Empirical Studies of Taxation in BRICS Countries: Literature Review
Bibliographic record
Abstract
The cross-country studies in taxation could provide a number of unique advantages over national studies The paper aims to analyze existing empirical research devoted to tax issues in BRICS countries to classify them according to their aim and empirical base and answer the question: could BRICS countries really be compared in tax frame, and for what extend. The sample for this study includes all papers available in Science Direct, Google Scholar and E-Library databases, which include pair of words “tax” and “BRIC(S)” or “налог” and “БРИКС” simultaneously in the title, abstract, keywords. Literature sources were sorted by relevance and search depth was not limited. The literature review showed that studies devoted to taxation in BRICS countries have different approaches based on their aim and data. The most part of research is based on tradition approach (tax rates and tax legislation) only few research of taxes and related topics in BRICS are based on indices. We also separated several directions in studies related to taxation in BRICS countries: comparative description of taxation; tax avoidance and tax evasion and their determinants; examination of the particular tax or particular industry to find relevant experience to apply; examination the interaction between taxes and other variables (taxes and economic development; taxes and economic inequality; taxes and environment). The majority of articles do not aim to find some regularities for BRICS countries they only use these countries as random sample comparing them with G7 or MINT or other groups of countries. While almost the BRICS member countries are rapidly developing, they are diverse in culture, economic conditions and social and political structures. From this perspective we could not address to BRICS countries as a homogeneous group that could be used for observation in tax frame.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".