Tax Policy and Foreign Direct Investment: Empirical Evidence from Mauritius
Bibliographic record
Abstract
This study demonstrates, through the use both qualitative and quantitative data, that there are several factors determining Foreign Direct Investment flows between two countries. A total of 180 accountants were surveyed in this study, whereby the majority of respondents agreed that Capital Gains Tax is an important factor determining FDI flow within a tax treaty but is not the only significant factor. The study also used regression analysis through a gravity equation to confirm the survey’s conclusion. Using Mauritius and a host of its tax treaty partners as proxies, it was found that Gross Domestic Product per capita, Capital Gains Tax, common language and distance were major factors affecting Foreign Direct Investment flow in a bilateral tax treaty. This study gives a good insight on the reasons why foreign investors use the Mauritian tax treaty network as a platform for investment. The main rationale for such investments was attributed to Mauritius offering a 0% Capital Gains Tax rate and being a low tax jurisdiction. However, this study sheds new light on this reasoning and provides evidence that investment does not depend solely on Capital Gains Tax levy but also a host of other important factors.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".