The Effects of Non-Tariff Measures on Agricultural Trade Efficiency of South Africa Within the SADC
Bibliographic record
Abstract
While tariff liberalization under regional trade agreements has progressed, non-tariff measures (NTMs) have emerged as a significant impediment to the realization of full trade potential, particularly in the agriculture sector where NTMs are especially prevalent and in the Southern African Development Community (SADC) where intra-regional trade is low. Despite the extensive available literature on this subject, the impact of NTMs on trade efficiency in the SADC has hardly been explored. Against this background, this study estimates the impact of NTMs on the efficiency of South Africa’s bilateral agricultural trade with 11 SADC member states using data from 2011 to 2022 and a stochastic frontier gravity model. The average efficiency is found to be 45.6 percent, implying that more than half of South Africa’s agricultural trade potential remains unrealized in the region due to inefficiencies. NTMs are found to be a source of inefficiency, the effect of which is larger than that of tariffs by a factor of 6. This result emphasizes an urgent need for harmonizing NTMs across SADC member states to reduce compliance costs which are associated with trade inefficiency. The study contributes to the literature by treating NTMs as man-made trade resistances that affect trade efficiency rather than trade volumes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".