Non-Tariff Measures in the Food Processing Sector in Malaysia: An Assessment of Welfare Impacts
Bibliographic record
Abstract
Given the ambiguity of the overall welfare effects of non-tariff measures (NTMs), this paper furthers our understanding of the well-being of consumers by focusing on a single sector analysis. It examines the welfare effects associated with the highly regulated food processing sector in Malaysia. A comparative static computable general equilibrium model is employed to quantify the welfare impacts of a partial removal of NTMs, or a reduction in trade restrictiveness of NTMs. The simulation results indicate welfare gains, albeit minimal (not more than 2%), from a partial reduction in NTMs, both in the short run and long run. A plausible reason for the somewhat small gains in welfare in the food sector is the dominance of standard-like measures relative to price or quantity-based regulations. The positive and small welfare effects from a partial removal of NTMs suggest that some regulations in the food processing sector may be pervasive in that they may embed some protectionist elements and/or they do not address genuine market failures. Therefore, this paper concludes that there is still scope for regulatory reform in the food processing sector in Malaysia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".