though various technical terminologies are involved in the study of this issue, the finer nuances will be delineated carefully throughout the paper.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".