A Critical Analysis of Fiscal Measures on Unhealthy Foods in Mauritius
Bibliographic record
Abstract
Abstract The level of obesity across the globe is on the rise and this is evidenced by the recent World Health Organisation’s ( WHO ) estimate of an increase in the worldwide prevalence of obesity which has nearly tripled between 1975 and 2018. Mauritius has a high rate of obesity which is evidenced by the Non-Communicable Diseases ( NCD ) report of 2016 indicating that around 54.2% of the participants are obese. One amongst the main causes of obesity is a high level of sugar consumption. In this regard, a number of policies are being undertaken by the Mauritius government one amongst which is the imposition of an excise duty on sugar. Furthermore, the study will also focus on consumption of some products that are considered to be unhealthy. For instance, while the WHO recommends a level of sodium chloride intake of less than 5 grams (1 tablespoon) per day to reduce blood pressure and cardio-vascular diseases, a study conducted by the Ministry of Health and Quality of Life ( MOHQL ) in 2012 revealed that the standardized mean salt intake was around 7.9 grams daily, which is far beyond the WHO ’s recommendations. The level of fat consumption is another factor that affects obesity. While the recommended level of fat is between 44 grams to 77 grams for an intake of 2,000 calories per day, a study conducted by Bundhun et al. in 2018 on the target population of 344 children showed that the majority of children consumes more than the recommended fat intake. In light of the above, the aim of this study is four-fold, mainly to: (a) analyse the effectiveness of the three cents of excise duty per gram of sugar on the Mauritius population consumption patterns; (b) assess the likely impact of a food tax in Mauritius imposed on fatty or salty foods that are considered unhealthy; (c) analyse the laws relating to unhealthy food taxes in Hungary; and (d) suggest possible recommendations which may be of use to stakeholders in Mauritius to combat obesity problem. The methodologies for the research are in essence comprised of the black letter approach which will analyse the legal provisions relating to the laws in Mauritius and Hungary. Books and reports will be also examined. A qualitative analysis is carried out in terms of questionnaire to find out the perceptions of the Mauritius population on food taxes. The paper aims at responding to the research objectives set out above. In particular, it is suggested that the rate of excise duty on sugar needs to be increased and that the Mauritius government should also consider the imposition of excise duty on the level of salt and fat contained in foods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".