Bibliographic record
Abstract
The implementation of the Goods and Services Tax (GST) has significantly transformed taxation systems worldwide, replacing complex indirect tax structures with a unified consumption-based model. This thesis presents a comparative study of GST implementation across various countries, analysing different GST models, their effectiveness, and their impact on economic growth, inflation, and business compliance. The study examines GST frameworks in India, Australia, Canada, the European Union, and other major economies, highlighting differences in tax rates, exemptions, revenue-sharing mechanisms, and compliance structures. It explores how GST has influenced economic efficiency, tax evasion, revenue collection, and market integration. Special attention is given to the multi-tier GST model in India, the dual GST structure in Canada, and the VAT-based system in the European Union, identifying their respective strengths and challenges. Through a mix of quantitative and qualitative analysis, this research assesses the short-term and long-term impacts of GST, including its effect on GDP growth, price stability, and ease of doing business. The study also explores challenges such as high compliance costs, tax rate complexities, and technological infrastructure requirements for successful GST administration. Findings suggest that while GST has simplified tax systems, reduced tax evasion, and improved transparency, its implementation challenges vary based on economic structure, governance efficiency, and digital readiness. The research concludes with policy recommendations to optimize GST frameworks for enhanced economic benefits. Keywords: GST, tax reform, economic impact, comparative analysis, fiscal policy, revenue collection.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".