Utilizing the Mutual Agreement Procedure in Resolving International Tax Disputes in Tanzania: Prospects and Challenges
Bibliographic record
Abstract
This article examines the utilization of the Mutual Agreement Procedure (MAP) in resolving tax disputes under Tanzanian Double Taxation Agreements (DTAs). The MAP, provided for under Article 25 of the Organization for Economic Cooperation and Development (OECD) Model Tax Convention on Income and on Capital 2017, and United Nations Model Double Taxation Convention between Developed and Developing Countries, is designed to prevent double taxation and facilitate cooperation between tax authorities of treaty partners. Tanzania has concluded DTAs with several countries, including Canada, India, and South Africa, which incorporate MAP provisions. However, many of these treaties are based on outdated models that do not reflect modern international standards such as the OECD’s 2012 Manual on Effective Mutual Agreement Process (MEMAP) and the Base Erosion and Profit Shifting (BEPS) Action 14 minimum standards. The study employs a doctrinal analysis of statutes and treaties, insights from tax practitioners and taxpayers, and comparative evaluation of international best practices. Findings reveal that while MAP offers an avenue for amicable dispute resolution, its practical use in Tanzania is limited due to a lack of clear timelines, the absence of binding arbitration, limited taxpayer awareness, and institutional constraints within the Tanzania Revenue Authority (TRA). The article concludes that MAP remains a valuable tool for enhancing tax certainty, protecting investors, and promoting international economic cooperation. Nonetheless, its effectiveness in Tanzania requires reform through modernization of DTAs, codification of MAP procedures in domestic law, and institutional strengthening of TRA to meet international best practices.
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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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".